Predictive Caching via Learning Temporal Distribution of Content Requests
- Title
- Predictive Caching via Learning Temporal Distribution of Content Requests
- Author
- 전상운
- 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
- Issue Date
- 2019-12
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE COMMUNICATIONS LETTERS, v. 23, No. 12, Page. 2335-2339
- Abstract
- 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.
- URI
- https://ieeexplore.ieee.org/document/8836639https://repository.hanyang.ac.kr/handle/20.500.11754/122287
- ISSN
- 1089-7798; 1558-2558
- DOI
- 10.1109/LCOMM.2019.2941202
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MILITARY INFORMATION ENGINEERING(국방정보공학과) > Articles
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