The DOI matches its Crossref record.
The HiBench benchmark suite: Characterization of the MapReduce-based data analysis
Abstract
The MapReduce model is becoming prominent for the large-scale data analysis in the cloud. In this paper, we present the benchmarking, evaluation and characterization of Hadoop, an open-source implementation of MapReduce. We first introduce HiBench, a new benchmark suite for Hadoop. It consists of a set of Hadoop programs, including both synthetic micro-benchmarks and real-world Hadoop applications. We then evaluate and characterize the Hadoop framework using HiBench, in terms of speed (i.e., job running time), throughput (i.e., the number of tasks completed per minute), HDFS bandwidth, system resource (e.g., CPU, memory and I/O) utilizations, and data access patterns.
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
Cited by
Elasticity in Cloud Computing: State of the Art and Research Challenges
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2711009 · Cited by 409
(2015) · doi:10.1145/2815400.2815415 · Cited by 202
DSPBench: A suite of benchmark applications for distributed data stream processing systems
Bordin, M. V., Griebler, D., Mencagli, G., Geyer, C. F. R., & Fernandes, L. G. L. (2020) · IEEE Access · doi:10.1109/access.2020.3043948 · Cited by 63
ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications
Malik, M., Ghasemzadeh, H., Mohsenin, T., Cammarota, R., Zhao, L., Sasan, A., Homayoun, H., & Rafatirad, S. (2019) · Proceedings of the 48th International Conference on Parallel Processing · doi:10.1145/3337821.3337834 · Cited by 11
Metadata
- Authors
- Huang, S., Huang, J., Dai, J., Xie, T., & Huang, B.
- Venue
- 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)
- Publisher
- IEEE
- Published
- 2010
- Pages
- 41-51
- OpenAlex
- W2155072926
- Cited by
- 713
- References
- 0
- FWCI
- 14.62
- 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: The HiBench benchmark suite: Characterization of the MapReduce-based data analysis. https://app.cochat.ai/papers/10.1109/icdew.2010.5452747
- The paper
- Huang, S., Huang, J., Dai, J., Xie, T., & Huang, B. (2010). The HiBench benchmark suite: Characterization of the MapReduce-based data analysis. 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010). https://doi.org/10.1109/icdew.2010.5452747
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.
