@inproceedings{1c02df2231f84f678bbdfe20b26fd7c2,
title = "Plover: Parallel In-memory database logging on scalable storage devices",
abstract = "Despite the prevalence of multi-core processors and large main memories, most in-memory databases still universally adopt a centralized ARIES-logging with a single I/O channel, which can be a serious bottleneck. In this paper, we propose a parallel logging mechanism, named Plover for in-memory databases, which utilizes the partial order property of transactions{\textquoteright} dependencies and allows for concurrent logging in scalable storage devices. To further alleviate the performance overheads caused by log partitioning, we present a workload-aware log partitioning scheme to minimize the number of cross-partition transactions, while maintaining load balance. As such, Plover can scale well with the increasing number of storage devices and extensive experiments show that Plover with workload-aware partitioning can achieve 2× speedup over a centralized logging scheme and more than 42\% over Plover with random partitioning.",
keywords = "In-memory database, Parallel logging, Scalability",
author = "Huan Zhou and Jinwei Guo and Ouya Pei and Weining Qian and Xuan Zhou and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 ; Conference date: 23-07-2018 Through 25-07-2018",
year = "2018",
doi = "10.1007/978-3-319-96893-3\_3",
language = "英语",
isbn = "9783319968926",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "35--43",
editor = "Yi Cai and Yoshiharu Ishikawa and Jianliang Xu",
booktitle = "Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings",
address = "德国",
}