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Optimizing Retention-Aware Caching in Vehicular Networks
Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Sichuan, Peoples R China.
Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Sichuan, Peoples R China.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
2019 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 9, p. 6139-6152Article in journal (Refereed) Published
Abstract [en]

Caching is an effective way to address the challenges due to explosive data traffic growth and massive device connectivity in fifth-generation (5G) networks. Currently, few works on caching pay attention to the impact of the time duration for which content is stored, called retention time, on caching optimization. The research on retention time is motivated by two practical issues, i.e., flash memory damage and storage rental cost in cloud networks, together giving rise to the storage cost. How to optimize caching contents taking the storage cost into consideration is a challenging problem, especially for the scenarios with cache-enabled mobile nodes. In this paper, a retention-aware caching problem (RACP) in vehicular networks is formulated, considering the impact of the storage cost. The problem's complexity analysis is provided. For symmetric cases, an optimal dynamic programming (DP) algorithm with polynomial time complexity is derived. For general cases, a low complexity and effective retention aware multi-helper caching algorithm (RAMA) is proposed. Numerical results are used to verify the effectiveness of the algorithms.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 67, no 9, p. 6139-6152
Keywords [en]
Caching, storage cost, vehicular networks
National Category
Telecommunications Communication Systems
Identifiers
URN: urn:nbn:se:uu:diva-395701DOI: 10.1109/TCOMM.2019.2921368ISI: 000487048400017OAI: oai:DiVA.org:uu-395701DiVA, id: diva2:1365051
Funder
Swedish Research Council, 2018-05247Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2019-10-23Bibliographically approved

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