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Analysis of Facebook content demand patterns
Lunds Universitet.
Lunds Universitet.
Lunds Universitet.
Lunds Universitet.
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2014 (English)In: SaCoNet 2014 / [ed] Said Hoceini, Guang-Jie Ren och Eva Marín-Tordera, IEEE Communications Society, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Data volumes in communication networks increase rapidly. Further, usage of social network applications is very wide spread among users, and among these applications, Facebook is the most popular. In this paper, we analyse user demands patterns and content popularity of Facebook generated traffic. The data comes from residential users in two metropolitan access networks in Sweden, and we analyse more than 17 million images downloaded by almost 16,000 Facebook users. We show that the distributions of image popularity and user activity may be described by Zipf distributions which is favourable for many types of caching. We also show that Facebook activity is more evenly spread over the day, compared to more defined peak hours of general Internet usage. Looking at content life time, we show that profile pictures have a relatively constant popularity while for other images there is an initial, short peak of demand, followed by a longer period of significantly lower and quite stable demand. These findings are useful for designing network andQoE optimisation solutions, such as predictive pre-fetching, proxy caching and delay tolerant networking.

Place, publisher, year, edition, pages
IEEE Communications Society, 2014.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hkr:diva-14782ISBN: 9781479951970 (print)OAI: oai:DiVA.org:hkr-14782DiVA: diva2:856675
Conference
SaCoNet 2014
Available from: 2015-09-24 Created: 2015-09-24 Last updated: 2015-12-11Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf