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Recent networking research has identified that data-driven congestion control(CC) can be more efficient than traditional CC in TCP. Deep reinforcementlearning (RL), in particular, has the potential to learn optimal networkpolicies. However, RL suffers from instability and over-fitting, deficiencieswhich so far render it unacceptable for use in datacenter networks. In thispaper, we analyze the requirements for RL to succeed in the datacenter context.We present a new emulator, Iroko, which we developed to support differentnetwork topologies, congestion control algorithms, and deployment scenarios.Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fairevaluation of different RL and traditional CC algorithms under the sameconditions. We present initial benchmarks on three deep RL algorithms comparedto TCP New Vegas and DCTCP. Our results show that these algorithms are able tolearn a CC policy which exceeds the performance of TCP New Vegas on a dumbbelland fat-tree topology. We make our emulator open-source and publicly available:https://github.com/dcgym/iroko

Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control