Changes between Version 31 and Version 32 of GENIBibliography


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Timestamp:
09/15/15 14:52:40 (4 years ago)
Author:
Mark Berman
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  • GENIBibliography

    v31 v32  
    10161016
    10171017<li>
     1018<b>Fund, Fraida and Dong, Chen and Korakis, Thanasis and Panwar, Shivendra</b>
     1019, &quot;A Framework for Multidimensional Measurements on an Experimental WiMAX Testbed.&quot;
     1020Testbeds and Research Infrastructure. Development of Networks and Communities, Springer Berlin Heidelberg,
     10212012.
     1022doi:10.1007/978-3-642-35576-9&#x005F;32.
     1023<a href="http://dx.doi.org/10.1007/978-3-642-35576-9&#x005F;32">http://dx.doi.org/10.1007/978-3-642-35576-9&#x005F;32</a>
     1024<br><br><b>Abstract: </b>A major difficulty in the design, study, and implementation of wireless protocols and applications is the multitude of nondeterministic factors (e.g. interference, weather conditions, competing traffic) that can affect their performance. For this reason, testbeds that enable researchers to quantify these influences have become increasingly essential in the wireless research community. The growing sophistication of wireless testbeds and the wide array of services they can provide to researchers have advanced the field tremendously. Toward this end, we present an early implementation of an instrumentation and measurement framework that we have deployed on an open-access 802.16e wireless research testbed at the Polytechnic Institute of NYU. We have created a set of tools to allow experimenters to routinely collect measurements of environmental conditions during experiment runtime. These tools integrate high volumes of multidimensional measurement data from a diverse array of sources, including measurements from software defined radio peripherals, sensors, and network device drivers. With this, we aim to give researchers the ability to conduct rigorous and repeatable over-the-air experiments. We also foresee potential applications for this framework beyond its use in experiments, such as in long-term testbed monitoring.
     1025</li>
     1026<br>
     1027
     1028
     1029
     1030<li>
    10181031<b>Gangam, Sriharsha and Blanton, Ethan and Fahmy, Sonia</b>
    10191032, &quot;Exercises for Graduate Students using GENI.&quot;
     
    11341147<li>
    11351148<b>Krishnappa, Dilip K. and Lyons, Eric and Irwin, David and Zink, Michael</b>
     1149, &quot;Performance of GENI Cloud Testbeds for Real Time Scientific Application.&quot;
     1150First GENI Research and Educational Experiment Workshop (GREE 2012), Los Angeles,
     11512012.
     1152
     1153
     1154<br><br><b>Abstract: </b>Dedicating high end servers for short-term execution of scientific applications such as weather forecasting wastes resources. Cloud platforms IaaS model seems well suited for applications which are executed on an irregular basis and for short duration. In this paper, we evaluate the performance of research testbed cloud platforms such as GENICloud and ORCA cloud clusters for our real-time scientific application of short-term weather forecasting called Nowcasting. In this paper, we evaluate the network capabilities of these research cloud testbeds for our real-time application of weather forecasting. In addition, we evaluate the computation time of executing Nowcasting on each cloud platform for weather data collected from real weather events. We also evaluate the total time taken to generate and transmit short-term forecast images to end users with live data from our own radar on campus. We also compare the performance of each of these clusters for Nowcasting with commercial cloud services such as Amazon's EC2. The results obtained from our measurement show that cloud testbeds are suitable for real-time application experiments to be carried out on a cloud platform.
     1155</li>
     1156<br>
     1157
     1158<li>
     1159<b>Krishnappa, Dilip K. and Lyons, Eric and Irwin, David and Zink, Michael</b>
    11361160, &quot;Network capabilities of cloud services for a real time scientific application.&quot;
    1137116137th Annual IEEE Conference on Local Computer Networks, Clearwater Beach, FL, USA, IEEE,
     
    11431167<br>
    11441168
    1145 <li>
    1146 <b>Krishnappa, Dilip K. and Lyons, Eric and Irwin, David and Zink, Michael</b>
    1147 , &quot;Performance of GENI Cloud Testbeds for Real Time Scientific Application.&quot;
    1148 First GENI Research and Educational Experiment Workshop (GREE 2012), Los Angeles,
    1149 2012.
    1150 
    1151 
    1152 <br><br><b>Abstract: </b>Dedicating high end servers for short-term execution of scientific applications such as weather forecasting wastes resources. Cloud platforms IaaS model seems well suited for applications which are executed on an irregular basis and for short duration. In this paper, we evaluate the performance of research testbed cloud platforms such as GENICloud and ORCA cloud clusters for our real-time scientific application of short-term weather forecasting called Nowcasting. In this paper, we evaluate the network capabilities of these research cloud testbeds for our real-time application of weather forecasting. In addition, we evaluate the computation time of executing Nowcasting on each cloud platform for weather data collected from real weather events. We also evaluate the total time taken to generate and transmit short-term forecast images to end users with live data from our own radar on campus. We also compare the performance of each of these clusters for Nowcasting with commercial cloud services such as Amazon's EC2. The results obtained from our measurement show that cloud testbeds are suitable for real-time application experiments to be carried out on a cloud platform.
    1153 </li>
    1154 <br>
    1155 
    11561169
    11571170
     
