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Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#RGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that we include Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetszZrZjZ5ZjZ5Zzrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? 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Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#pGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research may include Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5,W"  6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? 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Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1&61& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . 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Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(\Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Scrubbing, timeliness, retrospective permission, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $$ 0  $(  r  S  ~d `}  d r  S ~dV `0 d H  0޽h ? 3380___PPT10.zP`G^rhΕ&1h:( `/ 0DArialBlack0LLJԖׯ0ԖDArial Black0LLJԖׯ0Ԗ"@ .  @n ՜.+,D՜.+,    <On-screen ShowTelcordia Technologies' Arial Arial BlackDefault Design+GENI and the Challenges of Network Science3GENI and Network Science Propositions of This Talk&GENI and Network Science Talk Outline-“Network Science” All Kinds of Networks '“Computer-Related Networks” (CRNs)7A Broader Research Opportunity: Network Science & GENI Challenges For Network Science :GENI Can Be a Unique Facility NetSci ↔ Computer Science6Opportunities For GENI Offered by Network Science (1)6Opportunities For GENI Offered by Network Science (2)Exploiting the Opportunities GNetwork Science Studies on GENI Spanning Multiple GENI CRN Experiments>Implications for GENI Many Issues to Address (Affect All WGs)*The Grand Challenge The Grand Opportunity Slide 15  Fonts UsedDesign Template Slide TitlesD 4<Version%_ Will E LelandWill E Lelande Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1&61& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(\Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Scrubbing, timeliness, retrospective permission, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $$ 0  $(  r  S  ~d `}  d r  S ~dV `0 d H  0޽h ? 3380___PPT10.zP`G^r@ l&1V:( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =D2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1&61& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|~ Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(\Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Scrubbing, timeliness, retrospective permission, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $rJ&1:( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(\Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Scrubbing, timeliness, retrospective permission, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $0 0 p0(  x  c $d `}  d x  c $d ` d H  0޽h ? 3380___PPT10.}@r:J"JH&1:( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(|Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Scrubbing, timeliness, data ownership, retrospective permission, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $N `}   x  c $L `  H  0޽h ? 33___PPT10i.} /+D=' H= @B +r%z` &1;( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, scrubbing, timeliness, data ownership, retrospective permission, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $N `}   x  c $L `  H  0޽h ? 33___PPT10i.} /+D=' H= @B +rG%Q#&1:( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, scrubbing, timeliness, data ownership, retrospective studies, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $N `}   x  c $L `  H  0޽h ? 33___PPT10i.} /+D=' H= @B + 0 )!p(  pr p S (^ `}   r p S _r   p 0t` N ^  RApproaches: Theory Modeling and simulation Experimentation Real-world application H PFPP F(I p 0h #  mResults: Prediction Design & synthesis Control Directed evolution of all the networks that matter to society ` P9P+PP 9+( p  bWd?f P 6New InsightsImpact" 9 ! H p 0޽h ? 33___PPT10i.j;+D=' H= @B +r@%">F&1:( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, scrubbing, timeliness, data ownership, retrospective studies, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $rGF&1:( `/ 0DArialBlack0LLJԖׯ0Ԗ"DArial Black0LLJԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ododLkׯ0ppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci extensions in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, scrubbing, timeliness, data ownership, retrospective studies, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $rI%M&1;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndjׯ0jppp@  <4ddddT0Lg4VdVdhlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  = 3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9) %'Implications for GENI Issues to Address(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, scrubbing, timeliness, data ownership, retrospective studies, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $  0    (  r  S X `}  X r  S X  `G  X 2  <0XV+ 5CRN dB  <DjJx* x  <lX[ 4time RB  s *DjJ RB  s *DjJ RB  s *DjJ\ RB @ s *DjJ &RB  s *DjJ RB @ s *DjJ8 4nWRB @ s *DjJ 4nRB  s *DjJ Fh 2  <$nz 5CRN 2  <nR 5CRN 2  < n& yC 5CRN 2  6l nZ `< 4 :Ensemble   2  6tnv uP  :Ensemble   2  6 n 8  :Ensemble   2  < n  5CRN 2  <n  < * 5CRN 2  < n(  5CRN H  0޽h ? 33___PPT10i.