Unified Provisioning Framework for Dynamic Virtual Machine Placement Optimization in Cloud Data Center
Darshan Shah1, M. Vinayakmurthi2, Anand Kumar3
1Darshan Shah, Department of Computer Science, Reva University, Bangalore, India.
2Dr. M. Vinayakmurthi, Department of Computer Science, Reva University, Bangalore, India.
3Dr. Anand Kumar, M.S. Engineering College, Bangalore, India.
Manuscript received on 03 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 6464-6468 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2208078219/2019©BEIESP | DOI: 10.35940/ijrte.B2208.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Virtual Machine Placement, Cloud Data Center, Multi-Dimensional Resource Optimization.
Scope of the Article: Design Optimization of Structures