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URN etd-0719110-142216 Statistics This thesis had been viewed 3362 times. Download 3738 times. Author Chih-kai Hu Author's Email Address No Public. Department Computer Science and Enginerring Year 2009 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Page Count 66 Title DYNAMIC ADJUSTMENT MECHANISM OF THE VIRTUAL MACHINE COMPUTING RESOURCE IN THE CLOUD COMPUTING Keyword cloud computing Virtualization Virtual machines dynamic resource adjustment dynamic resource adjustment Virtual machines Virtualization cloud computing Abstract IaaS (Infrastructure as a Service) architecture in the cloud computing is to provide infrastructure, CPU, memory, and network, etc., for the service. The virtualization is to package a file type of infrastructure, provision of computing resources. This is usually a virtual environment platform, such as KVM, XEN, and VMware. In such a platform, based on the application of different computing resource requirements, dynamic resource adjustment in part, there are still many research issues to be resolved. For example: automated dynamic resource adjustment mechanism of calculating the allocation of resources.
In this thesis, a dynamic adjustment mechanism of the virtual machine computing resources, called DAVMCR, in the cloud computing environment has been proposed. It is a dynamic adjustment technique constructed under the KVM (Kernel-based Virtual Machine) virtualization architecture. By applying the proposed mechanism, when the loading of a virtual machine is heavy, the number of CPUs in the virtual machine will be increased automatically with non-stop service to increase the system performance. Once the system loading is no longer heavy, the augmented CPUs will be released immediately to increase the overall system resource utilization. Experimental results show that by applying the proposed mechanism in a heavy loaded virtual machine, the computing performance of the virtual machine, can be improved effectively and the response time of connection requests can also be reduced significantly.
Advisor Committee Liang-The Lee - advisor
Files Date of Defense 2010-07-09 Date of Submission 2010-07-19