The DOI matches its Crossref record.
A Comparison of Stream Processing Frameworks
Abstract
This study compares the performance of Big Data Stream Processing frameworks including Apache Spark, Flink, and Storm. Also, it measures the resource usage and performance scalability of the frameworks against a varying number of cluster sizes. It has been observed that, Flink outperforms both Spark and Storm under equal constraints. However, Spark can be optimized to provide the higher throughput than Flink with the cost of higher latency.
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Vikash et al. (2020), Nasiri et al. (2019), Karakaya et al. (2017), and van Dongen and van den Poel (2021a) explicitly evaluate scalability, however without testing different load intensities against different resource amounts in isolated experiments.
“Vikash et al. (2020), Nasiri et al. (2019), Karakaya et al. (2017), and van Dongen and van den Poel (2021a) explicitly evaluate scalability, however, without testing different load intensities against different resource amounts in isolated experiments.”
From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Karakaya et al. (2017)
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Research works printed but not linked: 9
- 10.1109/UCC.2014.15 · DOI
- 10.1145/2628194.2628251 · DOI
- namiot, 2015, On Big Data Stream Processing, International Journal of Open Information Technologies, 3, 48
- nabi, 2014, Of Streams and Storms, IBM White Paper
- solaimani, 2014, Spark-based anomaly detection over multi-source VMware performance data in real-time, in Computational Intelligence, Proceedings of 2014 IEEE Symposium on Cyber Security, 1
- 10.3390/computers3040117 · DOI
- córdova, 2015, Analysis of Real Time Stream Processing Systems Considering Latency
- wang, 2016, Stream Processing Systems Benchmark StreamBench
- 10.1109/BigData.2015.7363756 · DOI
Cited by
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Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
Henning, S., & Hasselbring, W. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111879 · Cited by 85
Metadata
- Authors
- Karakaya, Z., Yazici, A., & Alayyoub, M.
- Affiliations
- Atilim University (TR)
- ORCID
- Karakaya, Ziya
- Venue
- 2017 International Conference on Computer and Applications (ICCA)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 1-12
- OpenAlex
- W2767178487
- Cited by
- 27
- References
- 0
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 7 · 2022: 2 · 2021: 5 · 2020: 2 · 2019: 2 · 2018: 3
- FWCI
- 1.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer and Applications (ICCA) (publishedVersion)
- 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 frameworks, Apache Spark, Apache Flink, Apache Storm, performance comparison, resource utilization, scalability, cluster size, throughput, latency, big data
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