    14061419<b>Thomas, Charles and Sommers, Joel and Barford, Paul and Kim, Dongchan and Das, Ananya and Segebre, Roberto and Crovella, Mark</b>
    14071420, &quot;A Passive Measurement System for Network Testbeds.&quot;
    1408 8th International ICST Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TRIDENTCOM 2012),
    1409 2012.
    1410 
    1411 
    1412 <br><br><b>Abstract: </b>The ability to capture and process packet-level data is of intrinsic importance in network testbeds that offer broad experimental capabilities to researchers. In this paper we describe the design and implementation of a passive measurement system for network testbeds called GIMS. The system enables users to specify and centrally manage packet capture on a set of dedicated measurement nodes deployed on links in a distributed testbed. The first component of GIMS is a scalable experiment management system that coordinates multi-tenant access to measurement nodes through a web-based user interface. The second component of GIMS is a node management system that enables \\\\em (i) local processing on packets (\\\\em e.g., flow aggregation and sampling), \\\\em (ii) meta-data to be added to captured packets (\\\\em e.g., timestamps), \\\\em (iii) packet anonymization per local security policy, and \\\\em (iv) flexible data storage including transfer to remote archives. We demonstrate the capabilities of GIMS through a set of micro-benchmarks that specifically highlight the performance of the node management system deployed on a commodity workstation. Our implementations are openly available to the community and our development efforts are on-going.
     1421Testbeds and Research Infrastructure. Development of Networks and Communities, Springer Berlin Heidelberg,
     14222012.
     1423doi:10.1007/978-3-642-35576-9&#x005F;14.
     1424<a href="http://dx.doi.org/10.1007/978-3-642-35576-9&#x005F;14">http://dx.doi.org/10.1007/978-3-642-35576-9&#x005F;14</a>
     1425<br><br><b>Abstract: </b>The ability to capture and process packet-level data is of intrinsic importance in network testbeds that offer broad experimental capabilities to researchers. In this paper we describe the design and implementation of a passive measurement system for network testbeds called GIMS. The system enables users to specify and centrally manage packet capture on a set of dedicated measurement nodes deployed on links in a distributed testbed. The first component of GIMS is a scalable experiment management system that coordinates multi-tenant access to measurement nodes through a web-based user interface. The second component of GIMS is a node management system that enables (i) local processing on packets (e.g., flow aggregation and sampling), (ii) meta-data to be added to captured packets (e.g., timestamps), (iii) packet anonymization per local security policy, and (iv) flexible data storage including transfer to remote archives. We demonstrate the capabilities of GIMS through a set of micro-benchmarks that specifically highlight the performance of the node management system deployed on a commodity workstation. Our implementations are openly available to the community and our development efforts are on-going.
    14131426</li>
    14141427<br>
     
    18891902
    18901903<li>
     1904<b>Tsai, Pang-Wei and wen Cheng, Pei and Yang, Chu-Sing and Luo, Mon-Yen</b>
     1905, &quot;Supporting Extensions of VLAN-tagged traffic across OpenFlow Networks.&quot;
     19062013 Proceedings Second GENI Research and Educational Experiment Workshop, Salt Lake City, UT, IEEE,
     19072013.
     1908doi:10.1109/GREE.2013.20.
     1909<a href="http://dx.doi.org/10.1109/GREE.2013.20">http://dx.doi.org/10.1109/GREE.2013.20</a>
     1910
     1911</li>
     1912<br>
     1913
     1914
     1915
     1916<li>
    18911917<b>Valancius, Vytautas and Ravi, Bharath and Feamster, Nick and Snoeren, Alex C.</b>
    18921918, &quot;Quantifying the benefits of joint content and network routing.&quot;
     
    20362062
    20372063<li>
    2038 <b>Aug&#x65;&#x0301;, Jordan and Parmentelat, Thierry and Turro, Nicolas and Avakian, Sandrine and Baron, Lo\\ic and Larabi, Mohamed A. and Rahman, Mohammed Y. and Friedman, Timur and Fdida, Serge</b>
     2064<b>Aug&#x65;&#x0301;, Jordan and Parmentelat, Thierry and Turro, Nicolas and Avakian, Sandrine and Baron, Lo&#x69;&#x0308;c and Larabi, Mohamed A. and Rahman, Mohammed Y. and Friedman, Timur and Fdida, Serge</b>
    20392065, &quot;Tools to foster a global federation of testbeds.&quot;
    20402066Computer Networks,
     
