期刊
出版社
IEEE COMPUTER SOC
DOI: 10.1109/ICNP52444.2021.9651930
关键词
Traffic Engineering; Routing Optimization; Multi-Agent Reinforcement Learning; Graph Neural Networks
类别
资金
- Spanish MINECO [TEC2017-90034-C2-1-R]
- Catalan Institution for Research and Advanced Studies (ICREA)
- Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia
- European Social Fund
This research analyzes whether modern machine learning methods are suitable for traffic engineering optimization, and implements a distributed system based on multi-agent reinforcement learning and graph neural networks. Experimental results show that the proposed solution achieves comparable performance to constraint programming technology in various network scenarios while significantly reducing execution time.
Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).
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