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Issue No. 12 - Dec. (2015 vol. 27)
ISSN: 1041-4347
pp: 3332-3346
Shen Gao , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Jianliang Xu , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Theo Harder , Department of Computer Science, University of Kaiserslautern, Germany
Bingsheng He , , School of Computer Engineering, Nanyang Technological University, Singapore
Byron Choi , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Haibo Hu , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
ABSTRACT
Phase-change memory (PCM), as one of the most promising next-generation memory technologies, offers various attractive properties such as non-volatility, byte addressability, bit alterability, and low idle energy consumption. Recently, PCM has drawn much attention from the database community for optimizing query and transaction performance. As a complement to existing work, we present PCMLogging, a novel logging scheme that exploits PCM for both data caching and transaction logging to minimize I/O accesses in disk-based databases. Specifically, PCMLogging caches dirty pages/records in PCM and further maintains an implicit log in the cached updates to support database recovery. By integrating log and cached updates, PCMLogging enables simplified recovery and prolongs PCM lifetime. Furthermore, using PCMLogging, we develop a wear-leveling algorithm, that evenly distributes the write traffic across the PCM storage space, and a cost-based destaging algorithm that adaptively migrates cached data from PCM to external storage. Compared to classical write-ahead logging (WAL), our trace-driven simulation results reveal up to 1 $_$\sim$_$ 20X improvement in system throughput.
INDEX TERMS
Phase change materials, Random access memory, Nonvolatile memory, Ash, Concurrency control, Database systems
CITATION

S. Gao, J. Xu, T. Harder, B. He, B. Choi and H. Hu, "PCMLogging: Optimizing Transaction Logging and Recovery Performance with PCM," in IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 12, pp. 3332-3346, 2015.
doi:10.1109/TKDE.2015.2453154
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