} /+D=' L= @B +r$]&1;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndjׯ0jppp@  <4ddddT0Lg4VdVdhlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =2*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%'Implications for GENI Issues to Address(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $2n `}  n x  c $3n ` n H  0޽h ? 33___PPT10i.} /+D=' n= @B +r%@C&1;(       !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{}~`/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndjׯ0jppp@  <4ddddT0Lg4VdVdhlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%+Implications for GENI Issues Affect All WGs,Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $2n `}  n x  c $3n ` n H  0޽h ? 33___PPT10i.} /+D=' n= @B +rC%~Co&1q;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndjׯ0jppp@  <4ddddT0Lg4VdVdhlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =_3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%8Implications for GENI Issues to Address (Affect All WGs)9#,(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $2nh `8  n x  c $3n% ` n H  0޽h ? 33___PPT10i.} /+D=' = @B +r%$&1=;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndjׯ0jppp@  <4ddddT0Lg4VdVdhlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =+3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%8Implications for GENI Issues to Address (Affect All WGs)9#Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $rͿ6&1B;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndjׯ0jppp@  <4ddddT0Lg4VdVdhlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =03*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)>#Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, & How do experiments opt into or out of NetSci studies? What is the default? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $2nh `8  n x  c $3n% ` n H  0޽h ? 33___PPT10i.} /+D=' = @B +rj%6FE9&1f;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndkׯ0jppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =T3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)>#Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $Mh `8  M x  c $ĦM% ` M H  0޽h ? 33___PPT10i.} /+D=' = @B +r9%t]9w&1f;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndkׯ0jppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =T3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)>#Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci studies? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $rww*&1j;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndTkׯ0jppp@  <4ddddT0Lg4VdVdlׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =X3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)>#Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci ensembles? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $Nh `8  N x  c $̦N% ` N H  0޽h ? 33___PPT10i.} /+D=' N= @B +r^%:a&1;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndkׯ0jppp@  <4ddddT0Lg4VdVd,mׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)># Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, specs & ontologies, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci ensembles? How do end-users opt in and out? Do they?Z, )The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $Nh `8  N x  c $ԦN% ` N H  0޽h ? 33___PPT10i.} /+D=' = @B +r%k-y/&1;( `/ 0DArialBlack0LLKԖׯ0Ԗ"DArial Black0LLKԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTU      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndkׯ0jppp@  <4ddddT0Lg4VdVd,mׯ0`p@ pp<4BdBdvS0LJ<4!d!dvS0LJ80___PPT10 ``?(-2007-12-11O  =3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)># Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, specs & ontologies, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci ensembles? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $r800k&1;( `/ 0DArialBlack0LLHԖׯ0ԖDArial Black0LLHԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndiׯ0jppp@  <4ddddT0Lg4VdVdkׯ0`p@ pp<4BdBdvS0LG<4!d!dvS0LG80___PPT10 ``?(-2007-12-11O  =3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)># Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability, specs & ontologies, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci ensembles? How do end-users opt in and out? Do they?Z)The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $T0 0T(  r  S dO> O r  S  OP 0 O x  s <Ared Logo#" `ST  C ,AGENI_logo1L  C $A  nsf4c( H  0޽h ? 3380___PPT10.7;0rlk&1;( `/ 0DArialBlack0LLHԖׯ0Ԗ"DArial Black0LLHԖׯ0Ԗ"@ .  @n?" dd@  @@`` 1      4 !"#& '()*,b$qz@}Sc$$b$XL( /\B0@Icb$7W~?SRR$fNx9=8;O&(b${,IstME7%1G 1'b$TG*z/Xb$Z4+MMUE0][ 0AA ff@8*v ʚ;w8ʚ;g4ndndiׯ0jppp@  <4ddddT0Lg4VdVd8kׯ0`p@ pp<4BdBdvS0LG<4!d!dvS0LG80___PPT10 ``?