@inproceedings{fc20bb98569a406aaa45284010033c94,
title = "Apara: Workload-aware data partition and replication for parallel databases",
abstract = "Data partition and replication mechanisms directly determine query execution patterns in parallel database systems, which have a great impact on system performance. Recently, there have been some workload-aware data storage techniques, but they suffer from problems of narrow support to complex workloads or large requirements for storage. In order to enable the support for complex analytical workloads over massive distributed database systems, we design and implement a workload-aware data partition and replication tool, called Apara. We design two heuristic algorithms and define two cost models for effective data partition calculation and efficient replication usages. We run a set of experiments to compare and demonstrate the performance between Apara and the other representative work. The results show that Apara consistently outperforms the primary solutions on TPC-H workloads.",
keywords = "Distributed database, Partition, Replication, Workload-aware storage",
author = "Xiaolei Zhang and Chunxi Zhang and Yuming Li and Rong Zhang and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 ; Conference date: 01-08-2019 Through 03-08-2019",
year = "2019",
doi = "10.1007/978-3-030-26075-0\_15",
language = "英语",
isbn = "9783030260743",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "191--206",
editor = "Jie Shao and Yiu, \{Man Lung\} and Masashi Toyoda and Dongxiang Zhang and Wei Wang and Bin Cui",
booktitle = "Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings",
address = "德国",
}