The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Coflow: a networking abstraction for cluster applications
Abstract
Cluster computing applications -- frameworks like MapReduce and user-facing applications like search platforms -- have application-level requirements and higher-level abstractions to express them. However, there exists no networking abstraction that can take advantage of the rich semantics readily available from these data parallel applications.
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
McKeown (2008) · doi:10.1145/1355734.1355746 · Cited by 8,513
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003) · ACM SIGOPS Operating Systems Review · doi:10.1145/1165389.945450 · Cited by 4,984
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622
Self-citationPregel: a system for large-scale graph processing
Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807184 · Cited by 3,515
MapReduce: Simplified Data Processing on Large Cluster
Senior Software Developer, A. U., & Dayalan, M. (2018) · International Journal of Research and Engineering · doi:10.21276/ijre.2018.5.5.4 · Cited by 2,948
Dryad: distributed data-parallel programs from sequential building blocks
Isard, M., Budiu, M., Yu, Y., Birrell, A., & Fetterly, D. (2007) · Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007 · doi:10.1145/1272996.1273005 · Cited by 2,432
Pig latin: a not-so-foreign language for data processing
Olston, C., Reed, B., Srivastava, U., Kumar, R., & Tomkins, A. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376726 · Cited by 1,730
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., & Hellerstein, J. M. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2212351.2212354 · Cited by 1,653
Hedera: dynamic flow scheduling for data center networks
Al-Fares, M., Radhakrishnan, S., Raghavan, B., Huang, N., & Vahdat, A. M. (2010) · doi:10.5555/1855711.1855730 · Cited by 1,450
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003) · Proceedings of the nineteenth ACM symposium on Operating systems principles · doi:10.1145/945445.945450 · Cited by 1,423
Cited by
Efficient coflow scheduling with Varys
Chowdhury, M., Zhong, Y., & Stoica, I. (2014) · Proceedings of the 2014 ACM conference on SIGCOMM · doi:10.1145/2619239.2626315 · Cited by 438
Self-citationComprehensive analysis of big data variety landscape
Abawajy, J. (2015) · International Journal of Parallel, Emergent and Distributed Systems · doi:10.1080/17445760.2014.925548 · Cited by 71
Metadata
- Authors
- Chowdhury, M., & Stoica, I.
- Venue
- Proceedings of the 11th ACM Workshop on Hot Topics in Networks
- Publisher
- ACM
- Published
- 2012-10-29
- Pages
- 31-36
- OpenAlex
- W2013932952
- Cited by
- 340
- References
- 24
- FWCI
- 11.88
- 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: Coflow: a networking abstraction for cluster applications. https://app.cochat.ai/papers/10.1145/2390231.2390237
- The paper
- Chowdhury, M., & Stoica, I. (2012). Coflow: a networking abstraction for cluster applications. Proceedings of the 11th ACM Workshop on Hot Topics in Networks. https://doi.org/10.1145/2390231.2390237
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.
