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AuthorHe, Wenji
AuthorYao, Haipeng
AuthorMai, Tianle
AuthorGuizani, Mohsen
Available date2022-11-14T07:32:24Z
Publication Date2020-12-12
Publication Name2020 3rd International Conference on Hot Information-Centric Networking, HotICN 2020
Identifierhttp://dx.doi.org/10.1109/HotICN50779.2020.9350795
CitationHe, W., Yao, H., Mai, T., & Guizani, M. (2020, December). Programmable Switch Aided Content Popularity Prediction and Caching Strategy. In 2020 3rd International Conference on Hot Information-Centric Networking (HotICN) (pp. 188-193). IEEE.‏
ISBN9781728192161
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102108929&origin=inward
URIhttp://hdl.handle.net/10576/36293
AbstractContent distribution is the most critical task for the current Internet, (e.g., the estimated video traffic will reach 82 percent of the Internet traffic by 2022). With the fast increase of load of the network, traditional host-centric based network paradigm (i.e., TCP/IP) faces great challenges in terms of efficiency, security, and privacy. To solve the problems confronting the current Internet, the Information-Centric Network (ICN) becomes a promising solution, where the focal point is identified content rather than specific host addresses. This paradigm brings many benefits, e.g., network traffic reduction, low retrieval latency. Besides, benefiting from the advance of programmable network hardware, the operator can reconfigure the network hardware' behavior, thus providing hardware support to describe the ICN instances. However, ICN also poses new challenges to cache management. The cache redundancy and unequal resource allocation will seriously affect the performance of the network. In this paper, we propose a distributed variational Bayes aided content popularity prediction algorithm. The extensive and indepth simulations are performed to evaluate our proposed algorithm in comparison to the other state-of-the-art schemes.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcache
content popularity
distributed variational bayes
ICN
TitleProgrammable Switch Aided Content Popularity Prediction and Caching Strategy
TypeConference Paper
Pagination188-193
dc.accessType Abstract Only


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