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2019•International Journal of Recent Technology and Engineering (IJRTE)Open access

Unified Provisioning Framework for Dynamic Virtual Machine Placement Optimization in Cloud Data Center

Darshan Shah, M. Vinayakmurthi, Dr. Anand Kumar

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Abstract

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement.

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What this paper is about

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement.

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Available abstract

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement.

Key concepts: Data center, Virtual machine, Cloud computing, Provisioning, Computer science, Benchmark (surveying), Distributed computing, Virtualization

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