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Cited by 15Cloud Computing and Resource ManagementAdvanced Database Systems and QueriesParallel Computing and Optimization Techniques

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Streaming vs. Functions: A Cost Perspective on Cloud Event Processing

Open access
Pfandzelter, Tobias, Henning, Soren, Schirmer, Trever, Hasselbring, Wilhelm, Bermbach, David·2022·2022 IEEE International Conference on Cloud Engineering (IC2E)
DOI 10.1109/ic2e55432.2022.00015
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Abstract

In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.

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  • The previous study applies the Theodolite method but benchmarks scalability with respect to costs and compares stream processing deployments against Function-as-a-Service offerings.

    “Our previous study (Pfandzelter et al., 2022) applies our Theodolite method as well, but benchmarks scalability with respect to costs and is addressed to comparing stream processing deployments against Function-as-a-Service offerings.”

    From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Pfandzelter et al., 2022

    The Benchmark Methodology and Cost Measurement sections describe using Theodolite components to estimate costs at different workload levels, and the Conclusions compare distributed stream processing with FaaS. However, the Benchmark Methodology explicitly says the goal is not to measure platform scalability or elasticity, so describing the study as benchmarking scalability overstates its stated aim. The passages provide no page numbers. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Except for this study and the previous study, there are no evaluations in Kubernetes.

    “Except for this and our previous study (Pfandzelter et al., 2022), there are no evaluations in Kubernetes.”

    From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Pfandzelter et al., 2022

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Research works printed but not linked: 56

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Metadata

Authors
Pfandzelter, T., Henning, S., Schirmer, T., Hasselbring, W., & Bermbach, D.
Affiliations
  • Einstein Center Digital Future (DE)
  • Kiel University, Software Engineering Group
  • Christian-Albrechts-Universität zu Kiel (DE)
ORCID
Venue
2022 IEEE International Conference on Cloud Engineering (IC2E)
Publisher
IEEE
Published
2022-09
Pages
67-78
DOI
10.1109/ic2e55432.2022.00015
OpenAlex
W4312775432
Cited by
15
References
0
Citations by year
2026: 3 · 2025: 4 · 2024: 2 · 2023: 6
FWCI
2.10
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE International Conference on Cloud Engineering (publishedVersion)
  • Helmholtz Centre for Ocean Research Kiel (GEOMAR) (acceptedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • ePrints Soton (University of Southampton) (submittedVersion)
  • arXiv (Cornell University)
Funders
Deutsche Forschungsgemeinschaft (415899119)
Topics
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
Keywords
distributed stream processing, function-as-a-service, cost analysis, real-time processing, runtime systems

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