Name
Abstract | ||
|---|---|---|
We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task of modulating traffic sourcesu0027 data-transmission rates so as to efficiently and fairly allocate network resources. Congestion control is fundamental to computer networking research and practice, and has recently been the subject of extensive attention in light of the advent of challenging Internet applications such as live video, augmented and virtual reality, Internet-of-Things, and more. We build on the recently introduced Performance-oriented Congestion Control (PCC) framework to formulate congestion control protocol design as an RL task. Our RL framework opens up opportunities for network practitioners, and even application developers, to train congestion control models that fit their local performance objectives based on small, bootstrapped models, or complex, custom models, as their resources and requirements merit. We present and discuss the challenges that must be overcome so as to realize our long-term vision for Internet congestion control. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Networking and Internet Architecture | Test suite,Virtual reality,Data traffic,Computer science,Computer network,Network congestion,Application domain,Internet congestion control,Distributed computing,Reinforcement learning,The Internet |
DocType | Volume | Citations |
Journal | abs/1810.03259 | 2 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan Jay | 1 | 6 | 1.77 |
| Noga H. Rotman | 2 | 6 | 1.43 |
| P. Brighten Godfrey | 3 | 2519 | 145.37 |
| Michael Schapira | 4 | 1122 | 79.89 |
| Aviv Tamar | 5 | 275 | 24.04 |