Genetic Algorithm in Load Balancing problem of Cloud Computing

Surjeet Dalal

ISBN 10: 613985394X / ISBN 13: 9786139853946

Published by LAP LAMBERT Academic Publishing Jun 2018, 2018

About this Item

Neuware -Cloud computing technology has gained immense popularity in the recent years. It is the new word that describes an internet based computing technology which enables the users to access information and use various resources from the clouds from any location. This technology is evolving and developing with the increasing demands in the IT Sector and business environments. In this book the issue of load balancing in cloud environment is taken. A load balancing algorithm is developed using the genetic algorithm concept which is based on the evolutionary mechanism. On the basis of two parameters, response time and overall cost, a genetic algorithm is developed to balance the workload efficiently in cloud computing environment. The results illustrated the improvement in the response time and overall cost of the cloud systemBooks on Demand GmbH, Überseering 33, 22297 Hamburg 88 pp. Englisch. Seller Inventory # 9786139853946

Bibliographic Details

Title: Genetic Algorithm in Load Balancing problem ...

Publisher: LAP LAMBERT Academic Publishing Jun 2018

Publication Date: 2018

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

Cloud computing technology has gained immense popularity in the recent years. It is the new word that describes an internet based computing technology which enables the users to access information and use various resources from the clouds from any location. This technology is evolving and developing with the increasing demands in the IT Sector and business environments. In this book the issue of load balancing in cloud environment is taken. A load balancing algorithm is developed using the genetic algorithm concept which is based on the evolutionary mechanism. On the basis of two parameters, response time and overall cost, a genetic algorithm is developed to balance the workload efficiently in cloud computing environment. The results illustrated the improvement in the response time and overall cost of the cloud system