Joint optimization of resource utilization and makespan in cloud computing using genetic algorithm

Abstract Cloud computing offers cost-effective and on-demand remote access to computing, storage and networking resources. But it faces major technical challenges in resource management and task scheduling due to dynamic and heterogeneous resource configurations. Effective task scheduling is essential to optimize the virtualized or physical resources usage and to avoid overutilization or underutilization of the resources. This work addresses the load balancing problem in cloud environment by scheduling tasks on virtual machines using genetic algorithm. In this work, we have considered makespan and resource wastage as the main objectives. To solve this, a genetic algorithm-based optimization model is proposed for efficient task scheduling and workload distribution. The proposed metaheuristic uses a roulette wheel selection strategy, one-point crossover, mutation and elitism operators to get high-quality solutions. The performance of the proposed approach is evaluated using the simulation study conducted on real-world the Cloud-Fog computing datasets containing heterogeneous AWS EC2 VM configurations. We have performed the comparative study of the proposed method by comparing it with Particle Swarm Optimization, Artificial Bee Colony Optimization, Black Widow Optimization and other baseline methods. The results demonstrate that the proposed algorithm is effective and superior to the compared metaheuristic techniques. Specifically, on average, it achieves reductions of up to 16.04% in makespan and 32.52% in resource wastage, making it suitable for integration into cloud systems.