A clonal selection mechanism for load balancing in the cloud computing system

Abstract

Clients demand more services and better results as cloud computing expand quickly. As a result, the load balance management component in cloud computing systems should be able to redistribute resources by user needs. The massive increase in users using cloud services increases the load on the cloud, making its management difficult. Load balancing is therefore suggested as a way to handle the excess load on the cloud. Algorithms for load balancing are the basis of load balancing. Effective load balancing and job scheduling techniques have been proposed for this reason. The main requirement of cloud computing is to share and supply computing resources, such as virtual machines, based on user demand. In the cloud, load-balancing algorithms can be used to allocate virtual machines to user requests. Requests are planned as the number of users grows, and as the number of requests grows, some scheduling algorithms in cloud computing struggle to serve users' needs effectively. As a result, effective algorithms are required to reduce processing expenses, calculation time, and energy consumption. To address these objectives, hybrid algorithms were developed. For the clonal selection algorithm, load-balancing techniques are used in this research. This novel strategy is also tested using a cloud Analyst simulator. Finally, the findings are examined and contrasted with prior research.