    26972723
    26982724<li>
    2699 <b>Schwerdel, Dennis and Reuther, Bernd and Zinner, Thomas and M\\uller, Paul and Tran-Gia, Phouc</b>
     2725<b>Schwerdel, Dennis and Reuther, Bernd and Zinner, Thomas and M&#x75;&#x0308;ller, Paul and Tran-Gia, Phouc</b>
    27002726, &quot;Future Internet research and experimentation: The G-Lab approach.&quot;
    27012727Computer Networks,
     
    27492775
    27502776<li>
    2751 <b>Su&#x6e;&#x0303;&#x65;&#x0301;, M. and Bergesio, L. and Woesner, H. and Rothe, T. and K\\opsel, A. and Colle, D. and Puype, B. and Simeonidou, D. and Nejabati, R. and Channegowda, M. and Kind, M. and Dietz, T. and Autenrieth, A. and Kotronis, V. and Salvadori, E. and Salsano, S. and K\\orner, M. and Sharma, S.</b>
     2777<b>Su&#x6e;&#x0303;&#x65;&#x0301;, M. and Bergesio, L. and Woesner, H. and Rothe, T. and K&#x6f;&#x0308;psel, A. and Colle, D. and Puype, B. and Simeonidou, D. and Nejabati, R. and Channegowda, M. and Kind, M. and Dietz, T. and Autenrieth, A. and Kotronis, V. and Salvadori, E. and Salsano, S. and K&#x6f;&#x0308;rner, M. and Sharma, S.</b>
    27522778, &quot;Design and implementation of the OFELIA FP7 facility: The European OpenFlow testbed.&quot;
    27532779Computer Networks,
     
    28932919<li>
    28942920<b>Xin, Yufeng and Baldin, Ilya and Heermann, Chris and Mandal, Anirban and Ruth, Paul</b>
     2921, &quot;Scaling up applications over distributed clouds with dynamic layer-2 exchange and broadcast service.&quot;
     2922Teletraffic Congress (ITC), 2014 26th International, IEEE,
     29232014.
     2924doi:10.1109/itc.2014.6932973.
     2925<a href="http://dx.doi.org/10.1109/itc.2014.6932973">http://dx.doi.org/10.1109/itc.2014.6932973</a>
     2926<br><br><b>Abstract: </b>In this paper, we study the problem of provisioning large-scale virtual clusters over federated clouds connected by multi-domain, layer-2 wide area networks. We first present the virtual cluster request abstraction and the abstraction models for substrate resource pools. Based on these two abstraction models, we developed a novel layer-2 exchange mechanism and an implementation of it in a multi-domain networked cloud environment. The design of the mechanism takes into consideration the realistic constraints in current network and cloud systems. We show that efficient cluster splitting, cloud data center selection and resource allocation algorithms can be developed to provision large-scale virtual clusters across cloud sites. A prototype system has been deployed and integrated into the ExoGENI testbed for about a year, and is being heavily used by scientific and data analytic applications.
     2927</li>
     2928<br>
     2929
     2930<li>
     2931<b>Xin, Yufeng and Baldin, Ilya and Heermann, Chris and Mandal, Anirban and Ruth, Paul</b>
    28952932, &quot;Capacity of Inter-cloud Layer-2 Virtual Networking.&quot;
    28962933Proceedings of the 2014 ACM SIGCOMM Workshop on Distributed Cloud Computing, Chicago, Illinois, USA, ACM, New York, NY, USA,
     
    29022939<br>
    29032940
    2904 <li>
    2905 <b>Xin, Yufeng and Baldin, Ilya and Heermann, Chris and Mandal, Anirban and Ruth, Paul</b>
    2906 , &quot;Scaling up applications over distributed clouds with dynamic layer-2 exchange and broadcast service.&quot;
    2907 Teletraffic Congress (ITC), 2014 26th International, IEEE,
    2908 2014.
    2909 doi:10.1109/itc.2014.6932973.
    2910 <a href="http://dx.doi.org/10.1109/itc.2014.6932973">http://dx.doi.org/10.1109/itc.2014.6932973</a>
    2911 <br><br><b>Abstract: </b>In this paper, we study the problem of provisioning large-scale virtual clusters over federated clouds connected by multi-domain, layer-2 wide area networks. We first present the virtual cluster request abstraction and the abstraction models for substrate resource pools. Based on these two abstraction models, we developed a novel layer-2 exchange mechanism and an implementation of it in a multi-domain networked cloud environment. The design of the mechanism takes into consideration the realistic constraints in current network and cloud systems. We show that efficient cluster splitting, cloud data center selection and resource allocation algorithms can be developed to provision large-scale virtual clusters across cloud sites. A prototype system has been deployed and integrated into the ExoGENI testbed for about a year, and is being heavily used by scientific and data analytic applications.
    2912 </li>
    2913 <br>
    2914 
    29152941
    29162942
     
