Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Iroko: A Framework to Prototype Reinforcement Learning for Data Center
  Traffic Control   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

0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga