Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

R GowriPrakash; R Shankar; S Duraisamy

doi:10.17485/IJST/v14i7.1901

Article

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i7.1901

Year: 2021, Volume: 14, Issue: 7, Pages: 636-651

Original Article

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

R GowriPrakash^1*, R Shankar², S Duraisamy²

¹Ph.D, Research Scholar, Department of Computer Science, Chikkanna Government Arts College, Tirupur, Tamilnadu, India. Tel.: +918148188030
²Assistant Professor, Department of Computer Science, Chikkanna Government Arts
College, Tirupur, Tamilnadu, India

*Corresponding Author
Tel: +918148188030
Email: [email protected]

Received Date:19 October 2020, Accepted Date:23 February 2021, Published Date:03 March 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background: The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers. Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective. Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs. During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the highest traffic on the path is applied as a congestion factor. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths. Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR).

Keywords: Cloud computing; load balancing; VM migration; OH-BAC-FUP; multipath traffic routing

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Copyright

© 2021 GowriPrakash et al..This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)