Performance Modelling of Evolutionary Algorithms for Task Scheduling in Cloud Computing

The efficient task scheduling problem in cloud computing is considered a complex optimization problem with substantial implications for performance, resource management efficiency, and cost control. The complexity of scheduling arises from the ever-changing characteristics of cloud environments and the diverse nature of resources. Due to their resilience and flexible search capabilities, evolutionary algorithms (EAs) such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are promising approaches to addressing scheduling problems. Conventional scheduling algorithms often face challenges in scheduling complex problems in highly dynamic cloud environment. However, due to natural selection, Evolutionary Algorithms (EAs) offer a promising solution by providing adaptive and near-optimal solutions for task allocation. In this article, we present a framework for performance modeling to evaluate evolutionary algorithms in cloud task scheduling. The framework assesses key performance indicators by reducing makespan and execution costs while enhancing resource utilization. Subsequently, we compare the performance of GA, PSO, and ACO in terms of makespan, cost, and resource utilization.