Reordering transaction execution to boost high frequency trading applications

  • Ningnan Zhou
  • , Xuan Zhou
  • , Xiao Zhang*
  • , Xiaoyong Du
  • , Shan Wang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 upto an order of magnitude than the state-of-the-art CC mechanism.

Original languageEnglish
Title of host publicationWeb and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings
EditorsChristian S. Jensen, Xiang Lian, Lei Chen, Cyrus Shahabi, Xiaochun Yang
PublisherSpringer Verlag
Pages169-184
Number of pages16
ISBN (Print)9783319635637
DOIs
StatePublished - 2017
Event1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 - Beijing, China
Duration: 7 Jul 20179 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017
Country/TerritoryChina
CityBeijing
Period7/07/179/07/17

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