Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS Provision

Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia... Load balancing is a method of workload distribution across various computers or instruction data centres for maximizing throughput and minimizing work load on resources. To perform load balancing techniques in cloud computing environments, various challenges such as data security, and proper distribution exist which requires serious attention. The most important challenge posed by cloud applicationsis the provision of Quality of Service (QoS) provision as it develops the problem of resource allocation to the application so as to guarantee a service level along dimensions such as performance, availability and reliability. A centralized hierarchical Cloud-based Multimedia System (CMS) consisting of a resource manager, cluster heads, and server clusters is being considered by which the resource manager assigns clients’ requests to server clusters for performing multimedia service tasks based on the job features after which each the job is assigned to the servers within its server cluster by the cluster head. Designing an effective load balancing algorithm for CMS however being a complicated and challenging task, enables spreading of multimedia service job load on servers at the minimal cost for transmitting multimedia data between server clusters and clients without exceeding the maximal load limit of each server cluster. In the present work, the Multiple Kernel Learning with Support Vector Machine (MKL-SVM) approach is proposed to quantify the disturbance in the utilization of multiple resources on a resource manager at client side and then verifying at the server side in the each cluster. Also, Fuzzy Simple Additive Weighting (FSAW) method is introduced for QoS provision for improving the system performance. The proposed model CMSdynMLB serves as the multiservice load balancing while considering the integer linear programming problem having unevenness measurement. In order to solve the problem of dynamic load balancing, Hybrid Particle Swarm Optimization (HSPO) is proposed as it holds well for dynamic problems. From the simulation results, it is determined that proposed MKL-SVM algorithm can efficiently manage the dynamic multiservice load balancing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mobile Networks and Applications Springer Journals

Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS Provision

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Engineering; Communications Engineering, Networks; Computer Communication Networks; Electrical Engineering; IT in Business
ISSN
1383-469X
eISSN
1572-8153
D.O.I.
10.1007/s11036-017-0840-y
Publisher site
See Article on Publisher Site

Abstract

Load balancing is a method of workload distribution across various computers or instruction data centres for maximizing throughput and minimizing work load on resources. To perform load balancing techniques in cloud computing environments, various challenges such as data security, and proper distribution exist which requires serious attention. The most important challenge posed by cloud applicationsis the provision of Quality of Service (QoS) provision as it develops the problem of resource allocation to the application so as to guarantee a service level along dimensions such as performance, availability and reliability. A centralized hierarchical Cloud-based Multimedia System (CMS) consisting of a resource manager, cluster heads, and server clusters is being considered by which the resource manager assigns clients’ requests to server clusters for performing multimedia service tasks based on the job features after which each the job is assigned to the servers within its server cluster by the cluster head. Designing an effective load balancing algorithm for CMS however being a complicated and challenging task, enables spreading of multimedia service job load on servers at the minimal cost for transmitting multimedia data between server clusters and clients without exceeding the maximal load limit of each server cluster. In the present work, the Multiple Kernel Learning with Support Vector Machine (MKL-SVM) approach is proposed to quantify the disturbance in the utilization of multiple resources on a resource manager at client side and then verifying at the server side in the each cluster. Also, Fuzzy Simple Additive Weighting (FSAW) method is introduced for QoS provision for improving the system performance. The proposed model CMSdynMLB serves as the multiservice load balancing while considering the integer linear programming problem having unevenness measurement. In order to solve the problem of dynamic load balancing, Hybrid Particle Swarm Optimization (HSPO) is proposed as it holds well for dynamic problems. From the simulation results, it is determined that proposed MKL-SVM algorithm can efficiently manage the dynamic multiservice load balancing.

Journal

Mobile Networks and ApplicationsSpringer Journals

Published: Apr 7, 2017

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