Name
Title | ||
|---|---|---|
DeepRoute on Chameleon: Experimenting with Large-scale Reinforcement Learning and SDN on Chameleon Testbed |
Abstract | ||
|---|---|---|
As the numbers of internet users and connected devices continue to multiply, due to big data and Cloud applications, network traffic is growing at an exponential rate. WAN networks, in particular, are witnessing very large traffic spikes cause by large file transfers that last from a few minutes to hours on network links and there is a need to develop innovative ways in which flows can be managed in real-time.In this work, we develop a reinforcement learning approach, in particular Upper-Confidence Algorithm, to learn optimal paths and reroute traffic to improve network utilization. We present throughput and flow diversions using Mininet and demo the technique using Chameleon's Testbed (Bring-Your-Own-Controller [BYOC] functionality). This work is initial implementation towards DeepRoute, which combines Deep reinforcement learning algorithms with SDN controllers to create and route traffic using deployed OpenFlow switches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICNP.2019.8888090 | 2019 IEEE 27th International Conference on Network Protocols (ICNP) |
Keywords | Field | DocType |
big data,Cloud applications,network traffic,WAN networks,network links,reroute traffic,network utilization,deep reinforcement learning algorithms,SDN controllers,route traffic,Bring-Your-Own-Controller functionality,Upper-Confidence Algorithm,SDN on Chameleon Testbed | Computer science,Testbed,Computer network,OpenFlow,Throughput,Big data,Reinforcement learning,Distributed computing,The Internet,Cloud computing | Conference |
ISSN | ISBN | Citations |
1092-1648 | 978-1-7281-2701-9 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bashir Mohammed | 1 | 2 | 1.75 |
| Mariam Kiran | 2 | 121 | 17.83 |
| Nandini Krishnaswamy | 3 | 0 | 0.68 |