Journal
IEEE COMMUNICATIONS LETTERS
Volume 23, Issue 12, Pages 2335-2339Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2019.2941202
Keywords
Servers; Microcell networks; Wireless communication; Libraries; Estimation; Cache memory; Base stations; Cache networks; online learning; predictive caching; small cell networks; time-varying popularity distribution
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Funding
- Electronics and Telecommunications Research Institute (ETRI) - Korean Government [18ZF1100]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF2017R1D1A1A09000835]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [18ZF1100] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this letter, dynamic content placement of a local cache server that can store a subset of content objects in its cache memory is studied. Contrary to the conventional model in which content placement is optimized based on the time-invariant popularity distribution of content objects, we consider a general time-varying popularity distribution and such a probabilistic distribution is unknown for content placement. A novel learning method for predicting the temporal distribution of future content requests is presented, which utilizes the request histories of content objects whose lifespans are expired. Then we introduce the so-called predictive caching strategy in which content placement is periodically updated based on the estimated future content requests for each update period. Numerical evaluation is performed using real-world datasets reflecting the inherent nature of temporal dynamics, demonstrating that the proposed predictive caching outperforms the conventional online caching strategies.
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