Virtualization is one of the cornerstone technologies that makes utility computing platforms such as cloud computing a reality. With the accelerating adoption of cloud computing, the virtualizaion-based cloud platforms are consuming a significant amount of energy. However, the design of a green and efficient virtualization technology remains an open issue to both industry and academia. In this thesis, we for the first time investigate the virtual machine's (VM's) power consumption while supporting different services and applications (e.g., web, database and streaming). In particular, we establish a cloud computing platform in the The University of Minnesota Duluth. This platform consist of both Xen and KVM nodes and the VMs can be easily accessed from the Internet. Our real-world measurement indicates that the existing virtulization technologies add considerable energy overhead to the data centers. For example, a busy virtualized database server can consume 30\% more energy than its non-virtualized counterparts. To address such a problem, we propose a shared-memory-based enhancement to reduces the extra interrupts and memory copies for cloud virtualization. The evaluation indicates that our approach can reduce VM's energy consumption by 11% without noticeable loss of its running performance.
University of Minnesota M.S. thesis. July 2016. Major: Computer Science. Advisor: Haiyang Wang. 1 computer file (PDF); viii, 44 pages.
Power Consumption of Virtual Machines in Cloud Computing: Measurement and Enhancement.
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