Eeadoselfcloud: Energy Efficient Adaptive Depth Optimized Self Cloud Mechanism for VM Migration in Data Centers
Sebagenzi Jason1, Suchithra R.2
1Mr.Jason Sebagenzi, research scholar in Jain University, Bangalore.
2R.Suchithra, director of MCA department in Jain Deemed to be University, Bangalore.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 475-482 | Volume-8 Issue-6, August 2019. | Retrieval Number: E7864068519/2019©BEIESP | DOI: 10.35940/ijeat.E7864.088619
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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: Cloud computing is a paradigm where all resources like software, hardware and information are accessed over internet by using highly sophisticated virtual data centres. The cloud has a data center with a host of many features. Each machine is shared by many users, and virtual machines are used to use these machines. With a large number of data centers and data centers with a large number of physical hosts. Two important issues in cloud environment are Load balancing and power consumption which solved by virtual machine migration. In earlier learnings, Artificial Bee Colony (ABC)’s policy could lead to a compromise between productivity and energy consumption. There are, however, two ways in the ABC-based Abstract based approach: (1) How to find effective solutions across the globe. (2) how to reduce the time to decide to distribute BM. To overcome this issue, this project develop one novel VM migration scheme called eado Self Cloud. This proposed method introduces Bee Lion Optimization (BLO) for VM allocation. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are employed as parameters for the proposed algorithm analysis. The experimental results indicate that the proposed algorithm does better than the other available methods.
Keywords: Cloud Computing; Data centre; VM Placement; VM Migration; Energy Efficient Self Organization Cloud; Optimization.