Reordering Transaction Execution to Boost High Frequency Trading Applications

  • Ningnan Zhou
  • Xuan Zhou
  • Xiao Zhang
  • Xiaoyong Du
  • Shan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10367)

Abstract

High frequency trading (HFT) has always been welcomed because it benefits not only personal interests but also the whole social welfare. While the recent advance of portfolio selection in HFT market generates more profit, it yields much contended OLTP workloads. Featuring in exploiting the abundant parallelism, transaction pipeline, the state-of-the-art concurrency control (CC) mechanism, however suffers from limited concurrency confronted with HFT workloads. Its variants that enable more parallel execution by leveraging find-grained contention information also take little effect. To solve this problem, we for the first time observe and formulate the source of restricted concurrency as harmful ordering of transaction statements. To resolve harmful ordering, we propose PARE, a pipeline-aware reordered execution, to improve application performance by rearranging statements in order of their degrees of contention. In concrete, two mechanisms are devised to ensure the correctness of statement rearrangement and identify the degrees of contention of statements respectively. Experiment results show that PARE can improve transaction throughput and reduce transaction latency on HFT applications by up to an order of magnitude than the state-of-the-art CC mechanism.

Notes

Acknowledgement

This work is supported by Nature Science foundation of China Key Project No. 61432006 and The National Key Research and Development Program of China, No. 2016YFB1000702.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ningnan Zhou
    • 1
    • 2
  • Xuan Zhou
    • 3
  • Xiao Zhang
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
  • Shan Wang
    • 1
    • 2
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.MOE Key Laboratory of DEKERenmin University of ChinaBeijingChina
  3. 3.School of Data Science and EngineeringEast China Normal UniversityShanghaiChina

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