ABSTRACT
In recent years, the scale of datacenters has become larger due to the explosive increase in the amount of digital data. As a result, the growth of energy consumption is an important factor in the management use cost of datacenters. Storing and processing such large volumes of data by database applications are the core technologies in this Big Data era. However, storage accounts for a significant percentage of a datacenter's energy consumption. Therefore, we try to reduce the energy of storage to save on the total cost of datacenters. The purpose of this study is to reduce the energy consumption of storage while minimizing the deterioration of application performance. Although many methods for storage energy saving have been discussed, since it is difficult to control it efficiently only at the storage level, we have investigated the storage power control mechanism on middleware (database) layer. In this paper, we use TPC-H (a database benchmark) as an application example of data processing. We evaluate a data placement control method of storage proposed for energy saving in the database run-time processing suitable for a large-scale environment with many HDDs.
- GIPC Survey and Estimation Committee Report FY2009 (Summary), http://www.greenit-pc.jp/activity/reporting/100707/index.html, 2009Google Scholar
- TPC-H: http://www.tpc.org/tpch/default.aspGoogle Scholar
- Jorge Guerra, Himabindu Pucha, Joseph Glider, Wendy Belluomini, and Raju Rangaswami: Cost Effective Storage using Extent Based Dynamic Tiering, In Proc. 9th USENIX Conference on File and Storage Technologies, pp. 1--14, 2011. Google ScholarDigital Library
- Dushyanth Narayanan, Austin Donnelly, and Antony Rowstron: Write Off-Loading: Practical Power Management for Enterprise Storage, In Proc. 6th USENIX Conference on File and Storage Technologies, pp. 253--267, 2008. Google ScholarDigital Library
- Athanasios E Papathanasiou and Michael L Scott: Energy Efficient Prefetching and Caching, In Proc. the annual conference on USENIX Annual Technical Conference, 2004. Google ScholarDigital Library
- Akshat Verma, Ricardo Koller, Luis Useche, and Raju Rangaswami: SRCMap: Energy Proportional Storage using Dynamic Consolidation, In Proc. 8th USENIX Conference on File and Storage Technologies, 2010. Google ScholarDigital Library
- Charles Weddle, Mathew Oldham, Jin Qian, An-I Andy Wang, Peter Reiher, and Geo Kuenning: PARAID: A Gear-Shifting Power-Aware RAID, In Proc. 5th USENIX Conference on File and Storage Technologies, Vol. 3, pp. 245--260, October 2007. Google ScholarDigital Library
- Norifumi Nishikawa, Miyuki Nakano, and Masaru Kitsuregawa: Runtime Disk Energy Saving Method Using Application I/O Behavior and Its Evaluation: Energy Saving Efficiency for Online Transaction Processing, The IEICE transactions on information and systems, Vol. J95-D, No. 3, pp. 447--459, March 2012.Google Scholar
- Norifumi Nishikawa, Miyuki Nakano, and Masaru Kitsuregawa: Energy Efficient Storage Management Cooperated with Large Data Intensive Applications, In Proc. 28th IEEE International Conference on Data Engineering (IEEE ICDE 2012), pp. 126--137, April 2012. Google ScholarDigital Library
- Naho Iimura, Norifumi Nishikawa, Miyuki Nakano, and Masato Oguchi: A Proposal of Storage Control Method for Energy Saving on Runtime Database Processing, In Proc. Multimedia, Distributed, Cooperative, and Mobile Symposium 2013, pp. 1646--1652, 7C-1, July 2013.Google Scholar
- Jian Ouyang, Shiding Lin, Zhenyu Hou, Peng Wang, Yong Wang, and Guangyu Sun. Active SSD design for energy-efficiency improvement of web-scale data analysis, IEEE International Symposium on Low Power Electronics and Design (ISLPED 2013), pp. 286--291, September 2013. Google ScholarDigital Library
- Peng Li, Gomez, K., Lilja, D. J. Exploiting free silicon for energy-efficient computing directly in NAND ash-based solid-state storage systems, IEEE High Performance Extreme Computing Conference (HPEC 2013), pp. 1--6, September 2013.Google ScholarCross Ref
- Devesh Tiwari, Sudharshan S. Vazhkudai, Youngjae Kim, Xiaosong Ma, Simona Boboila, and Peter J. Desnoyers. Reducing Data Movement Costs Using Energy-Efficient, Active Computation on SSD, USENIX Workshop on Power-Aware Computing and Systems (HotPower '12), October, 2012. Google ScholarDigital Library
- Alan D. Brunelle: btrecord and btreplay User Guide, http://www.cse.unsw.edu.au/aaronc/iosched/doc/btreplay.html, 2007.Google Scholar
- Y. H. Lu, G. D. Micheli: Comparing System-Level Power Management Policies, IEEE Design & Test of Computers, Vol. 18, No. 2, pp. 10--19, March 2001. Google ScholarDigital Library
- pdbus: http://www.hitachi.co.jp/Prod/comp/soft1/manual/pc/d635540/W3550027.HTMGoogle Scholar
- smartd.conf: http://smartmontools.sourceforge.net/man/smartd.conf.5.htmlGoogle Scholar
Index Terms
- A proposal of storage power control method with data placement in an environment using many HDDs
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