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Comparing Implementation Platforms for Real-Time Stream Processing Systems on Multi-Core Hardware
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (Software Technology Labs)
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (Software Technology Labs)ORCID iD: 0000-0002-7565-3714
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
2011 (English)In: Proceedings of the 23rd IASTED International Conference: Parallel and Distributed Computing and Systems / [ed] T. Gonzalez, Calgary, AB, Canada: ACTA Press, 2011, p. 235-243Chapter in book (Refereed)
Abstract [en]

Today there exist many programming models and platforms for implementing real-time stream processing systems. A decision in favor of the wrong technology might lead to increased development time and costs. It is, therefore, necessary to decide which alternatives further efforts should concentrate on and which may be forgotten. Such decisions cannot be based sole on analytical comparisons; the present experiment seeks to complement analytical with empirical results.

More specifically, the paper discusses the results of comparing programmability and performance of one and the same real-world real-time stream processing system implemented using three different alternative implementation platforms: C++ as a general-purpose programming language, IBM InfoSphere Streams as a dedicated stream processing platform, and MatLab as the technical computing system preferred in the application domain. As a result: system implementation based on MatLab was easiest, the C++ based implementation outperformed the others in response time, while InfoSphereStreams led to the highest data throughput. Altogether, the results give a picture of advantages and disadvantages of each technology for our real-time stream processing system. More empirical studies ought to provide similar empirical knowledge to help decide which technology to use for solving particular stream processing problems.

Place, publisher, year, edition, pages
Calgary, AB, Canada: ACTA Press, 2011. p. 235-243
Keywords [en]
Real-time systems, parallel computing, stream processing, performance, programmability
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-16723ISBN: 978-0-88986-907-3 (print)OAI: oai:DiVA.org:lnu-16723DiVA, id: diva2:476261
Conference
Parallel and Distributed Computing and Systems, Dallas, USA, December 14–16, 2011
Available from: 2012-01-13 Created: 2012-01-11 Last updated: 2018-01-12Bibliographically approved

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Danylenko, OlegLöwe, WelfRydström, Sara

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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  • en-US
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  • Other locale
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Output format
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