Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing
Anant Kumar Jayswal1, Prem Chand Saxena2
1Anant Kumar Jayswal, SC & SS, Jawaharlal Nehru University, New Delhi, India.
2Prof. Prem Chand Saxena, SC & SS, Jawaharlal Nehru University, New Delhi, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on February 10, 2020. | PP: 45-51 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8836019320/2020©BEIESP | DOI: 10.35940/ijitee.C8836.029420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Cloud Computing, load Balancing, Multi-Agent Genetic Algorithm, Virtual Machine.
Scope of the Article: Cloud Computing