(-2007-12-11O  =3*GENI and the Challenges of Network Science:Dr. Will E. Leland wel@research.telcordia.com 4 March 200802GENI and Network Science Propositions of This Talk3Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI Science and society can benefit from both(x%GENI and Network Science Talk Outline&  Network Science and  Computer-Related Networks Challenges for Network Science Opportunities for GENI Implications for GENI P Network Science All Kinds of Networks ()BThe study of common properties and mechanisms that hold over all  networks There are robust empirical laws for networks Heavy-tailed distributions, long-range dependence Self-similarity Small-world phenomena Pareto distribution of applications, communities Disparate kinds of networks have deep relations Biological, physical, social, engineered Layers of abstraction & dependency Emerging discipline: not proven outLZ-Z0ZLZ$ZL- !L$D Computer-Related Networks (CRNs)#jGENI is a testbed for more than  computer networks I use  computer-related networks (CRNs) to emphasize that GENI research includes Communicating devices (varying intelligence) Communicating applications and services Communicating infrastructure Naming, routing, discovery, authentication, security, robustness, maintenance, reconfiguration, policy, & Communicating humans using these device and services Researchers [distinguished class of GENI users] End users Service & data creators and suppliers Operators Not assuming existing IP technology, WWW, or packetsZrZjZ5ZjZ5Zrj j5W 6A Broader Research Opportunity: Network Science & GENI$What is a network? The 3  CEL attributes: Connectivity: Nodes with finite links between them Exchange: Exchanging resources takes time Locality: Nodes only interact through links Common research themes: Dynamics, resilience, evolution, analysis, synthesis, visualization Network interactions, layers, abstraction, representation Enrich CRN research with NetSci perspective Broad insights Challenging hypotheses & examples Enrich network science with CRN perspective Varied, well-instrumented, well-studied, multi-layer networks+,1,>+ '"$,1,  >Challenges For Network Science NetSci hypotheses claim universality: must be tested across ensembles of different networks Topological properties (small-world, heavy-tails,& ) Evolutionary trajectories (emergent phenomena, & ) Dynamic behavior (phase transitions, & ) NetSci hypotheses propose abstract mechanisms: must be mapped to real networks Do these mechanisms really describe what happens in well-instrumented networks? Do they have the effects predicted (transient, steady-state)? Do we learn something new? \O 8# "&nGENI Can Be a Unique Facility NetSci ! Computer Science8Network Science needs a facility for research on Ensembles of highly varied, well-instrumented, multi-scale, multi-layer networks With known, controllable cross-network interactions Including both emergent and engineered networks Such facilities do not now exist GENI is uniquely situated for Testing the hypotheses of Network Science Bringing Network Science to the benefit of research on computer-related networks Bringing CRN research to the benefit of Network Science j1ZZ?ZZZ1?"5Opportunities For GENI Offered by Network Science (1)A source of strong scientific hypotheses to test Proposed network evolution mechanisms Drivers of evolution: percolation, attachment preferences, highly optimized tolerance, & Barriers to evolution: centrality, Nash equilibria, & Evolutionary consequences for structure and behavior Scaling laws: Locality of interaction, small-world networks, self-similarity for size distributions (node degrees, communities, frequency of use, & ) Behavioral phenomena Phase transitions, emergent behavior, resilience, & 1Z&ZZZZZ6Z1& 6 #5Opportunities For GENI Offered by Network Science (2)\A source of ideas Unfamiliar network structures and mechanisms Economic networks based on potlatch, ecological networks driven by mimetic phenomena (warning, crypsis, Mullerian & Batesian mimicry), kinship networks based on fraternal polyandry, & . Cross-network mechanisms Resilience by social network instead of computer net? Efficient support for economic or social networks? Damping epidemics by improving remote interactions? Insight into CRN structures and mechanisms What would we change to speed or slow evolution? Mutual evolution of overlay networksZ-ZZZZ+ZVZ-+ V> t!Exploiting the Opportunities Add this context to our research agenda Our research community, NetSEC, other NetSci communities Develop requirements that support NetSci studies on ensembles of CRN experiments Design, prototype, experiment Mechanisms, impacts, operations, measurements, legal, & Inform and evolve the research agenda New possibilities Necessary trade-offs to be resolved in supporting the agenda(9p8&O(9p8&O  @8$FNetwork Science Studies on GENI Spanning Multiple GENI CRN ExperimentsG 'Possible NetSci studies: relation to experiments studied Pure observation of CRN experiments Mutual influence over time between study and CRN experiments Embedded NetSci plug-ins in CRN experiments (Partial) control of CRN experiment parameters or environment Ensembles: multi-experiment collections Active and default opt-in of CRN experiments Completed and current CRN experiments Used in CRN and NetSci multi-experiment studiesT9)9)%=Implications for GENI Many Issues to Address (Affect All WGs)>#(Policy, technical, operational, legal, & What NetSci studies should GENI address? What measurements should GENI experiments take so (other) researchers could study NetSci issues? Defaults, plug-ins, scrubbing, timeliness, data ownership, retrospective studies, repeatability/replayability, specs & ontologies, & How do experiments opt into or out of NetSci studies? What is the default? Experiment specs? How do NetSci studies recruit specific GENI CRN experiments & other NetSci ensembles? How do end-users opt in and out? Do they?Z )The Grand Challenge The Grand OpportunityUnderstanding Complex Networks  Z ,  Thank you!" $ 0 0(  x  c $h `8   x  c $ % `  H  0޽h ? 33___PPT10i.} /+D=' = @B +r8%&1