Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing

Cloud Computing systems are widely applied in many fields such as communication data management, web application, network monitoring, financial management and so on. The distributed Cloud Computing technology has been produced as the development of the computer network and distributed computing tech...

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Main Author:	Elrasheed Ismail, Sultan
Format:	Thesis
Published:	2013
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/7286/ http://umpir.ump.edu.my/id/eprint/7286/

id	ump-7286
recordtype	eprints
spelling	ump-72862015-02-24T06:10:38Z http://umpir.ump.edu.my/id/eprint/7286/ Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing Elrasheed Ismail, Sultan QA76 Computer software Cloud Computing systems are widely applied in many fields such as communication data management, web application, network monitoring, financial management and so on. The distributed Cloud Computing technology has been produced as the development of the computer network and distributed computing technology. Researches on data Cloud Computing become the necessary trend in the distributed Cloud Computing system domain since the sources and application of the data are distributed and the scale of the applications enlarges quickly. Load management is the focus of research in both of the area in distributed Cloud Computing systems and centralized Cloud Computing systems. Although researches on the load management in the cloud systems is similar to that of traditional parallel and distributed systems in many aspects, essential differences exist between them. The choice of a scheduling strategy has significant impact on the runtime Central Processing Unit, memory consumption as well as the storage systems. Load balancing optimization techniques such as Ant Colony Optimization (ACO), First Come First Served (FCFS), Round Robin (RR) and Particle Swarm Optimization (PSO) are popular techniques being used for scheduling and load balancing. However, these techniques have its weaknesses in terms of minimizing makespan, computation cost and communication cost. In this study, load balancing technique in Cloud Computing called Quantum Particle Swarm Optimization (QPSO) technique proposed by considering only minimization of makespan, computation cost and communication cost. Performance of the QPSO technique based on many heuristic algorithms it is comprised the following steps. Firstly, tasks are assigned averagely to the machines according to a special initialization policy. Then the optimal criterion for exchanging tasks between two machines is proposed and exploited to speed up the improving process towards load balance. Secondly, this thesis proposes job-combination based static algorithm for load balancing where all jobs should organized into the standard job combinations, each task of which consists of one to four jobs. Then they are assigned to the machines according to the assignment algorithm for job combinations, which is a special integer partition algorithm. Finally, the result of experiment shows that QPSO can achieve at least three times cost saving as compared with ACO, FCFS, RR and PSO. 2013-08 Thesis NonPeerReviewed Elrasheed Ismail, Sultan (2013) Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing. PhD thesis, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:78839&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
topic	QA76 Computer software
spellingShingle	QA76 Computer software Elrasheed Ismail, Sultan Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
description	Cloud Computing systems are widely applied in many fields such as communication data management, web application, network monitoring, financial management and so on. The distributed Cloud Computing technology has been produced as the development of the computer network and distributed computing technology. Researches on data Cloud Computing become the necessary trend in the distributed Cloud Computing system domain since the sources and application of the data are distributed and the scale of the applications enlarges quickly. Load management is the focus of research in both of the area in distributed Cloud Computing systems and centralized Cloud Computing systems. Although researches on the load management in the cloud systems is similar to that of traditional parallel and distributed systems in many aspects, essential differences exist between them. The choice of a scheduling strategy has significant impact on the runtime Central Processing Unit, memory consumption as well as the storage systems. Load balancing optimization techniques such as Ant Colony Optimization (ACO), First Come First Served (FCFS), Round Robin (RR) and Particle Swarm Optimization (PSO) are popular techniques being used for scheduling and load balancing. However, these techniques have its weaknesses in terms of minimizing makespan, computation cost and communication cost. In this study, load balancing technique in Cloud Computing called Quantum Particle Swarm Optimization (QPSO) technique proposed by considering only minimization of makespan, computation cost and communication cost. Performance of the QPSO technique based on many heuristic algorithms it is comprised the following steps. Firstly, tasks are assigned averagely to the machines according to a special initialization policy. Then the optimal criterion for exchanging tasks between two machines is proposed and exploited to speed up the improving process towards load balance. Secondly, this thesis proposes job-combination based static algorithm for load balancing where all jobs should organized into the standard job combinations, each task of which consists of one to four jobs. Then they are assigned to the machines according to the assignment algorithm for job combinations, which is a special integer partition algorithm. Finally, the result of experiment shows that QPSO can achieve at least three times cost saving as compared with ACO, FCFS, RR and PSO.
format	Thesis
author	Elrasheed Ismail, Sultan
author_facet	Elrasheed Ismail, Sultan
author_sort	Elrasheed Ismail, Sultan
title	Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
title_short	Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
title_full	Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
title_fullStr	Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
title_full_unstemmed	Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
title_sort	quantum particle swarm optimization technique for load balancing in cloud computing
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/7286/ http://umpir.ump.edu.my/id/eprint/7286/
first_indexed	2023-09-18T22:03:52Z
last_indexed	2023-09-18T22:03:52Z
_version_	1777414578354782208

Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing

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