Accelerating Geo-Distributed Transaction Processing with Fast Logging

Takuto Ogura, Yoshiki Akita, Yuki Miyazawa, Hideyuki Kawashima

研究成果: Conference contribution

5 被引用数 (Scopus)


We herein propose three novel optimization methods to accelerate distributed transaction processing into a geographically distributed database. The first optimization involves the parallelization of the write-ahead logging protocol. It allows multiple worker threads to synchronize log entries to the storage device simultaneously without any dependencies. The second optimization involves the grouped transfer of log entries from the leader to followers. This reduces the number of transmissions and effectively uses the network bandwidth. The third optimization involves the separation of the worker thread logic. By breaking the logic into the prepare phase and the commit phase, the worker threads at the leader node run asynchronously in parallel without waiting for responses from the follower nodes. The experimental results demonstrated that the proposed method achieved more than 10 million tps and less than 100 ms with client interactions through the network. The CPU utilization was almost 100%, which implied a dramatic reduction in synchronization in worker threads.

ホスト出版物のタイトルProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
編集者Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
継続期間: 2021 12月 152021 12月 18


名前Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021


Conference2021 IEEE International Conference on Big Data, Big Data 2021
国/地域United States
CityVirtual, Online

ASJC Scopus subject areas

  • 情報システムおよび情報管理
  • 人工知能
  • 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


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