Cluster and reinforcement learning-based multi-objective evolutionary algorithm for joint scheduling of virtual machines and prioritize tasks in cloud computing

Abstract

In today’s world, cloud computing is considered an essential on-demand service that is facing an ongoing problem in Virtual Machine (VM) placement and task scheduling optimization that simultaneously improves server efficiency and user experience. Considering these challenges, this paper aims to reduce the makespan, cost, and total tardiness in Joint Scheduling of Virtual Machines and Prioritize Tasks (JSVPT) by a multi-objective optimization framework. We designed a novel Cluster-Based Multi-Objective Evolutionary Algorithm (MOEA-CD/RLPD) framework, which includes a three-tier encoding scheme with Reinforcement Learning (RL)-guided local search, preselection, and dynamic resource allocation strategy to solve the problem. To guide the search process, we employ K-means clustering to decompose the population into diverse subgroups, promoting balanced exploration. The pre-selection mechanism uses a classifier to identify promising solutions in the decision space, which allows resources to be used effectively. Reinforcement learning adaptively selects intensification operators based on reward feedback, improving exploitation by intensifying promising regions of the search space. An Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is incorporated to maintain a diverse and high-quality Pareto archive. The performance of the proposed algorithm is assessed on multiple test instances covering different scales and benchmarked against five state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs). Experimental studies demonstrate that the proposed algorithm outperforms most existing algorithms in the literature.