Skip to Main content Skip to Navigation
Conference papers

Caching strategies based on popularity prediction in content delivery networks

Abstract : In Content Delivery Networks (CDNs), knowing the popularity of video content helps the manager to take efficient decisions about which video content should be cached near the end users and also about the duplication degree of each video to satisfy the end user Quality of Experience. This paper focuses on predicting the popularity of video content, in terms of the number of requests. For that purpose, different software entities, called experts, compute the popularity value of each video content. Each expert uses its own prediction method. The accuracy of expert's prediction is evaluated by a loss function as the discrepancy between the prediction value and the real number of requests. We use real traces extracted from YouTube to compare different prediction methods and determine the best tuning of their parameters. The goal is to find the best trade-off between complexity and accuracy of the prediction methods used. Finally, we apply these prediction methods to caching. Prediction methods are compared in terms of cache Hit Ratio and Update Ratio with the well-known LFU caching strategy.
Document type :
Conference papers
Complete list of metadata

Cited literature [9 references]  Display  Hide  Download

https://hal.inria.fr/hal-01411122
Contributor : Nesrine Ben Hassine Connect in order to contact the contributor
Submitted on : Wednesday, December 7, 2016 - 9:52:35 AM
Last modification on : Wednesday, October 20, 2021 - 12:24:14 AM
Long-term archiving on: : Tuesday, March 21, 2017 - 12:38:37 AM

File

pemwn2016.pdf
Files produced by the author(s)

Identifiers

Collections

`

Citation

Nesrine Hassine, Dana Marinca, Pascale Minet, Dominique Barth. Caching strategies based on popularity prediction in content delivery networks. The 12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Oct 2016, New York City, United States. pp.1 - 8, ⟨10.1109/WiMOB.2016.7763215⟩. ⟨hal-01411122⟩

Share

Metrics

Record views

524

Files downloads

751