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Online Spatiotemporal Popularity Learning via Variational Bayes for Cooperative Caching
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
2020 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 68, no 11, p. 7068-7082Article in journal (Refereed) Published
Abstract [en]

Herein, we focus on an end-to-end design of a proactive cooperative caching strategy for a multi-cell network. The design is challenging as it involves two interrelated problems: the ability to predict future content popularity and to meet network operation characteristics. To this end, we first formulate a cooperative content caching in order to optimize the aggregated network cost for delivering contents to users. An efficient proactive caching policy requires an accurate prediction of time-varying content popularity. Content popularity has temporal and spatial dependencies and therefore, we develop a probabilistic dynamical model for content popularity prediction by exploiting its spatiotemporal correlations. To achieve an accurate tracking and prediction of content popularity evolution, the proposed dynamical model is non-linear and incorporates non-Gaussian distributions. We use Variational Bayes (VB) approach for estimating the model parameters. The VB provides mathematical tractability. We then develop an online VB method that works with streaming data where content request arrives sequentially. Using extensive simulations study on a real-world dataset, we show that our online VB based dynamical model provides improved performance compared to conventional content caching policies.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 68, no 11, p. 7068-7082
Keywords [en]
Predictive models, Correlation, Servers, Bayes methods, Analytical models, Spatiotemporal phenomena, Cooperative caching, Content caching, multi-cell network, popularity prediction, routing, cache placement, online variational Bayes
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-287810DOI: 10.1109/TCOMM.2020.3015478ISI: 000591819400032Scopus ID: 2-s2.0-85096663038OAI: oai:DiVA.org:kth-287810DiVA, id: diva2:1522681
Note

QC 20210126

Available from: 2021-01-26 Created: 2021-01-26 Last updated: 2024-03-15Bibliographically approved

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Chatterjee, SaikatOttersten, Bjorn

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