Scalable and quantitative contention generation for performance evaluation on OLTP databases-Reference-Cited by-同舟云学术

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Scalable and quantitative contention generation for performance evaluation on OLTP databases

Published:2022-08-09 Issue:2 Volume:17 Page:
ISSN:2095-2228
Container-title:Frontiers of Computer Science
language:en
Short-container-title:Front. Comput. Sci.

Author:

Zhang Chunxi,Li Yuming,Zhang Rong,Qian Weining,Zhou Aoying

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11704-022-1056-2.pdf

Reference21 articles.

1. Huang G, Cheng X, Wang J, Wang Y, He D, Zhang T, Li F, Wang S, Cao W, Li Q. X-engine: an optimized storage engine for large-scale ecommerce transaction processing. In: Proceedings of 2019 International Conference on Management of Data. 2019, 651–665

2. Appuswamy R, Anadiotis A C, Porobic D, Iman M K, Ailamaki A. Analyzing the impact of system architecture on the scalability of OLTP engines for high-contention workloads. Proceedings of the VLDB Endowment, 2017, 11(2): 121–134

3. Harding R, Van Aken D, Pavlo A, Stonebraker M. An evaluation of distributed concurrency control. Proceedings of the VLDB Endowment, 2017, 10(5): 553–564

4. Lan H, Bao Z, Peng Y. A survey on advancing the DBMS query optimizer: cardinality estimation, cost model, and plan enumeration. Data Science and Engineering, 2021, 6(1): 86–101

5. Ji Y, Chai Y, Zhou X, Ren L, Qin Y. Smart intra-query fault tolerance for massive parallel processing databases. Data Science And Engineering, 2020, 5(1): 65–79

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