Reference : Active Content Popularity Learning and Caching Optimization with Hit Ratio Guarantees
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/44137
Active Content Popularity Learning and Caching Optimization with Hit Ratio Guarantees
English
Bommaraveni, Srikanth mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
X.Vu, Thang mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
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) > >]
27-Aug-2020
Active Content Popularity Learning and Caching Optimization with Hit Ratio Guarantees
IEEE
8
11
Yes
International
2169-3536
2169-3536
[en] Edge caching ; Active learning ; Matrix completion
[en] Edge caching is an effective solution to reduce delivery latency and network congestion by bringing contents close to end-users. A deep understanding of content popularity and the principles underlying the content request sequence are required to effectively utilize the cache. Most existing works design caching policies based on global content requests with very limited consideration of individual content requests which reflect personal preferences. To enable the optimal caching strategy, in this paper, we propose an Active learning (AL) approach to learn the content popularities and design an accurate content request prediction model. We model the content requests from user terminals as a demand matrix and then employ AL-based query-by-committee (QBC) matrix completion to predict future missing requests. The main principle of QBC is to query the most informative missing entries of the demand matrix. Based on the prediction provided by the QBC, we propose an adaptive optimization caching framework to learn popularities as fast as possible while guaranteeing an operational cache hit ratio requirement. The proposed framework is model-free, thus does not require any statistical knowledge about the underlying traffic demands. We consider both the fixed and time-varying nature of content popularities. The effectiveness of the proposed learning caching policies over the existing methods is demonstrated in terms of root mean square error, cache hit ratio, and cache size on a simulated dataset.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
National Research Fund, Luxembourg
FNR CORE ProCAST
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/44137
10.1109/ACCESS.2020.3014379
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

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
09159587.pdfPublisher postprint1.31 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.