Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

Authors

  • Kenga Mosoti Derdus  Faculty of Information Technology, Strathmore University, Nairobi, Kenya
  • Vincent Oteke Omwenga  Faculty of Information Technology, Strathmore University, Nairobi, Kenya
  • Patrick Job Ogao  Faculty of Engineering Science and Technology, Technical University of Kenya, Nairobi, Kenya

DOI:

https://doi.org//10.32628/CSEIT1952309

Keywords:

Cloud computing, Virtualization, VM allocation algorithm, Energy efficiency, IaaS cloud.

Abstract

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

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

How to Cite

[1]
Kenga Mosoti Derdus, Vincent Oteke Omwenga, Patrick Job Ogao, " Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.1123-1136, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952309