| | 19 | |
| | 20 | === Disk-to-Disk transfer using SOS === |
| | 21 | We have shown the increase in network performance using SOS in earlier demos. In this demo we will demonstrate the disk-to-disk transfer of large data sets using SOS and show the performance increase. |
| | 22 | |
| | 23 | '''Presenters:''' |
| | 24 | * Junaid Zulfiqar, Clemson University |
| | 25 | * Khayam Gondal, Clemson University |
| | 26 | * Geddings Barrineau, Clemson University |
| | 27 | * Kuang-Ching Wang, Clemson University |
| | 28 | |
| | 29 | === On Balancing Load to Quickly Detect and Stop Attack Traffic === |
| | 30 | Our previous work [1] proposed a control theoretic load balancer that offloaded traffic from an overloaded intrusion detection application (i.e., Snort) instance to another. We leveraged the management architecture of RINA to publish load and alert information from Snort instances to a Ryu SDN controller. In this demo, we generalize the framework with an “attack analyzer” that analyzes different kinds of intrusion alerts. On the GENI testbed, we generate DoS and port-scanning attack traffic using hping3 and Nmap tools, respectively. The controller communicates with the switch using OpenFlow to balance replicated traffic across Snort instances for analysis and to stop attack traffic. We show that under high load conditions, load balancing can help detect and stop attacks quickly. We show the impact of network delays and different control theoretic load balancers. |
| | 31 | |
| | 32 | '''Presenters:''' |
| | 33 | * Nabeel Akhtar, Boston University |
| | 34 | * Marzieh Babaeianjelodar, Clarkson University |
| | 35 | * Ibrahim Matta, Boston University |
| | 36 | * Yaoqing Liu, Clarkson University |
| | 37 | |
| | 38 | === A distributed multi-loop networked system for Wide are control of large power grid === |
| | 39 | We are going to demonstrate a prototype system that includes distributed Cloud, SDN, and distributed power grid control applications instantiated in ExoGeni testbed. This system aims to design three interactive control loops in controlling the compute, network and the physical systems. The demo would show that the SDN network connecting the distributed application running in the Cloud. The SDN control would change the paths based on active end-to-end latency measurement and the application would show improved performance in term of physical system stability with latency awareness. |
| | 40 | |
| | 41 | '''Presenters:''' |
| | 42 | * Haoqi Ni |
| | 43 | * Mohamed Rahouti, University of Southern Florida |
| | 44 | * Aranya Chakrabortty |
| | 45 | * Kaiqi Xiong, University of Southern Florida |
| | 46 | * Yufeng Xin, RENCI |
| | 47 | |
| | 48 | |
