Work

AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization

Year: 2018

Type: conference-paper

Abstract: 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.

Source: ACM SIGCOMM Conference (SIGCOMM)

DOI: 10.1145/3230543.3230551

Authors: Li Chen, Justinas Lingys, Kai Chen, Feng Liu

Institutions: Hong Kong University of Science and Technology, SAIC Motor (China)

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: National Key Research and Development Program of China