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Virtual Machine Allocation in Cloud Computing Environment

Virtual Machine Allocation in Cloud Computing Environment

Absalom E. Ezugwu, Seyed M. Buhari, Sahalu B. Junaidu
Copyright: © 2013 |Volume: 3 |Issue: 2 |Pages: 14
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781466632219|DOI: 10.4018/ijcac.2013040105
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MLA

Ezugwu, Absalom E., et al. "Virtual Machine Allocation in Cloud Computing Environment." IJCAC vol.3, no.2 2013: pp.47-60. http://doi.org/10.4018/ijcac.2013040105

APA

Ezugwu, A. E., Buhari, S. M., & Junaidu, S. B. (2013). Virtual Machine Allocation in Cloud Computing Environment. International Journal of Cloud Applications and Computing (IJCAC), 3(2), 47-60. http://doi.org/10.4018/ijcac.2013040105

Chicago

Ezugwu, Absalom E., Seyed M. Buhari, and Sahalu B. Junaidu. "Virtual Machine Allocation in Cloud Computing Environment," International Journal of Cloud Applications and Computing (IJCAC) 3, no.2: 47-60. http://doi.org/10.4018/ijcac.2013040105

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Abstract

Virtual machine allocation problem is one of the challenges in cloud computing environments, especially for the private cloud design. In this environment, each virtual machine is mapped unto the physical host in accordance with the available resource on the host machine. Specifically, quantifying the performance of scheduling and allocation policy on a Cloud infrastructure for different application and service models under varying performance metrics and system requirement is an extremely challenging and difficult problem to resolve. In this paper, the authors present a Virtual Computing Laboratory framework model using the concept of private cloud by extending the open source IaaS solution Eucalyptus. A rule based mapping algorithm for Virtual Machines (VMs) which is formulated based on the principles of set theoretic is also presented. The algorithmic design is projected towards being able to automatically adapt the mapping between VMs and physical hosts’ resources. The paper, similarly presents a theoretical study and derivations of some performance evaluation metrics for the chosen mapping policies, these includes determining the context switching, waiting time, turnaround time, and response time for the proposed mapping algorithm.

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