A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud

Delong Cui, Zhiping Peng, Jianbin Xiong, Bo Xu, Weiwei Lin

Abstract: Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q-learning algorithm. Both simulation and real cloud platform experiment results demonstrate the efficiency of the proposed job scheduling scheme.