Efficient content replacement in wireless content delivery network with cooperative caching

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dc.contributor.authorSung, Jihoonko
dc.contributor.authorKim, Kyounghyeko
dc.contributor.authorKim, Junhyukko
dc.contributor.authorRhee, June-Koo Kevinko
dc.date.accessioned2023-07-28T06:00:28Z-
dc.date.available2023-07-28T06:00:28Z-
dc.date.created2023-07-07-
dc.date.created2023-07-07-
dc.date.issued2016-12-
dc.identifier.citation15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, pp.547 - 552-
dc.identifier.urihttp://hdl.handle.net/10203/310943-
dc.description.abstractWireless content delivery networks (WCDNs) have received attention as a promising solution to reduce the network congestion caused by rapidly growing demands for mobile content. The amount of reduced congestion is intuitively proportional to the hit ratio in a WCDN. Cooperation among cache servers is strongly required to maximize the hit ratio in a WCDN where each cache server is equipped with a small-size cache storage space. In this paper, we address a content replacement problem that deals with how to manage contents in a limited cache storage space in a reactive manner to cope with a dynamic content demand over time. As a new challenge, we apply reinforcement learning, which is Q-learning, to the content replacement problem in a WCDN with coooperative caching. We model the content replacement problem as a Markov Decision Process (MDP) and finally propose an efficient content replacement strategy to maximize the hit ratio based on a multi-agent Q-learning scheme. Simulation results exhibit that the proposed strategy contributes to achieving better content delivery performance in delay due to a higher hit ratio, compared to typical existing schemes of least recently used (LRU) and least frequently used (LFU).-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleEfficient content replacement in wireless content delivery network with cooperative caching-
dc.typeConference-
dc.identifier.wosid000399100100087-
dc.identifier.scopusid2-s2.0-85015359225-
dc.type.rimsCONF-
dc.citation.beginningpage547-
dc.citation.endingpage552-
dc.citation.publicationname15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationAnaheim, CA-
dc.contributor.localauthorRhee, June-Koo Kevin-
dc.contributor.nonIdAuthorKim, Junhyuk-
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EE-Conference Papers(학술회의논문)
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