DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing

Abstract

Cloud computing is a computing method based on the Internet designed to share resources through virtualization technology. For a large number of requests waiting to be processed, task scheduling is used to reasonably allocate computing resources to requests. With the rapid development of computer hardware and software, deep reinforcement learning (DRL) provides a new direction for better solving task scheduling problems. In this paper, we propose a novel DRL-based dynamic load balancing task scheduling algorithm under service-level agreement (SLA) constraints to reduce the load imbalance of virtual machines (VMs) and task rejection rate. First, we use the DRL method to select a suitable VM for the task and then determine whether to execute the task on the selected VM violates the SLA. If the SLA is violated, the task is refused and feedback a negative reward for DRL training; otherwise, the task is received and executed, and feedback a reward according to the balance of the VMs load after the task is executed. Compared with three other task scheduling algorithms applied to randomly generated benchmark and Google real user workload trace benchmark, the proposed algorithm exhibits the best performance in balancing VMs load and reducing the task rejection rate, improving the overall level of cloud computing services.