    29823008<br>
    29833009<a id="full-2015"><H2>GENI Publications for 2015</H2></a>
     3010
     3011
     3012<li>
     3013<b>&#x4f;&#x0308;z&#x63;&#x0327;elik, &#x49;&#x0307;lker and Brooks, Richard R.</b>
     3014, &quot;Deceiving entropy based DoS detection.&quot;
     3015Computers &#x0026; Security,
     30162015.
     3017doi:10.1016/j.cose.2014.10.013.
     3018<a href="http://dx.doi.org/10.1016/j.cose.2014.10.013">http://dx.doi.org/10.1016/j.cose.2014.10.013</a>
     3019<br><br><b>Abstract: </b>Denial of Service (DoS) attacks disable network services for legitimate users. As a result of growing dependence on the Internet by both the general public and service providers, the availability of Internet services has become a concern. While DoS attacks cause inconvenience for users, and revenue loss for service providers; their effects on critical infrastructures like the smart grid and public utilities could be catastrophic. For example, an attack on a smart grid system can cause cascaded power failures and lead to a major blackout. Researchers have proposed approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. The detector uses network traffic statistics; such as the entropy of incoming packet header fields (e.g. source IP addresses or protocol type). It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. Entropy features are common in recent DDoS detection publications. They are also one of the most effective features for detecting these attacks. However, intrusion detection systems (IDS) using entropy based detection approaches can be a victim of spoofing attacks. An attacker can sniff the network and calculate background traffic entropy before a (D)DoS attack starts. They can then spoof attack packets to keep the entropy value in the expected range during the attack. This paper explains the vulnerability of entropy based network monitoring systems. We present a proof of concept entropy spoofing attack and show that by exploiting this vulnerability, the attacker can avoid detection or degrade detection performance to an unacceptable level.
     3020</li>
     3021<br>
     3022
    29843023
    29853024
     
    30113050
    30123051<li>
     3052<b>Chen, Xinming and Wolf, Tilman and Griffioen, Jim and Ascigil, Onur and Dutta, Rudra and Rouskas, George and Bhat, Shireesh and Baldin, Ilya and Calvert, Ken</b>
     3053, &quot;Design of a protocol to enable economic transactions for network services.&quot;
     3054Communications (ICC), 2015 IEEE International Conference on, IEEE,
     30552015.
     3056doi:10.1109/icc.2015.7249175.
     3057<a href="http://dx.doi.org/10.1109/icc.2015.7249175">http://dx.doi.org/10.1109/icc.2015.7249175</a>
     3058<br><br><b>Abstract: </b>Deployment of innovative new networking services requires support by network providers. Since economic motivation plays an important role for network providers, it is critical that a network architecture intrinsically considers economic relationships. We present the design of a protocol that associates access to network services with economic contracts. We show how this protocol can be realized in fundamentally different ways, using out-of-band signaling and in-band signaling, based on two different prototype implementations. We present results that show the effectiveness of the proposed protocol and thus demonstrate a first step toward realizing an economy plane for the Internet.
     3059</li>
     3060<br>
     3061
     3062
     3063
     3064<li>
    30133065<b>Chin, Tommy and Mountrouidou, Xenia and Li, Xiangyang and Xiong, Kaiqi</b>
    30143066, &quot;Selective Packet Inspection to Detect DoS Flooding Using Software Defined Networking (SDN).&quot;
     
