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A Multi-Objective Load Balancing System for Cloud Environments
Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO, Computer Networking)ORCID iD: 0000-0001-9194-010X
School of Information Technologies, University of Sydney, Australia.
2017 (English)In: Computer journal, ISSN 0010-4620, E-ISSN 1460-2067, Vol. 60, no 9, p. 1316-1337Article in journal (Refereed) Published
Abstract [en]

Virtual machine (VM) live migration has been applied to system load balancing in cloud environments for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migration process is both time- and cost-consuming as it requires the transfer of large size files or memory pages and consumes a huge amount of power and memory for the origin and destination physical machine (PM), especially for storage VM migration. This process also leads to VM downtime or slowdown. To deal with these shortcomings, we develop a Multi-objective Load Balancing (MO-LB) system that avoids VM migration and achieves system load balancing by transferring extra workload from a set of VMs allocated on an overloaded PM to other compatible VMs in the cluster with greater capacity. To reduce the time factor even more and optimize load balancing over a cloud cluster, MO-LB contains a CPU Usage Prediction (CUP) sub-system. The CUP not only predicts the performance of the VMs but also determines a set of appropriate VMs with the potential to execute the extra workload imposed on the VMs of an overloaded PM. We also design a Multi-Objective Task Scheduling optimization model using Particle Swarm Optimization to migrate the extra workload to the compatible VMs. The proposed method is evaluated using a VMware-vSphere-based private cloud in contrast to the VM migration technique applied by vMotion. The evaluation results show that the MO-LB system dramatically increases VM performance while reducing service response time, memory usage, job makespan, power consumption and the time taken for the load balancing process.

Place, publisher, year, edition, pages
UK: Oxford University Press, 2017. Vol. 60, no 9, p. 1316-1337
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-66134DOI: 10.1093/comjnl/bxw109ISI: 000410267000005OAI: oai:DiVA.org:kau-66134DiVA, id: diva2:1180701
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-06-25Bibliographically approved

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  • apa
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