Self-similarity in World Wide Web traffic: evidence and possible causes
Abstract
The notion of self-similarity has been shown to apply to wide-area and local-area network traffic. We show evidence that the subset of network traffic that is due to World Wide Web (WWW) transfers can show characteristics that are consistent with self-similarity, and we present a hypothesized explanation for that self-similarity. Using a set of traces of actual user executions of NCSA Mosaic, we examine the dependence structure of WWW traffic. First, we show evidence that WWW traffic exhibits behavior that is consistent with self-similar traffic models. Then we show that the self-similarity in such traffic can be explained based on the underlying distributions of WWW document sizes, the effects of caching and user preference in file transfer, the effect of user "think time", and the superimposition of many such transfers in a local-area network. To do this, we rely on empirically measured distributions both from client traces and from data independently collected at WWW servers.
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Job size distributions in a number of settings are well-modeled by a Pareto distribution with α close to 1.
“(As observed in prior work [7, 8, 2], job size distributions in a number of settings are well-modeled by a Pareto”
From Improving Online Algorithms via ML Predictions (2024) · cites this paper as [7]
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Improving Online Algorithms via ML Predictions
Kumar, R., Purohit, M., & Svitkina, Z. (2024) · arXiv · doi:10.48550/arxiv.2407.17712 · Cited by 111 · see citing works
- Authors
- Crovella, M. E., & Bestavros, A.
- Affiliations
- Boston University (US)
- ORCID
- Crovella, M.E.
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 1997
- Volume
- 5
- Issue
- 6
- Pages
- 835-846
- ISSN
- 1063-6692 (ISSN-L)
- OpenAlex
- W2138543759
- Cited by
- 2,731
- References
- 0
- Citations by year
- 2026: 18 · 2025: 12 · 2024: 12 · 2023: 20 · 2022: 20 · 2021: 26 · 2020: 37 · 2019: 37 · 2018: 43 · 2017: 60 · 2016: 55 · 2015: 61 · 2014: 81 · 2013: 93 · 2012: 98
- FWCI
- 91.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Transactions on Networking (publishedVersion)
- Funders
- University at Buffalo
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- self-similarity, long-range dependence, network traffic modeling, caching, user preferences
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