A reinforcement learning approach to prioritized workflow scheduling for cloud load balancing

Cloud is a distributed computing system that offers resources and services depending on the need. The primary concerns with managing cloud resources will be task scheduling and resource provisioning in a cloud environment. Computational complexity and the interdependency of scientific procedures restrict the use of cloud resource management. Balanced load is accomplished by emphasis on workflow detection and scheduling based on utilization percentage. A novel two stage framework is designed to reduce the number of jobs in the workflow scheduling jobs that cannot meet their deadlines. This paper proposes a new approach called Hybridized Fennec Rat Swarm Optimization (HFe-RSO), which assigns priority to the tasks. Then, a workflow scheduling model, Enhanced Soft Actor-Critic Deep Reinforcement Learning (ESAC-DRL), is developed for efficient resource management by considering the task priority and utilization rate. Workflow benchmarks such as Montage, CyberShake, Sipht, Epigenomics, and Inspiral are used to assess the performance of the proposed model. The performance of the proposed approach is analysed using cost, makespan, and utilization rate, and compared with state-of-the-art approaches. By using the proposed approach, the cost of $259.11, makespan of 99.84ms, and the utilization rate 96.20%, was achieved. The experimental results demonstrate the significance of the proposed approach over other existing approaches.