Accelerating Concurrency Control with Active Thread Adjustment

Kosei Masumura, Takashi Hoshino, Hideyuki Kawashima

研究成果: Conference contribution

抄録

We attempted to improve the performance of Silo, a concurrency control protocol for inmemory DataBase Management System that performs well under high-contention work-loads. Adaptive backoff is known as an effective optimization method under high-contention workloads. As a result of analyzing, we found that its efficacy lies in the non-existence of conflict events rather than in the reduction of the conflict rate, which has been considered in the past. On the basis of this analysis, we propose a method of adjusting the number of active threads. We conducted experiments comparing Cicada, another concurrency control protocol, and our method applied to Silo. The results indicate that the proposed method enabled Silo to significantly outperform. We found that cache misses are related to the performance.

本文言語English
ホスト出版物のタイトルProceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
編集者Herwig Unger, Young-Kuk Kim, Eenjun Hwang, Sung-Bae Cho, Stephan Pareigis, Kyamakya Kyandoghere, Young-Guk Ha, Jinho Kim, Atsuyuki Morishima, Christian Wagner, Hyuk-Yoon Kwon, Yang-Sae Moon, Carson Leung
出版社Institute of Electrical and Electronics Engineers Inc.
ページ280-287
ページ数8
ISBN(電子版)9781665421973
DOI
出版ステータスPublished - 2022
イベント2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 - Daegu, Korea, Republic of
継続期間: 2022 1月 172022 1月 20

出版物シリーズ

名前Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022

Conference

Conference2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
国/地域Korea, Republic of
CityDaegu
Period22/1/1722/1/20

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 情報システムおよび情報管理
  • 健康情報学

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