Multi-Criteria Optimization of Scientific Workflow Schedules for Improved Energy Efficiency in Cloud Infrastructures

Abstract

Rising global dependence on cloud services has become crucial for enterprises, aiming to guarantee continuous data accessibility while pursuing enhanced energy efficiency and minimized carbon emissions from data centers. However, the persistent challenge of high-energy consumption in these facilities necessitates a concentrated approach toward energy reduction. This paper introduces an innovative multi-objective scheduling strategy for scientific workflows, tailored for heterogeneous computing environments. Our method employs a hybrid genetic algorithm, incorporating Hill Climbing to generate an initial population of chromosomes. Subsequently, a genetic algorithm optimizes task assignments to the most suitable virtual machines, utilizing a meticulously designed fitness function to evaluate each chromosome's suitability for solving the scheduling problem. Through extensive experimentation, we demonstrate that our proposed algorithm outperforms other scheduling techniques in terms of solution quality, contributing to reduced energy consumption, processing duration, and cost. We contend that this innovative approach holds substantial potential in mitigating the energy consumption and carbon footprint associated with cloud data centers, offering a sustainable and environmentally conscious solution for scientific workflow scheduling.