    30633115
    30643116<li>
     3117<b>Juluri, Parikshit</b>
     3118, &quot;Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services.&quot;
     3119
     31202015.
     3121
     3122<a href="https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/46696/JuluriMeaImpQua.pdf?sequence=1&#x0026;&#x0023;38;isAllowed=y">https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/46696/JuluriMeaImpQua.pdf?sequence=1&#x0026;&#x0023;38;isAllowed=y</a>
     3123<br><br><b>Abstract: </b>HTTP based online video streaming services have been consistently dominating the online traffic for the past few years. Measuring and improving the performance of these services is an important challenge. Traditional Quality-of-Service (QoS) metrics such as packet loss, jitter and delay which were used for networked services are not easily understood by the users. Instead, Quality-of-Experience (QoE) metrics which capture the overall satisfaction are more suitable for measuring the quality as perceived by the users. However, these QoE metrics have not yet been standardized and their measurement and improvement poses unique challenges. In this work we first present a comprehensive survey of the different set of QoE metrics and the measurement methodologies suitable for HTTP based online video streaming services. We then present our active QoE measurement tool Pytomo that measures the QoE of YouTube videos. A case study on the measurement of QoE of YouTube videos when accessed by residential users from three different Internet Service Providers (ISP) in a metropolitan area is discussed. This is the first work that has collected QoE data from actual residential users using active measurements for YouTube videos. Based on these measurements we were able to study and compare the QoE of YouTube videos across multiple ISPs. We also were able to correlate the QoE observed with the server clusters used for the different users. Based on this correlation we were able to identify the server clusters that were experiencing diminished QoE. DynamicAdaptive Streaming overHTTP (DASH) is an HTTP based video streaming that enables the video players to adapt the video quality based on the network conditions. We next present a rate adaptation algorithm that improves the QoE of DASH video streaming services that selects the most optimum video quality. With DASH the video server hosts multiple representation of the same video and each representation is divided into small segments of constant playback duration. The DASH player downloads the appropriate representation based on the network conditions, thus, adapting the video quality to match the conditions. Currently deployed Adaptive Bitrate (ABR) algorithms use throughput and buffer occupancy to predict segment fetch times. These algorithms assume that the segments are of equal size. However, due to the encoding schemes employed this assumption does not hold. In order to overcome these limitations, we propose a novel Segment Aware Rate Adaptation algorithm (SARA) that leverages the knowledge of the segment size variations to improve the prediction of segment fetch times. Using an emulated player in a geographically distributed virtual network setup, we compare the performance of SARA with existing ABR algorithms. We demonstrate that SARA helps to improve the QoE of the DASH video streaming with improved convergence time, better bitrate switching performance and better video quality. We also show that unlike the existing adaptation schemes, SARA provides a consistent QoE irrespective of the segment size distributions.
     3124</li>
     3125<br>
     3126
     3127
     3128
     3129<li>
     3130<b>Liu, Xuan</b>
     3131, &quot;Dynamic Virtual Network Restoration with Optimal Standby Virtual Router Selection.&quot;
     3132
     31332015.
     3134
     3135<a href="https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/46697/LiuDynVirNet.pdf?sequence=1&#x0026;&#x0023;38;isAllowed=y">https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/46697/LiuDynVirNet.pdf?sequence=1&#x0026;&#x0023;38;isAllowed=y</a>
     3136<br><br><b>Abstract: </b>Network virtualization technologies allow service providers to request partitioned, QoS guaranteed and fault-tolerant virtual networks provisioned by the substrate network provider (i.e., physical infrastructure provider). A virtualized networking environment (VNE) has common features such as partition, flexibility, etc., but fault-tolerance requires additional efforts to provide survivability against failures on either virtual networks or the substrate network. Two common survivability paradigms are protection (proactive) and restoration (reactive). In the protection scheme, the substrate network provider (SNP) allocates redundant resources (e.g., nodes, paths, bandwidths, etc) to protect against potential failures in the VNE. In the restoration scheme, the SNP dynamically allocates resources to restore the networks, and it usually occurs after the failure is detected. In this dissertation, we design a restoration scheme that can be dynamically implemented in a centralized manner by an SNP to achieve survivability against node failures in the VNE. The proposed restoration scheme is designed to be integrated with a protection scheme, where the SNP allocates spare virtual routers (VRs) as standbys for the virtual networks (VN) and they are ready to serve in the restoration scheme after a node failure has been identified. These standby virtual routers (S-VR) are reserved as a sharedbackup for any single node failure, and during the restoration procedure, one of the S-VR will be selected to replace the failed VR. In this work, we present an optimal S-VR selection approach to simultaneously restore multiple VNs affected by failed VRs, where these VRs may be affected by failures within themselves or at their substrate host (i.e., power outage, hardware failures, maintenance, etc.). Furthermore, the restoration scheme is embedded into a dynamic reconfiguration scheme (DRS), so that the affected VNs can be dynamically restored by a centralized virtual network manager (VNM). We first introduce a dynamic reconfiguration scheme (DRS) against node failures in a VNE, and then present an experimental study by implementing this DRS over a realistic VNE using GpENI testbed. For this experimental study, we ran the DRS to restore one VN with a single-VR failure, and the results showed that with a proper S-VR selection, the performance of the affected VN could be well restored. Next, we proposed an Mixed-Integer Linear Programming (MILP) model with dual–goals to optimally select S-VRs to restore all VNs affected by VR failures while load balancing. We also present a heuristic algorithm based on the model. By considering a number of factors, we present numerical studies to show how the optimal selection is affected. The results show that the proposed heuristic's performance is close to the optimization model when there were sufficient standby virtual routers for each virtual network and the substrate nodes have the capability to support multiple standby virtual routers to be in service simultaneously. Finally, we present the design of a software-defined resilient VNE with the optimal S-VR selection model, and discuss a prototype implementation on the GENI testbed.
     3137</li>
     3138<br>
     3139
     3140
     3141
     3142<li>
    30653143<b>Liu, Xuan and Edwards, Sarah and Riga, Niky and Medhi, Deep</b>
    30663144, &quot;Design of a software-defined resilient virtualized networking environment.&quot;
     
