extended-abstract

A New Application Benchmark for Data Stream Processing Architectures in an Enterprise Context: Doctoral Symposium

Authors:

Guenter Hesse,

Christoph Matthies,

Benjamin Reissaus,

Matthias UflackerAuthors Info & Claims

DEBS '17: Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems

Pages 359 - 362

https://doi.org/10.1145/3093742.3093902

Published: 08 June 2017 Publication History

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Abstract

Against the backdrop of ever-growing data volumes and trends like the Internet of Things (IoT) or Industry 4.0, Data Stream Processing Systems (DSPSs) or data stream processing architectures in general receive a greater interest. Continuously analyzing streams of data allows immediate responses to environmental changes. A challenging task in that context is assessing and comparing data stream processing architectures in order to identify the most suitable one for certain settings.

The present paper provides an overview about performance benchmarks that can be used for analyzing data stream processing applications. By describing shortcomings of these benchmarks, the need for a new application benchmark in this area, especially for a benchmark covering enterprise architectures, is highlighted. A key role in such an enterprise context is the combination of streaming data and business data, which is barely covered in current data stream processing benchmarks. Furthermore, first ideas towards the development of a solution, i.e., a new application benchmark that is able to fill the existing gap, are depicted.

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Cited By

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Hesse GMatthies CPerscheid MUflacker MPlattner HBourcier JJiang ZBezemer CCortellessa V(2021)ESPBench: The Enterprise Stream Processing BenchmarkProceedings of the ACM/SPEC International Conference on Performance Engineering10.1145/3427921.3450242(201-212)Online publication date: 9-Apr-2021
https://dl.acm.org/doi/10.1145/3427921.3450242
Hesse GMatthies CUflacker M(2020)How Fast Can We Insert? An Empirical Performance Evaluation of Apache Kafka2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS51040.2020.00089(641-648)Online publication date: Dec-2020
https://doi.org/10.1109/ICPADS51040.2020.00089

Index Terms

A New Application Benchmark for Data Stream Processing Architectures in an Enterprise Context: Doctoral Symposium
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Stream management
  2. Information systems applications
    1. Enterprise information systems
      1. Enterprise applications
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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Published In

DEBS '17: Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems

June 2017

393 pages

ISBN:9781450350655

DOI:10.1145/3093742

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 June 2017

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Extended-abstract
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Refereed limited

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DEBS '17

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DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems

June 19 - 23, 2017

Barcelona, Spain

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DEBS '17 Paper Acceptance Rate 22 of 60 submissions, 37%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

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Hesse GMatthies CPerscheid MUflacker MPlattner HBourcier JJiang ZBezemer CCortellessa V(2021)ESPBench: The Enterprise Stream Processing BenchmarkProceedings of the ACM/SPEC International Conference on Performance Engineering10.1145/3427921.3450242(201-212)Online publication date: 9-Apr-2021
https://dl.acm.org/doi/10.1145/3427921.3450242
Hesse GMatthies CUflacker M(2020)How Fast Can We Insert? An Empirical Performance Evaluation of Apache Kafka2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS51040.2020.00089(641-648)Online publication date: Dec-2020
https://doi.org/10.1109/ICPADS51040.2020.00089

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Recommendations

ESPBench: The Enterprise Stream Processing Benchmark

Dual-Paradigm Stream Processing

Energy consumption analysis of data stream processing: a benchmarking approach