Changes between Version 22 and Version 23 of GEC25Agenda/Demos


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Timestamp:
03/01/17 11:09:49 (7 years ago)
Author:
lnevers@bbn.com
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  • GEC25Agenda/Demos

    v22 v23  
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    37 "This demo will show a prototype of an SDN-based traffic analysis resistant network architecture (TRAN). TRAN intends to explore an end-to-end network architecture that removes traffic analysis vulnerabilities by using SDN-based solutions to ""shake up"" the foundation of the Internet architecture - IP.
     37This demo will show a prototype of an SDN-based traffic analysis resistant network architecture (TRAN). TRAN intends to explore an end-to-end network architecture that removes traffic analysis vulnerabilities by using SDN-based solutions to ""shake up"" the foundation of the Internet architecture - IP.
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    39 One of proposed goal for TARN is to circumvent the Internet censorship without being subject to traffic analysis, by reducing the likelihood of being ""tracked"". Unlike other proxy-based solutions (e.g,. Tor, Psiphon, Decoy Routing), TARN architecture avoids forcing Internet users to trust intermediate proxy nodes. "
     39One of proposed goal for TARN is to circumvent the Internet censorship without being subject to traffic analysis, by reducing the likelihood of being ""tracked"". Unlike other proxy-based solutions (e.g,. Tor, Psiphon, Decoy Routing), TARN architecture avoids forcing Internet users to trust intermediate proxy nodes.
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    4141== Demonstration of a GENI based cyber physical test bed for advanced manufacturing ==
     
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    104 "We are developing a new set of cloud/fog protocols to support computer vision applications related to the field of real-time visual situational awareness (e.g., tracking objects of interest, 3D scene reconstructions, augmented reality-based communications, etc) which are critical to first responders. These applications require seamless processing of imagery/video at the network edge and core cloud platforms with resilient performance that caters to user Quality of Experience (QoE) expectations. The absence or poor wireless communications at the edge networks near incident scenes further complicate an exploitation of these applications. As part of our project activities, we have setup a realistic virtual environment testbed in GENI and developed an SDN controller to evaluate our hybrid cloud-fog architecture along with the proposed algorithms. Specifically, to enable core-cloud computation we used high-performance nodes for handling large instance processing (e.g., tracking of objects, data fusion for the 3D scene reconstruction). We also used low-performance nodes for handling small instance processing (e.g., image tilling, stabilization, geo-projection) through fogs at the SDN network-edge. To transfer data between the core-cloud and fogs over SDN network, we used !OpenFlow Virtual Switches.  Finally, to compensate the lack of wireless networking at the edge in GENI our testbed setup also included campus enterprise network with connected clients. In addition to visual data processing speed up, our preliminary experiment results indicate the need for sustained throughput at the wireless edge networks, and use of novel geographical routing protocols to enhance responders QoE.
    105 At the workshop, we will share what barriers we are overcoming in GENI to create a realistic cloud-fog testbed, and how we are building upon these research results on performance engineering of public safety applications. In addition, we will share how we are working towards setting up a city-scale testbed with city collaborator sites and first responder agencies."
     104We are developing a new set of cloud/fog protocols to support computer vision applications related to the field of real-time visual situational awareness (e.g., tracking objects of interest, 3D scene reconstructions, augmented reality-based communications, etc) which are critical to first responders. These applications require seamless processing of imagery/video at the network edge and core cloud platforms with resilient performance that caters to user Quality of Experience (QoE) expectations. The absence or poor wireless communications at the edge networks near incident scenes further complicate an exploitation of these applications. As part of our project activities, we have setup a realistic virtual environment testbed in GENI and developed an SDN controller to evaluate our hybrid cloud-fog architecture along with the proposed algorithms. Specifically, to enable core-cloud computation we used high-performance nodes for handling large instance processing (e.g., tracking of objects, data fusion for the 3D scene reconstruction). We also used low-performance nodes for handling small instance processing (e.g., image tilling, stabilization, geo-projection) through fogs at the SDN network-edge. To transfer data between the core-cloud and fogs over SDN network, we used !OpenFlow Virtual Switches.  Finally, to compensate the lack of wireless networking at the edge in GENI our testbed setup also included campus enterprise network with connected clients. In addition to visual data processing speed up, our preliminary experiment results indicate the need for sustained throughput at the wireless edge networks, and use of novel geographical routing protocols to enhance responders QoE.
     105At the workshop, we will share what barriers we are overcoming in GENI to create a realistic cloud-fog testbed, and how we are building upon these research results on performance engineering of public safety applications. In addition, we will share how we are working towards setting up a city-scale testbed with city collaborator sites and first responder agencies.
    106106
    107107== Layer-Two Peering across SAVI and GENI Testbeds using !HyperExchange ==
     
