Changes between Version 3 and Version 4 of sol4/MultidomainShakedown


Ignore:
Timestamp:
04/15/14 11:20:24 (8 years ago)
Author:
nriga@bbn.com
Comment:

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  • sol4/MultidomainShakedown

    v3 v4  
    2222
    2323Through experimentations, we will test the scalability of GENI network stitching, GENI’s SDN capabilities across multiple domains and the usability of GENI tools in order to setup multi-domain experiments. We deploy experiments from the list below or defined in consultation with the GPO. Detailed description of the experiments is included in the end. The experiment designs and setup will be made available to the GENI community.
    24        Experiment #A: Application aware Big Data experiment across multi-domain, heterogeneous network so that the nature of the Big Data applications should utilize the best resources. (Testing of GENI Tools including: Stitching, OpenFlow, ORCA/ExoGENI, and other emerging tools). 
    25        Experiment #B: Distributed iceberg detection: We evaluate our close-loop analysis and programmable measurement approach by injecting varying degrees of high volumetric flows in CAIDA Backscatter data traces.
    26        Experiment #C: Attacking OpenFlow Controller: Given an OpenFlow controller server, how can an attacker gain access to that server, and how can she reconfigure it to give her desired control over the OpenFlow controller?
    27        Experiment #D: Intelligent traffic inference: We will implement a prototype of an intelligent SDN based traffic (de)aggregation and measurement paradigm (iSTAMP), which leverages OpenFlow to dynamically partition TCAM entries of a switch/router into two parts.  In the first part, a set of incoming flows are optimally aggregated to provide well-compressed aggregated flow measurements that can lead to the best estimation accuracy via network inference process. The second portion of TCAM entries are dedicated to track/measure the most rewarding flows (defined as flows with the highest impact on the ultimate monitoring application performance) to provide accurate per-flow measurements. These flows are selected and "stamped" as important (or rewarding from monitor's perspective) using an intelligent Multi-Armed Bandit (MAB) based algorithm.
     24 *      Experiment #A: Application aware Big Data experiment across multi-domain, heterogeneous network so that the nature of the Big Data applications should utilize the best resources. (Testing of GENI Tools including: Stitching, OpenFlow, ORCA/ExoGENI, and other emerging tools). 
     25 *      Experiment #B: Distributed iceberg detection: We evaluate our close-loop analysis and programmable measurement approach by injecting varying degrees of high volumetric flows in CAIDA Backscatter data traces.
     26 *      Experiment #C: Attacking OpenFlow Controller: Given an OpenFlow controller server, how can an attacker gain access to that server, and how can she reconfigure it to give her desired control over the OpenFlow controller?
     27 *      Experiment #D: Intelligent traffic inference: We will implement a prototype of an intelligent SDN based traffic (de)aggregation and measurement paradigm (iSTAMP), which leverages OpenFlow to dynamically partition TCAM entries of a switch/router into two parts.  In the first part, a set of incoming flows are optimally aggregated to provide well-compressed aggregated flow measurements that can lead to the best estimation accuracy via network inference process. The second portion of TCAM entries are dedicated to track/measure the most rewarding flows (defined as flows with the highest impact on the ultimate monitoring application performance) to provide accurate per-flow measurements. These flows are selected and "stamped" as important (or rewarding from monitor's perspective) using an intelligent Multi-Armed Bandit (MAB) based algorithm.
    2828
    2929Through experimentation we will also test GENI’s capabilities for running multi-domain experiments and will identify limitations of the current deployment. We will develop prototype services that will demonstrate benefits of enhancing GENI to address identified limitations.