    31673245
    31683246<li>
    3169 <b>\\Oz&#x63;&#x0327;elik, &#x49;&#x0307;lker and Brooks, Richard R.</b>
    3170 , &quot;Deceiving entropy based DoS detection.&quot;
    3171 Computers &#x0026; Security,
     3247<b>Zhang, Miao and Kissel, Ezra and Swany, Martin</b>
     3248, &quot;Using phoebus data transfer accelerator in cloud environments.&quot;
     3249Communications (ICC), 2015 IEEE International Conference on, IEEE,
    317232502015.
    3173 doi:10.1016/j.cose.2014.10.013.
    3174 <a href="http://dx.doi.org/10.1016/j.cose.2014.10.013">http://dx.doi.org/10.1016/j.cose.2014.10.013</a>
    3175 <br><br><b>Abstract: </b>Denial of Service (DoS) attacks disable network services for legitimate users. As a result of growing dependence on the Internet by both the general public and service providers, the availability of Internet services has become a concern. While DoS attacks cause inconvenience for users, and revenue loss for service providers; their effects on critical infrastructures like the smart grid and public utilities could be catastrophic. For example, an attack on a smart grid system can cause cascaded power failures and lead to a major blackout. Researchers have proposed approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. The detector uses network traffic statistics; such as the entropy of incoming packet header fields (e.g. source IP addresses or protocol type). It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. Entropy features are common in recent DDoS detection publications. They are also one of the most effective features for detecting these attacks. However, intrusion detection systems (IDS) using entropy based detection approaches can be a victim of spoofing attacks. An attacker can sniff the network and calculate background traffic entropy before a (D)DoS attack starts. They can then spoof attack packets to keep the entropy value in the expected range during the attack. This paper explains the vulnerability of entropy based network monitoring systems. We present a proof of concept entropy spoofing attack and show that by exploiting this vulnerability, the attacker can avoid detection or degrade detection performance to an unacceptable level.
     3251doi:10.1109/icc.2015.7248346.
     3252<a href="http://dx.doi.org/10.1109/icc.2015.7248346">http://dx.doi.org/10.1109/icc.2015.7248346</a>
     3253<br><br><b>Abstract: </b>The quality of data exchange in cloud computing applications relies on the connection performance between user clients and their cloud storage providers, and is often dependent on the wide area network (WAN) properties among data centers. For certain classes of applications, it can be crucial to provide an end-to-end solution that accelerates large data transfers and improves overall user experience. The development and deployment of WAN optimization technology has been investigated for improving application perfor- mance in heterogeneous, multi-domain environments. WAN opti- mization devices and services implement a number of approaches for performance improvement, and one key insight is that in contrast to traditional end-to-end TCP connections, middleboxes that segment and optimize transport-layer connections can im- prove the performance of wide area data transfers. In the context of dynamic cloud computing environments, there is an obvious target for implementations of WAN optimization as Network Function Virtualization (NFV), where the flexibility of virtualized cloud environments can be exploited. This paper describes recent developments and experimentation of our Phoebus WAN accelerator framework. We introduce a software suite that includes new Phoebus clients that operate with the Phoebus Gateway network. We test and discuss virtualizing Phoebus Gateways to provide acceleration services in cloud data transfers. Use cases and performance evaluations are conducted on FutureGrid and Internet2 testbeds, and we demonstrate the effectiveness of a virtualized Phoebus deployment.
    31763254</li>
    31773255<br>
     
    40194097
    40204098<li>
     4099<b>Fund, Fraida and Dong, Chen and Korakis, Thanasis and Panwar, Shivendra</b>
     4100, &quot;A Framework for Multidimensional Measurements on an Experimental WiMAX Testbed.&quot
     4101Testbeds and Research Infrastructure. Development of Networks and Communities, Springer Berlin Heidelberg,
     41022012.
     4103doi:10.1007/978-3-642-35576-9&#x005F;32.
     4104</li>
     4105<br>
     4106
     4107
     4108
     4109<li>
    40214110<b>Gangam, Sriharsha and Blanton, Ethan and Fahmy, Sonia</b>
    40224111, &quot;Exercises for Graduate Students using GENI.&quot
     
