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Cited by 63Advanced Database Systems and QueriesCloud Computing and Resource ManagementParallel Computing and Optimization Techniques

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DSPBench: A Suite of Benchmark Applications for Distributed Data Stream Processing Systems

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Bordin, Maycon Viana, Griebler, Dalvan, Mencagli, Gabriele, Geyer, Claudio F. R., Fernandes, Luiz Gustavo L.·2020·IEEE Access
DOI 10.1109/access.2020.3043948
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Abstract

Systems enabling the continuous processing of large data streams have recently attracted the attention of the scientific community and industrial stakeholders. Data Stream Processing Systems (DSPSs) are complex and powerful frameworks able to ease the development of streaming applications in distributed computing environments like clusters and clouds. Several systems of this kind have been released and currently maintained as open source projects, like Apache Storm and Spark Streaming. Some benchmark applications have often been used by the scientific community to test and evaluate new techniques to improve the performance and usability of DSPSs. However, the existing benchmark suites lack of representative workloads coming from the wide set of application domains that can leverage the benefits offered by the stream processing paradigm in terms of near real-time performance. The goal of this article is to present a new benchmark suite composed of 15 applications coming from areas like Finance, Telecommunications, Sensor Networks, Social Networks and others. This article describes in detail the nature of these applications, their full workload characterization in terms of selectivity, processing cost, input size and overall memory occupation. In addition, it exemplifies the usefulness of our benchmark suite to compare real DSPSs by selecting Apache Storm and Spark Streaming for this analysis.

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  • Other recent studies benchmark qualities such as throughput, latency, and fault-tolerance of stream processing systems.

    “there are other recent studies that benchmark qualities such as throughput (Bordin et al., 2020;Chu et al., 2020;van Dongen and van den Poel, 2020), latency (Bordin et al., 2020;van Dongen and van den Poel, 2020;Hesse et al., 2021), and fault-tolerance (van Dongen and van den Poel, 2021b).”

    From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Bordin et al., 2020

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Metadata

Authors
Bordin, M. V., Griebler, D., Mencagli, G., Geyer, C. F. R., & Fernandes, L. G. L.
Affiliations
  • Universidade Federal do Rio Grande do Sul (BR)
  • Pontifícia Universidade Católica do Rio Grande do Sul (BR)
  • University of Pisa (IT)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
222900-222917
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2020.3043948
OpenAlex
W3112724157
Cited by
63
References
0
Citations by year
2026: 8 · 2025: 15 · 2024: 12 · 2023: 12 · 2022: 9 · 2021: 7
FWCI
4.74
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • PUCRS Repository (Pontifical Catholic University of Rio Grande do Sul) (submittedVersion, cc-by-nc-sa)
  • Greater South Information System
Funders
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (01/2017, 28/2018, (Code 001), 2018-0, 05/2019)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (437693/2018‐0, 2018-0, #28/2018)
  • Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (01/2017, 19/2551-0001895-9, 19/2551, Code 001, 19/2551-0001895, 17/2551‐0000871‐5, 05/2019)
  • Ministério da Ciência, Tecnologia, Inovações e Comunicações (28/2018)
  • Horizon 2020 Framework Programme (871385)
Topics
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
Keywords
stream processing, benchmark programs, workload characterization, Apache Storm, Spark Streaming, near real-time processing, sensor networks, social networks, telecommunications

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