The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Youtube traffic characterization: a view from the edge
Abstract
This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time.
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
Web caching and Zipf-like distributions: evidence and implications
Breslau, L., Cao, P., Fan, L., Phillips, G., & Shenker, S. (1999) · IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320) · doi:10.1109/infcom.1999.749260 · Cited by 3,467
Self-similarity in World Wide Web traffic: evidence and possible causes
Crovella, M. E., & Bestavros, A. (1997) · IEEE/ACM Transactions on Networking · doi:10.1109/90.650143 · Cited by 2,731
Why we tag: motivations for annotation in mobile and online media
Ames, M., & Naaman, M. (2007) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240772 · Cited by 833
A workload characterization study of the 1998 World Cup Web site
Arlitt, M., & Jin, T. (2000) · IEEE Network · doi:10.1109/65.844498 · Cited by 766
Self-citationInternet Web servers: workload characterization and performance implications
Arlitt, M. F., & Williamson, C. L. (1997) · IEEE/ACM Transactions on Networking · doi:10.1109/90.649565 · Cited by 730
Self-citationEmpirically derived analytic models of wide-area TCP connections
Paxson, V. (1994) · IEEE/ACM Transactions on Networking · doi:10.1109/90.330413 · Cited by 581
Characteristics of WWW Client-based Traces
Cunha, C. R., Azer Bestavros, C., & Crovella, M. E. (1995) · Cited by 526
Denning, P. J. (1980) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1980.230464 · Cited by 396
An analysis of live streaming workloads on the internet
Sripanidkulchai, K., Maggs, B., & Zhang, H. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028795 · Cited by 317
Measurement and analysis of a streaming-media workload
Chesire, M., Wolman, A., Voelker, G. M., & Levy, H. M. (2001) · USENIX Symposium on Internet Technologies and Systems · Cited by 269
Cited by
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Sommer, R., & Paxson, V. (2010) · 2010 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2010.25 · Cited by 2,085
Statistics and Social Network of YouTube Videos
Cheng, X., Dale, C., & Liu, J. (2008) · 2008 16th Interntional Workshop on Quality of Service · doi:10.1109/iwqos.2008.32 · Cited by 720
Krishnamurthy, B., Gill, P., & Arlitt, M. (2008) · Proceedings of the first workshop on Online social networks · doi:10.1145/1397735.1397741 · Cited by 697
Self-citationAnalyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S. (2009) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2008.2011358 · Cited by 485
Social media addiction: What is the role of content in YouTube?
Balakrishnan, J., & Griffiths, M. D. (2017) · Journal of Behavioral Addictions · doi:10.1556/2006.6.2017.058 · Cited by 266
Metadata
- Authors
- Gill, P., Arlitt, M., Li, Z., & Mahanti, A.
- Venue
- Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
- Publisher
- ACM
- Published
- 2007-10-24
- Pages
- 15-28
- OpenAlex
- W1986697120
- Cited by
- 852
- References
- 17
- FWCI
- 29.71
- 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: Youtube traffic characterization: a view from the edge. https://app.cochat.ai/papers/10.1145/1298306.1298310
- The paper
- Gill, P., Arlitt, M., Li, Z., & Mahanti, A. (2007). Youtube traffic characterization: a view from the edge. Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. https://doi.org/10.1145/1298306.1298310
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.