    41194208<li>
    41204209<b>Krishnappa, Dilip K. and Lyons, Eric and Irwin, David and Zink, Michael</b>
     4210, &quot;Performance of GENI Cloud Testbeds for Real Time Scientific Application.&quot
     4211First GENI Research and Educational Experiment Workshop (GREE 2012), Los Angeles,
     42122012.
     4213
     4214</li>
     4215<br>
     4216
     4217<li>
     4218<b>Krishnappa, Dilip K. and Lyons, Eric and Irwin, David and Zink, Michael</b>
    41214219, &quot;Network capabilities of cloud services for a real time scientific application.&quot
    4122422037th Annual IEEE Conference on Local Computer Networks, Clearwater Beach, FL, USA, IEEE,
    412342212012.
    41244222doi:10.1109/lcn.2012.6423665.
    4125 </li>
    4126 <br>
    4127 
    4128 <li>
    4129 <b>Krishnappa, Dilip K. and Lyons, Eric and Irwin, David and Zink, Michael</b>
    4130 , &quot;Performance of GENI Cloud Testbeds for Real Time Scientific Application.&quot
    4131 First GENI Research and Educational Experiment Workshop (GREE 2012), Los Angeles,
    4132 2012.
    4133 
    41344223</li>
    41354224<br>
     
    43494438<b>Thomas, Charles and Sommers, Joel and Barford, Paul and Kim, Dongchan and Das, Ananya and Segebre, Roberto and Crovella, Mark</b>
    43504439, &quot;A Passive Measurement System for Network Testbeds.&quot
    4351 8th International ICST Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TRIDENTCOM 2012),
    4352 2012.
    4353 
     4440Testbeds and Research Infrastructure. Development of Networks and Communities, Springer Berlin Heidelberg,
     44412012.
     4442doi:10.1007/978-3-642-35576-9&#x005F;14.
    43544443</li>
    43554444<br>
     
    47584847
    47594848<li>
     4849<b>Tsai, Pang-Wei and wen Cheng, Pei and Yang, Chu-Sing and Luo, Mon-Yen</b>
     4850, &quot;Supporting Extensions of VLAN-tagged traffic across OpenFlow Networks.&quot
     48512013 Proceedings Second GENI Research and Educational Experiment Workshop, Salt Lake City, UT, IEEE,
     48522013.
     4853doi:10.1109/GREE.2013.20.
     4854</li>
     4855<br>
     4856
     4857
     4858
     4859<li>
    47604860<b>Valancius, Vytautas and Ravi, Bharath and Feamster, Nick and Snoeren, Alex C.</b>
    47614861, &quot;Quantifying the benefits of joint content and network routing.&quot
     
    48834983
    48844984<li>
    4885 <b>Aug&#x65;&#x0301;, Jordan and Parmentelat, Thierry and Turro, Nicolas and Avakian, Sandrine and Baron, Lo\\ic and Larabi, Mohamed A. and Rahman, Mohammed Y. and Friedman, Timur and Fdida, Serge</b>
     4985<b>Aug&#x65;&#x0301;, Jordan and Parmentelat, Thierry and Turro, Nicolas and Avakian, Sandrine and Baron, Lo&#x69;&#x0308;c and Larabi, Mohamed A. and Rahman, Mohammed Y. and Friedman, Timur and Fdida, Serge</b>
    48864986, &quot;Tools to foster a global federation of testbeds.&quot
    48874987Computer Networks,
     
    54425542
    54435543<li>
    5444 <b>Schwerdel, Dennis and Reuther, Bernd and Zinner, Thomas and M\\uller, Paul and Tran-Gia, Phouc</b>
     5544<b>Schwerdel, Dennis and Reuther, Bernd and Zinner, Thomas and M&#x75;&#x0308;ller, Paul and Tran-Gia, Phouc</b>
    54455545, &quot;Future Internet research and experimentation: The G-Lab approach.&quot
    54465546Computer Networks,
     
    54865586
    54875587<li>
    5488 <b>Su&#x6e;&#x0303;&#x65;&#x0301;, M. and Bergesio, L. and Woesner, H. and Rothe, T. and K\\opsel, A. and Colle, D. and Puype, B. and Simeonidou, D. and Nejabati, R. and Channegowda, M. and Kind, M. and Dietz, T. and Autenrieth, A. and Kotronis, V. and Salvadori, E. and Salsano, S. and K\\orner, M. and Sharma, S.</b>
     5588<b>Su&#x6e;&#x0303;&#x65;&#x0301;, M. and Bergesio, L. and Woesner, H. and Rothe, T. and K&#x6f;&#x0308;psel, A. and Colle, D. and Puype, B. and Simeonidou, D. and Nejabati, R. and Channegowda, M. and Kind, M. and Dietz, T. and Autenrieth, A. and Kotronis, V. and Salvadori, E. and Salsano, S. and K&#x6f;&#x0308;rner, M. and Sharma, S.</b>
    54895589, &quot;Design and implementation of the OFELIA FP7 facility: The European OpenFlow testbed.&quot
    54905590Computer Networks,
     
