The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Performance Analysis of Private Blockchain Platforms in Varying Workloads
Abstract
This paper conducts a performance analysis of two popular private blockchain platforms, Hyperledger Fabric and Ethereum (private deployment), to assess the performance and limitations of these state-of-the-art platforms. Blockchain, a decentralized transaction and data management technology, is said to be the technology that will have similar impacts as the Internet had on people's lives. Many industries have become interested in adopting blockchain in their IT systems, but scalability is an often- cited concern of current blockchain technology. Therefore, the goals of this preliminary performance analysis are twofold. First, a methodology for evaluating a blockchain platform is developed. Second, the analysis results are presented to inform practitioners in making decisions regarding adoption of blockchain technology in their IT systems. The experimental results, based on varying number of transactions, show that Hyperledger Fabric consistently outperforms Ethereum across all evaluation metrics which are execution time, latency and throughput. Additionally, both platforms are still not competitive with current database systems in term of performances in high workload scenarios.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Where Is Current Research on Blockchain Technology?—A Systematic Review
Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016) · PLOS ONE · doi:10.1371/journal.pone.0163477 · Cited by 2,317
Information propagation in the Bitcoin network
Decker, C., & Wattenhofer, R. (2013) · IEEE P2P 2013 Proceedings · doi:10.1109/p2p.2013.6688704 · Cited by 1,192
On Scaling Decentralized Blockchains: (A Position Paper)
Croman, K., Decker, C., Eyal, I., Gencer, A. E., Juels, A., Kosba, A., Miller, A., Saxena, P., Shi, E., Gün Sirer, E., et al. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-53357-4_8 · Cited by 1,165
BLOCKBENCH: A Framework for Analyzing Private Blockchains
Dinh, T. T. A., Wang, J., Chen, G., Liu, R., Ooi, B. C., & Tan, K.-L. (2017) · Proceedings of the 2017 ACM International Conference on Management of Data · doi:10.1145/3035918.3064033 · Cited by 814
BLOCKBENCH: A Framework for Analyzing Private Blockchains
Dinh, T. T. A., Wang, J., Chen, G., Liu, R. Z. Z., Ooi, B. C., & Tan, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.04057 · Cited by 201
Cited by
A Vademecum on Blockchain Technologies: When, Which, and How
Belotti, M., Bozic, N., Pujolle, G., & Secci, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2928178 · Cited by 498
Makhdoom, I., Zhou, I., Abolhasan, M., Lipman, J., & Ni, W. (2020) · Computers & Security · doi:10.1016/j.cose.2019.101653 · Cited by 325
A Survey of IoT Applications in Blockchain Systems: Architecture, Consensus, and Traffic Modeling
Lao, L., Li, Z., Hou, S., Xiao, B., Guo, S., & Yang, Y. (2021) · ACM Computing Surveys · doi:10.1145/3372136 · Cited by 304
Performance Evaluation of Blockchain Systems: A Systematic Survey
Fan, C., Ghaemi, S., Khazaei, H., & Musilek, P. (2020) · IEEE Access · doi:10.1109/access.2020.3006078 · Cited by 294
Kuzlu, M., Pipattanasomporn, M., Gurses, L., & Rahman, S. (2019) · 2019 IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00003 · Cited by 279
Metadata
- Authors
- Pongnumkul, S., Siripanpornchana, C., & Thajchayapong, S.
- Venue
- 2017 26th International Conference on Computer Communication and Networks (ICCCN)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1-6
- OpenAlex
- W2754866430
- Cited by
- 398
- References
- 5
- FWCI
- 28.17
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Performance Analysis of Private Blockchain Platforms in Varying Workloads. https://app.cochat.ai/papers/10.1109/icccn.2017.8038517
- The paper
- Pongnumkul, S., Siripanpornchana, C., & Thajchayapong, S. (2017). Performance Analysis of Private Blockchain Platforms in Varying Workloads. 2017 26th International Conference on Computer Communication and Networks (ICCCN). https://doi.org/10.1109/icccn.2017.8038517
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
