Energy efficient and reliable storage disks
Proceedings of the 46th Annual Southeast Regional Conference on XX
ViPZonE: OS-level memory variability-driven physical address zoning for energy savings
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Experimental Study on the Energy Consumption in IaaS Cloud Environments
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Power consumption is an important issue for cluster supercomputers as it directly affects their running cost and cooling requirements. This paper investigates the memory energy efficiency of high-end data servers used for supercomputers. Emerging memory technologies allow memory devices to dynamically adjust their power states. To achieve maximum energy saving, the memory management on data servers needs to judiciously utilize these energy-aware devices. As we explore different management schemes under four real-world parallel I/O workloads, we find that the memory energy consumption is determined by a complex interaction among four important factors: (1) cache hit rates that may directly translate performance gain into energy saving, (2) cache populating schemes that perform buffer allocation and affect access locality at the chip level, (3) request clustering that aims to temporally align memory transfers from different buses into the same memory chips, and (4) access patterns in workloads that affect the first three factors.