Multi-Agent Deep Reinforcement Learning-Based Fine-Grained Traffic Scheduling in Data Center Networks

Huiting Wang, Yazhi Liu (), Wei Li and Zhigang Yang
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Huiting Wang: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Yazhi Liu: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Wei Li: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Zhigang Yang: College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China

Future Internet, 2024, vol. 16, issue 4, 1-17

Abstract: In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.

Keywords: data center network; traffic scheduling; multi-agent deep reinforcement learning; in-band network telemetry; programmable data plane (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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