Performance Analysis of Rules Generated Hybrid Optimization Algorithm for Resource Allocation and Migration in the Cloud Environment

Abstract

The cloud service model is generally a framework that provides three types of services to the client. These three commonly used models for delivering cloud computing services are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The IaaS model is sometimes referred to as the Hardware as a Service (HaaS) model. One of the most influential characteristics of IaaS is to have higher scalability in sub-service provisions. Dynamic and flexible, along with having automated administrative task operation ability. For any cloud computing system, Infrastructure as a Service (IaaS) is typically the foundational layer, providing access to physical servers, virtual machines (VMs), and other resources necessary to support running applications. In the present scenario, the global cloud data centers are facing a relatively higher number of issues like lacking proper capacity planning along with adequate capacity management, reducing the cost and reducing the energy stages or, on the other hand, increasing energy efficiency. This study aims to propose a resource allocation and migration technique for improving energy efficiency. The proposed study will be considering the Rule Generated Hybrid Optimization technique. The results indicated that the suggested approach outperform Spark Lion Whale Optimization when energy consumption and resource utilization was considered.