TY - JOUR
T1 - PCMLogging
T2 - Optimizing Transaction Logging and Recovery Performance with PCM
AU - Gao, Shen
AU - Xu, Jianliang
AU - Härder, Theo
AU - He, Bingsheng
AU - Choi, Byron
AU - Hu, Haibo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - 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 ∼ 20X improvement in system throughput.
AB - 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 ∼ 20X improvement in system throughput.
KW - caching
KW - database recovery
KW - performance
KW - Phase-change memory
UR - http://www.scopus.com/inward/record.url?scp=84959544388&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2015.2453154
DO - 10.1109/TKDE.2015.2453154
M3 - Journal article
AN - SCOPUS:84959544388
SN - 1041-4347
VL - 27
SP - 3332
EP - 3346
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 12
M1 - 7150536
ER -