Reference : Active Content Popularity Learning via Query-by-Committee for Edge Caching
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Multidisciplinary, general & others
Security, Reliability and Trust
http://hdl.handle.net/10993/42591
Active Content Popularity Learning via Query-by-Committee for Edge Caching
English
Bommaraveni, Srikanth mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Vu, Thang mailto []
Vuppala, Satyanarayana mailto []
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
3-Nov-2019
Yes
International
The 53ND ANNUAL ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, Nov. 2019.
NOVEMBER 3-6 2019
IEEE
Asilomar Hotel and Conference Ground, Pacific Grove, California
USA
[en] Edge caching ; Active learning ; Matrix completion, Content popularity, 5G cellular
[en] Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications. One of the challenges in edge caching systems is to optimally cache strategic contents to maximize the percentage of total requests served by the edge caches. To enable the optimal caching strategy, we propose an Active Learning approach (AL) to learn and design an accurate content request prediction algorithm. Specifically, we use an AL based Query-by-committee (QBC) matrix completion algorithm with a strategy of querying the most informative missing entries of the content popularity matrix. The proposed AL framework leverage's the trade-off between exploration and exploitation of the network, and learn the user's preferences by posing queries or recommendations. Later, it exploits the known information to maximize the system performance. The effectiveness of proposed AL based QBC content learning algorithm is demonstrated via numerical results.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/42591
FnR ; FNR11691338 > Bjorn Ottersten > ProCAST > Proactive Edge Caching for Content Delivery Networks powered by Hybrid Satellite/Terrestrial Backhauling > 01/07/2018 > 30/06/2021 > 2017

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