Intelligent load balancing in data center software-defined networks

Abstract

In response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load-balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software-defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real-time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat-tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal-cost multipath and Hedera.