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Issue No.08 - Aug. (2013 vol.24)
pp: 1695-1705
Yefu Wang , University of Tennessee, Knoxville
Xiaorui Wang , The Ohio State University, Columbus
Many power management strategies have been proposed for enterprise servers based on dynamic voltage and frequency scaling (DVFS), but those solutions cannot further reduce the energy consumption of a server when the server processor is already at the lowest DVFS level and the server utilization is still low (e.g., 10 percent or lower). To achieve improved energy efficiency, request batching can be conducted to group received requests into batches and put the processor into sleep between the batches. However, it is challenging to perform request batching on a virtualized server because different virtual machines on the same server may have different workload intensities. Hence, putting the shared processor into sleep may severely impact the application performance of all the virtual machines. This paper proposes Virtual Batching, a novel request batching solution for virtualized servers with primarily light workloads. Our solution dynamically allocates CPU resources such that all the virtual machines can have approximately the same performance level relative to their allowed peak values. Based on this uniform level, Virtual Batching determines the time length for periodically batching incoming requests and putting the processor into sleep. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase processor frequency for performance guarantees. Virtual Batching is also extended to integrate with server consolidation for maximized energy conservation with performance guarantees for virtualized data centers. Empirical results based on a hardware testbed and real trace files show that Virtual Batching can achieve the desired performance with more energy conservation than several well-designed baselines, e.g., 63 percent more, on average, than a solution based on DVFS only.
Servers, Time factors, Virtual machining, Memory management, Energy consumption, Switches, Energy conservation, data centers, Servers, Time factors, Virtual machining, Memory management, Energy consumption, Switches, Energy conservation, control theory, Energy management, virtual machines, request batching, servers
Yefu Wang, Xiaorui Wang, "Virtual Batching: Request Batching for Server Energy Conservation in Virtualized Data Centers", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 8, pp. 1695-1705, Aug. 2013, doi:10.1109/TPDS.2012.237
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