SIMULATED ANNEALING APPROACH TO COST-BASED MULTI- QUALITY OF SERVICE JOB SCHEDULING IN CLOUD COMPUTING ENVIROMENT

Abdullah Abdullah

doi:10.3844/ajassp.2014.872.877

Abstract

Cloud computing environments facilitate applications by providing visualized resources that can be provisioned dynamically. The advent of cloud computing as a new model of service provisioning in distributed systems, encourages researchers to investigate its benefits and drawbacks in executing scientific applications such as workflows. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks with minimum scheduler execution time. A Genetic Algorithm (GA) for job scheduling has been proposed and produced good results. The main disadvantage of GA algorithm is time consuming problem. In this study, a novel Simulated Annealing (SA) algorithm is proposed for scheduling task in cloud environment. SA based approach produced comparative result in a minimal execution time.

Highlights

This problem some kind of branch and bound and other approximation method has been developed, the
The simulation results proved that the Simulated Annealing (SA) approach will give good result with minimal execution time
We will compare the performance between Mapping, Genetic Algorithm (GA) and SA approaches with these types of parameters

Summary

Introduction

This problem some kind of branch and bound and other approximation method has been developed, the. Users can submit their job into cloud for computing may be too complex, depending on the computational processing or leave their data in cloud for business orientation of cloud environment, which is storage. It impossible to find the globally optimal solution by in order to realize the full potential of the using simple algorithms or rules. It is well known as NP- cloud platform, an architectural framework for efficiently complete problem.

Methods

Results

Discussion

Conclusion