A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

TL;DR

This paper introduces an energy-efficient resource allocation algorithm based on immune clonal optimization, aiming to reduce energy consumption and improve response time in cloud data centers.

Contribution

It presents a novel immune clonal algorithm tailored for energy-efficient resource allocation in cloud computing environments.

Findings

Significant reduction in response time and makespan.

Enhanced energy efficiency of data centers.

Effective compliance with service level agreements.

Abstract

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.