Title: A weighted throttled load balancing approach for virtual machines in cloud environment

Authors: Walugembe Hussein; Tao Peng; Guojun Wang

Addresses: School of Information Science and Engineering, Central South University, Changsha, Hunan Province, 410083, China ' School of Information Science and Engineering, Central South University, Changsha, Hunan Province, 410083, China ' School of Information Science and Engineering, Central South University, Changsha, Hunan Province, 410083, China

Abstract: In a cloud environment, load balancing techniques play a vital role in reducing costs associated with file management systems and maximise availability of resources by reducing the amount of downtime that affects the system during outages. Virtualisation technology provides an effective solution to the management of dynamic web-based resources and this prompted us to propose a virtual machine load balancing algorithm. In this paper, we propose a weighted throttled load balancing algorithm, a modification of throttled load balancing algorithm by assigning a weight to each virtual machine (VM). For throttled load balancing algorithm, virtual machines (VMs) are either busy or available and if there is no VM available to serve a request, the request is queued. In our proposed algorithm a request is not queued instead it is assigned a busy VM that has a higher weight in comparison to other VMs. The proposed approach improves system availability through minimising latency, and reduces processing time through proper allocation of requests to the most powerful VM available. The algorithm is implemented using CloudSim simulator and the experimental results achieve better response time and processing time.

Keywords: weighted throttled load balancing; virtual machines; CloudSim; cloud computing; latency; processing time; simulation.

DOI: 10.1504/IJCSE.2015.073499

International Journal of Computational Science and Engineering, 2015 Vol.11 No.4, pp.402 - 408

Received: 30 Sep 2013
Accepted: 28 Oct 2013

Published online: 10 Dec 2015 *

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