Dynamic Multi-Objective Workflow Scheduling Model in Cloud Environment Based on Adaptive Mutation Strategy

Abstract

In the cloud computing environment, workflow scheduling presents a significant challenge due to the unpredictable and dynamic nature of user demands and cloud resources. To address the complexities of workflow scheduling, this paper introduces a dynamic multi-objective workflow scheduling model that comprehensively considers task completion time, load balancing, as well as dynamic changes in power consumption and cost in real-world scenarios. To effectively solve this model and better adapt to dynamic multi-objective optimization problems, we propose a dynamic reference vector guided evolutionary algorithm (DRVEA). The proposed algorithm incorporates an adaptive random mutation strategy, which dynamically adjusts the evolutionary process based on changing optimization goals, thereby enhancing convergence and solution diversity. Experimental results, obtained from both workflow scheduling simulations and standard multi-objective test environments, demonstrate that the proposed algorithm outperforms existing methods, achieving superior results in both solution quality and adaptability.