Heuristic-based load-balancing algorithm for IaaS cloud

The tremendous growth of virtualization technology in cloud environment reflects the increasing number of tasks that require the services of the virtual machines (VMs). To balance the load among the VMs and minimizing the makespan of the tasks are the challenging research issues. Many algorithms hav...

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Bibliographic Details
Published in:Future generation computer systems Vol. 81; pp. 156 - 165
Main Authors: Adhikari, Mainak, Amgoth, Tarachand
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2018
Subjects:
IaaS cloud
Load balancing
Makespan
Resource utilization
Server configuration
Virtual machines
IaaS cloud
Resource utilization
Virtual machines
Makespan
Server configuration
Load balancing
ISSN:0167-739X, 1872-7115
Online Access:Get full text
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Summary:The tremendous growth of virtualization technology in cloud environment reflects the increasing number of tasks that require the services of the virtual machines (VMs). To balance the load among the VMs and minimizing the makespan of the tasks are the challenging research issues. Many algorithms have been proposed to solve the said problem. However, they lack in finding the potential information about the resources and tasks and it may lead to the improper assignment of the tasks to the VMs. In this paper, we propose a new load balancing algorithm for Infrastructure as a Service (IaaS) cloud. We devise an efficient strategy to configure the servers based on the number of incoming tasks and their sizes to find suitable VMs for assignment and maximize the utilization of computing resource. We test the proposed algorithm through simulation runs and compare the simulation results with the existing algorithms using various performance metrics. Through comparisons, we demonstrate that the proposed algorithm performs better than the existing ones. •The objective of HBLBA is to minimize the makespan and utilize the resource efficiently.•We devise a mechanism to configure servers and it helps in utilizing resources efficiently.•An intelligent decision strategy for task assignment.•Finally, a comprehensive validation via simulation runs using various performance metrics.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2017.10.035