Hybrid Genetic Algorithm for Cloud Computing Applications

TL;DR

This paper introduces a hybrid genetic algorithm combined with fuzzy theory for efficient job scheduling in cloud computing, aiming to improve load balancing, reduce execution time and cost, and optimize resource utilization.

Contribution

It presents a novel hybrid algorithm that integrates fuzzy theory with genetic algorithms to enhance cloud job scheduling efficiency and reduce computational iterations.

Findings

Improved execution time and cost efficiency.

Enhanced load balancing and resource utilization.

Reduced algorithm iteration count.

Abstract

In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).