Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
Abstract
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far render it unacceptable for use in datacenter networks. In this paper, we analyze the requirements for RL to succeed in the datacenter context. We present a new emulator, Iroko, which we developed to support different network topologies, congestion control algorithms, and deployment scenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fair evaluation of different RL and traditional CC algorithms under the same conditions. We present initial benchmarks on three deep RL algorithms compared to TCP New Vegas and DCTCP. Our results show that these algorithms are able to learn a CC policy which exceeds the performance of TCP New Vegas on a dumbbell and fat-tree topology. We make our emulator open-source and publicly available: https://github.com/dcgym/iroko
Cite
@article{arxiv.1812.09975,
title = {Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control},
author = {Fabian Ruffy and Michael Przystupa and Ivan Beschastnikh},
journal= {arXiv preprint arXiv:1812.09975},
year = {2020}
}
Comments
5 figures, 1 Table, 11 pages, Accepted to http://mlforsystems.org/accepted_papers.html (ML for Systems) workshop