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. |