The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization
Open accessAbstract
Traffic optimizations (TO, e.g. flow scheduling, load balancing) in datacenters are difficult online decision-making problems. Previously, they are done with heuristics relying on operators' understanding of the workload and environment. Designing and implementing proper TO algorithms thus take at least weeks. Encouraged by recent successes in applying deep reinforcement learning (DRL) techniques to solve complex online control problems, we study if DRL can be used for automatic TO without human-intervention. However, our experiments show that the latency of current DRL systems cannot handle flow-level TO at the scale of current datacenters, because short flows (which constitute the majority of traffic) are usually gone before decisions can be made.
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Cites
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Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
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NetLLM: adapting large language models for networking
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“Many networking tasks such as ABR [103] and CJS [78] employ RL to solve complex system optimization problems, which involve active interaction between the environments (e.g., network environments with varying band-width or workload patterns) to collect experiences for reward optimization.”
BackgroundMethodNetwork planning with deep reinforcement learning
Zhu, H., Gupta, V., Ahuja, S. S., Tian, Y., Zhang, Y., & Jin, X. (2021) · Proceedings of the 2021 ACM SIGCOMM 2021 Conference · doi:10.1145/3452296.3472902 · Cited by 112
Metadata
- Authors
- Chen, L., Lingys, J., Chen, K., & Liu, F.
- Venue
- Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication
- Publisher
- ACM
- Published
- 2018-08-07
- Pages
- 191-205
- OpenAlex
- W2824027552
- Cited by
- 282
- References
- 38
- FWCI
- 27.78
- Type
- conference paper
- Language
- en
- Open access
- gold
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