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Data-driven data center traffic control Ruffy Varga, Fabian
Abstract
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in data centers (DCs). 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 DC networks. We analyze the requirements for data-driven policies to succeed in the DC context. And, we present a new emulator, Iroko, which supports different network topologies, DC traffic engineering algorithms, and deployment scenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fair evaluation of RL against traditional algorithms under equal conditions. We present initial benchmarks of three deep RL algorithms against TCP New Vegas and DCTCP. The results show that out-of-the-box algorithms are able to learn a CC policy with comparative performance to TCP in our emulator. We make Iroko open-source and publicly available: https://github.com/dcgym/iroko.
Item Metadata
Title |
Data-driven data center traffic control
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2019
|
Description |
Recent networking research has identified that data-driven congestion control
(CC) can be more efficient than traditional CC in data centers (DCs). 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 DC networks. We analyze
the requirements for data-driven policies to succeed in the DC context.
And, we present a new emulator, Iroko, which supports different network
topologies, DC traffic engineering algorithms, and deployment
scenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast
and fair evaluation of RL against traditional algorithms under equal conditions.
We present initial benchmarks of three deep RL algorithms against TCP New Vegas
and DCTCP. The results show that out-of-the-box algorithms are able to learn a
CC policy with comparative performance to TCP in our emulator. We make Iroko
open-source and publicly available: https://github.com/dcgym/iroko.
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Genre | |
Type | |
Language |
eng
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Date Available |
2019-04-23
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial 4.0 International
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DOI |
10.14288/1.0378362
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2019-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial 4.0 International