Traffic-Aware Load Balancing Based on Deep Reinforcement Learning in Cloud-Based Industrial Data Centers

Modern industrial datacenter networks employ multirooted tree topologies to accommodate a diverse range of cloud applications, which generate heterogeneous traffic with low-latency short flows and high-throughput long flows. Recently, the proposed learning-based load balancing mechanisms are resilient to dynamic network, but they are agnostic to heterogeneous traffic, resulting in large tail delay. In this article, we propose a new deep reinforcement learning (DRL) based load balancing called DRLB, which uses DRL with the distributed distributional deterministic policy gradients algorithm to make (re)routing for long flows, and adopts the weighted cost multipathing mechanism for short flows. Furthermore, this article introduces a traffic feature-based dynamic training cycle mechanism to adaptively adjust the training cycles. The experimental results show DRLB reduces the flow completion time of short flows by up to 58% and improves the throughput of long flows by 38% compared to the state-of-the-art load balancing mechanisms.