KCC 2021
Current Result Document :
| 한글제목(Korean Title) |
Distributed Learning-Based Proactive Content Caching for Improved Quality-of-Experience (QoE) |
| 영문제목(English Title) |
Distributed Learning-Based Proactive Content Caching for Improved Quality-of-Experience (QoE) |
| 저자(Author) |
Subina Khanal
Eui-Nam Huh
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| 원문수록처(Citation) |
VOL 48 NO. 01 PP. 1295 ~ 1297 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Content popularity at the network edge is dynamic, i.e., the contents requested by users change over time. As a result, conventional content caching techniques make it difficult to guarantee Service Level Agreements (SLAs) and Quality-of-Experience (QoE) for seamless media streaming. Given the limited cache space and the high number of cache misses, a proactive content caching strategy is essential. Furthermore, in a distributed network infrastructure, the decision to cache or not cache the contents at different nodes affect the cache hit probability significantly. This paper proposes a distributed learning-based popularity prediction framework for proactive content caching that includes local prediction models to build a regional content popularity database. We use item-based collaborative filtering to construct a proactive content caching algorithm for seamless media streaming at the network edge. Simulation results show the efficiency of our proposed approach. |
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| 파일첨부 |
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