Open AccessMulti-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing
Author(s) -
Anant Kumar Jayswal*,
Prof. Prem Chand Saxena
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8836.029420
Subject(s) - load balancing (electrical power) , cloud computing , computer science , virtual machine , distributed computing , genetic algorithm , computation , key (lock) , task (project management) , algorithm , load distribution , operating system , mathematics , engineering , geometry , systems engineering , machine learning , grid , structural engineering
Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.