Hybrid Approach at Cloud Data Center for Improving Makespan and Creating a Better Energy Enviornment

Abstract

The field of cloud computing is growing quickly, and in order to achieve maximum performance and cost savings, effective resource management is required. The goal of this research is to adopt a hybrid technique to improve makespan in cloud data centers. In order to meet the increasing demand for cloud services, the main objective is to establish cost-effective and efficient cloud resource management. This work attempts to create a hybrid method, called HGWCA, by merging two different algorithms. The algorithms for Grey Wolf and Cat Swarm optimizations. The makespan, throughput, degree of imbalance, and turnaround time are the evaluation criteria that are employed. When compared to alternative algorithms, the suggested HGWCA method performs better in each of these metrics. The outcomes demonstrate that the hybrid strategy greatly enhances cloud data center performance based on makespan, degree of imbalance, throughput, and turnaround time. According to the study's findings, there is a lot of room for improvement in cloud data center performance with the suggested hybrid approach. Subsequent investigations could examine the suggested methodology in more extensive and intricate cloud data center setups, in addition to investigating the incorporation of extra optimization methods to enhance overall efficiency. The makespan improvement attained by the hybrid approach that was suggested was 5.18%. improvement in the result.