    121121__Authors:__ - Garegin Grigoryan, Keivan Bahmani, Grayson Schermerhorn, Yaoqing Liu - Clarkson University
    122122
    123 "Data centers are massive infrastructures that host today’s internet and cloud services.  A typical data center is consuming around energy budget of 25000 households and almost about 200 times electricity than that of a standard office space [1].  This massive amount of energy motivated a growing interest in using green renewable energy at data centers. Google is planning to provide 100% of electricity supplies for its data centers and offices using the wind and solar power by the end of 2017 [2].
    124 The amount of renewable energy that can be generated in data centers depends on their location and time. We introduce a Green Energy Aware SDN platform with an SDN controller, that schedules client requests to servers depending on delay and the current renewable energy generated at the data centers. In this work, we adopt National Solar Radiation Database (NSRDB), maintained by National Renewable Energy Laboratory (NREL) to estimate the amount of solar renewable energy that can be generated in each data center. Our platform can be used for scheduling client requests not solely based on green energy, but other parameters from data centers (e.g. CPU utilization, delay requirements).[1]    M. Poess and R. O. Nambiar, “Energy Cost, the Key Challenge of Today’s Data Centers: A Power Consumption Analysis of TPC-C Results,” Proc VLDB Endow, vol. 1, no. 2, pp. 1229–1240, Aug. 2008. [2]      “We’re set to reach 100% renewable energy — and it’s just the beginning,” Google, 06-Dec-2016. [Online]. Available: http://blog.google:443/topics/environment/100-percent-renewable-energy/. [Accessed: 22-Feb-2017]."
     123Data centers are massive infrastructures that host today’s internet and cloud services.  A typical data center is consuming around energy budget of 25000 households and almost about 200 times electricity than that of a standard office space [1].  This massive amount of energy motivated a growing interest in using green renewable energy at data centers. Google is planning to provide 100% of electricity supplies for its data centers and offices using the wind and solar power by the end of 2017 [2].
     124The amount of renewable energy that can be generated in data centers depends on their location and time. We introduce a Green Energy Aware SDN platform with an SDN controller, that schedules client requests to servers depending on delay and the current renewable energy generated at the data centers. In this work, we adopt National Solar Radiation Database (NSRDB), maintained by National Renewable Energy Laboratory (NREL) to estimate the amount of solar renewable energy that can be generated in each data center. Our platform can be used for scheduling client requests not solely based on green energy, but other parameters from data centers (e.g. CPU utilization, delay requirements).[1]    M. Poess and R. O. Nambiar, “Energy Cost, the Key Challenge of Today’s Data Centers: A Power Consumption Analysis of TPC-C Results,” Proc VLDB Endow, vol. 1, no. 2, pp. 1229–1240, Aug. 2008. [2]      “We’re set to reach 100% renewable energy — and it’s just the beginning,” Google, 06-Dec-2016. [Online]. Available: http://blog.google:443/topics/environment/100-percent-renewable-energy/. [Accessed: 22-Feb-2017].
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    129129__Authors:__ Rick !McGeer, Matt Hemmings, Andy Bavier, Glenn Ricart - US Ignite
    130130
    131 "!PlanetIgnite is a general-purpose, Infrastructure-as-a-Service, self-assembling, lightweight edge cloud on virtualized infrastructure with support for single-pane-of-glass distributed application configuration and deployment. This is an entirely new concept. !PlanetLab, GENI, and SAVI are general-purpose IaaS edge clouds, but require top-down installation and dedicated hardware resources at each site and do not offer single- pane-of-glass application deployment. Seattle is a lightweight self-assembling edge cloud that offers single- pane-of-class configuration and control, but developers are restricted to using a subset of Python. !PlanetIgnite is a Containers-as-a-Service Edge Cloud which offers Docker Containers to each !PlanetIgnite user. A !PlanetIgnite node is an off-the-shelf Ubuntu 14.04 Virtual machine with Docker installed, meaning it can be installed on any edge node where a VM with a routable v4 address is available. Adding a !PlanetIgnite node to the infrastructure is simple: a site wishing to host a !PlanetIgnite node simply downloads the image; on boot, the new !PlanetIgnite node registers with the !PlanetIgnite portal, which runs a series of acceptance tests. Once complete, the image is registered and the node is added to the set of !PlanetIgnite sites.
    132 "
     131!PlanetIgnite is a general-purpose, Infrastructure-as-a-Service, self-assembling, lightweight edge cloud on virtualized infrastructure with support for single-pane-of-glass distributed application configuration and deployment. This is an entirely new concept. !PlanetLab, GENI, and SAVI are general-purpose IaaS edge clouds, but require top-down installation and dedicated hardware resources at each site and do not offer single- pane-of-glass application deployment. Seattle is a lightweight self-assembling edge cloud that offers single- pane-of-class configuration and control, but developers are restricted to using a subset of Python. !PlanetIgnite is a Containers-as-a-Service Edge Cloud which offers Docker Containers to each !PlanetIgnite user. A !PlanetIgnite node is an off-the-shelf Ubuntu 14.04 Virtual machine with Docker installed, meaning it can be installed on any edge node where a VM with a routable v4 address is available. Adding a !PlanetIgnite node to the infrastructure is simple: a site wishing to host a !PlanetIgnite node simply downloads the image; on boot, the new !PlanetIgnite node registers with the !PlanetIgnite portal, which runs a series of acceptance tests. Once complete, the image is registered and the node is added to the set of !PlanetIgnite sites.