Analysis of Facebook content demand patterns
(2014) International Conference on Smart Communications in Network Technologies (SaCoNeT)- Abstract
- 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... (More)
- 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 and QoE optimisation solutions, such as predictive pre-fetching, proxy caching and delay tolerant networking. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4812793
- author
- Kihl, Maria LU ; Larsson, Robin LU ; Unnervik, Niclas ; Haberkamm, Jolina ; Arvidsson, Åke and Aurelius, Andreas
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- International Conference on Smart Communications in Network Technologies (SaCoNeT)
- conference location
- Vilanova, Spain
- conference dates
- 2014-06-18 - 2014-06-20
- external identifiers
-
- wos:000345738400001
- scopus:84906311237
- DOI
- 10.1109/SaCoNeT.2014.6867760
- project
- EIT_EFRAIM Eco system for future media distribution
- EIT_NOTTS Next generation over-the-top multimedia services
- LCCC
- language
- English
- LU publication?
- yes
- id
- 4deae8bc-31d4-4289-97d0-943deaafb592 (old id 4812793)
- date added to LUP
- 2016-04-04 09:57:54
- date last changed
- 2022-02-28 17:38:03
@inproceedings{4deae8bc-31d4-4289-97d0-943deaafb592,
abstract = {{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 and QoE optimisation solutions, such as predictive pre-fetching, proxy caching and delay tolerant networking.}},
author = {{Kihl, Maria and Larsson, Robin and Unnervik, Niclas and Haberkamm, Jolina and Arvidsson, Åke and Aurelius, Andreas}},
booktitle = {{[Host publication title missing]}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Analysis of Facebook content demand patterns}},
url = {{https://lup.lub.lu.se/search/files/5427961/4812798.pdf}},
doi = {{10.1109/SaCoNeT.2014.6867760}},
year = {{2014}},
}