Efficient Job Scheduling in Cloud Environments using Reinforcement Learning Actor-Critic Models

Abstract

Optimized scheduling is an important task in the scheduling of job execution on cloud virtual machines, where optimal resource usage and a shorter makespan have become important features. When scheduling jobs, balancing the workload across all available virtual machines provides optimized performance. Reinforcement learning is a better optimization algorithm due to its adaptability to dynamic environments and balancing exploration and exploitation. To perform optimized balancing of job scheduling, an Actor-Critic-based reinforcement algorithm is applied in this work. The Alibaba cloud dataset is used to analyze the algorithm's performance. Policy constraints are made for assigning the number of tasks to the scheduler. During the learning phase, the rewards turn out to be negative. After the learning phase, the rewards stabilize. The results show that the algorithm is able to produce positive reward points. A 5% reduction in the makespan of job execution demonstrates the improvement in scheduling and resource use.