Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/354095
Title: Enhanced Multi Objective Based Virtual Machine Optimization for Demand Management in Cloud Environment
Researcher: Krishnakumar K
Guide(s): Malarvizhi N
Keywords: Computer Science
Computer Science Artificial Intelligence
Engineering and Technology
University: Meenakshi Academy of Higher Education and Research
Completed Date: 2021
Abstract: ABSTRACT newlineCloud computing enables the use of virtual machine resources and reduces operating cost. Whereas the selection of virtual machine still remains a challenge due to no proper resource utilization and lack of optimal resource allocation. The services and storage space required for a particular resource is specified by the user. Hence the significant challenge exists in cloud computing is the variant level of performance in concern to utilization, throughput, stability, etc. The proposed research quotEnhanced Multi-objective-based Virtual Machine Optimization For Demand Management in Cloud Environmentquot consists of three phases: i) Classification Phase, ii) Scheduling Phase, and iii) Optimization Phase. The first phase, quotEfficient User Classification Framework for Multi-objective-based Virtual Machines,quot efficiently handles multiple user requests by classifying them into valid and invalid categories. The second phase of the proposed research work Enhanced Multi-objective-based Virtual Machine Optimization For Demand Management in Cloud Environment. BCCOA is proposed for dynamic resource allocation in this venture. Based on this, it proves that the execution cost and respone time of the proposed method is low. Finally, the third phase Enhanced Optimization For Multi-Objective-Based Virtual Machine Model In Cloud monitors and optimizes the cloud resource allocation, enhancing the efficiency of the cloud environment. The framework utilizes the novel Optimized Mayfly Tanhoptimization- Virtual Machine Scheduling Algorithm. The average memory utilization (0.84) and average CPU utilization (0.93) are better than the other two existing approaches FCFS and SLPSO. Thus improving the cloud efficiency by dynamically adjusting VM scheduling showing exceptionally high utilization newline
Pagination: xvii 88
URI: http://hdl.handle.net/10603/354095
Appears in Departments:Department of Engineering

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02_certificate.pdf195.07 kBAdobe PDFView/Open
03_declaration.pdf226.17 kBAdobe PDFView/Open
04_chapter 1.pdf694.07 kBAdobe PDFView/Open
05_chapter 2.pdf146.55 kBAdobe PDFView/Open
06_chapter 3.pdf345.92 kBAdobe PDFView/Open
07_chapter 4.pdf433.22 kBAdobe PDFView/Open
08_chapter 5.pdf491.13 kBAdobe PDFView/Open
09_chapter 6.pdf97.89 kBAdobe PDFView/Open
10_bibliography.pdf183.46 kBAdobe PDFView/Open
11_annexure.pdf51.64 kBAdobe PDFView/Open
12_contents.pdf34.5 kBAdobe PDFView/Open
13_list of table and figures.pdf423.66 kBAdobe PDFView/Open
80_recommendation.pdf168.9 kBAdobe PDFView/Open
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