    56085708<li>
    56095709<b>Xin, Yufeng and Baldin, Ilya and Heermann, Chris and Mandal, Anirban and Ruth, Paul</b>
     5710, &quot;Scaling up applications over distributed clouds with dynamic layer-2 exchange and broadcast service.&quot
     5711Teletraffic Congress (ITC), 2014 26th International, IEEE,
     57122014.
     5713doi:10.1109/itc.2014.6932973.
     5714</li>
     5715<br>
     5716
     5717<li>
     5718<b>Xin, Yufeng and Baldin, Ilya and Heermann, Chris and Mandal, Anirban and Ruth, Paul</b>
    56105719, &quot;Capacity of Inter-cloud Layer-2 Virtual Networking.&quot
    56115720Proceedings of the 2014 ACM SIGCOMM Workshop on Distributed Cloud Computing, Chicago, Illinois, USA, ACM, New York, NY, USA,
    561257212014.
    56135722doi:10.1145/2627566.2627573.
    5614 </li>
    5615 <br>
    5616 
    5617 <li>
    5618 <b>Xin, Yufeng and Baldin, Ilya and Heermann, Chris and Mandal, Anirban and Ruth, Paul</b>
    5619 , &quot;Scaling up applications over distributed clouds with dynamic layer-2 exchange and broadcast service.&quot
    5620 Teletraffic Congress (ITC), 2014 26th International, IEEE,
    5621 2014.
    5622 doi:10.1109/itc.2014.6932973.
    56235723</li>
    56245724<br>
     
    56835783<br>
    56845784<a id="concise-2015"><H2>GENI Publications for 2015</H2></a>
     5785
     5786
     5787<li>
     5788<b>&#x4f;&#x0308;z&#x63;&#x0327;elik, &#x49;&#x0307;lker and Brooks, Richard R.</b>
     5789, &quot;Deceiving entropy based DoS detection.&quot
     5790Computers &#x0026; Security,
     57912015.
     5792doi:10.1016/j.cose.2014.10.013.
     5793</li>
     5794<br>
     5795
    56855796
    56865797
     
    57085819
    57095820<li>
     5821<b>Chen, Xinming and Wolf, Tilman and Griffioen, Jim and Ascigil, Onur and Dutta, Rudra and Rouskas, George and Bhat, Shireesh and Baldin, Ilya and Calvert, Ken</b>
     5822, &quot;Design of a protocol to enable economic transactions for network services.&quot
     5823Communications (ICC), 2015 IEEE International Conference on, IEEE,
     58242015.
     5825doi:10.1109/icc.2015.7249175.
     5826</li>
     5827<br>
     5828
     5829
     5830
     5831<li>
    57105832<b>Chin, Tommy and Mountrouidou, Xenia and Li, Xiangyang and Xiong, Kaiqi</b>
    57115833, &quot;Selective Packet Inspection to Detect DoS Flooding Using Software Defined Networking (SDN).&quot
     
    57525874
    57535875<li>
     5876<b>Juluri, Parikshit</b>
     5877, &quot;Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services.&quot
     5878
     58792015.
     5880
     5881</li>
     5882<br>
     5883
     5884
     5885
     5886<li>
     5887<b>Liu, Xuan</b>
     5888, &quot;Dynamic Virtual Network Restoration with Optimal Standby Virtual Router Selection.&quot
     5889
     58902015.
     5891
     5892</li>
     5893<br>
     5894
     5895
     5896
     5897<li>
    57545898<b>Liu, Xuan and Edwards, Sarah and Riga, Niky and Medhi, Deep</b>
    57555899, &quot;Design of a software-defined resilient virtualized networking environment.&quot
     
    58405984
    58415985<li>
    5842 <b>\\Oz&#x63;&#x0327;elik, &#x49;&#x0307;lker and Brooks, Richard R.</b>
    5843 , &quot;Deceiving entropy based DoS detection.&quot
    5844 Computers &#x0026; Security,
     5986<b>Zhang, Miao and Kissel, Ezra and Swany, Martin</b>
     5987, &quot;Using phoebus data transfer accelerator in cloud environments.&quot
     5988Communications (ICC), 2015 IEEE International Conference on, IEEE,
    584559892015.
    5846 doi:10.1016/j.cose.2014.10.013.
     5990doi:10.1109/icc.2015.7248346.
    58475991</li>
    58485992<br>