Joint Optimization of Container Resource Defragmentation and Task Scheduling in Queueing Cloud Computing: A DRL-Based Approach

Container-based virtualization has become pivotal in cloud computing, and resource fragmentation is inevitable due to the frequency of container deployment/termination and the heterogeneous nature of IoT tasks. In queuing cloud systems, resource defragmentation and task scheduling are interdependent yet rarely co-optimized in existing research. This paper addresses this gap by investigating the joint optimization of resource defragmentation and task scheduling in a queuing cloud computing system. We first formulate the problem to minimize task completion time and maximize resource utilization, then transform it into an online decision problem. We propose a Deep Reinforcement Learning (DRL)-based two-layer iterative approach called DRL-RDG, which uses a Resource Defragmentation approach based on a Greedy strategy (RDG) to find the optimal container migration solution and a DRL algorithm to learn the optimal task-scheduling solution. Simulation results show that DRL-RDG achieves a low average task completion time and high resource utilization, demonstrating its effectiveness in queuing cloud environments.