Proactive Caching Scheme with Local Content Popularity Prediction

doi:10.13190/j.jbupt.2019-054

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (1): 80-91.doi: 10.13190/j.jbupt.2019-054

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Proactive Caching Scheme with Local Content Popularity Prediction

REN Jia-zhi, TIAN Hui, NIE Gao-feng

State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2019-04-10 Online:2020-02-28 Published:2020-03-27
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Abstract

Abstract: Considering the problem that most works on content placement so far consider global popularity, neglecting the demand difference between base stations (BSs), a content placement scheme based on similarity between small base stations (SBSs) and local content popularity prediction considering popularities' geographical diversity are proposed. Firstly, SBSs that possess similar historical content requests is identified by similarity measurements. Then the probabilities of future requests are predicted for each similar SBS group utilizing linear regression method. Based on this local popularity, the sub-optimal content placement decision is made according to stochastic geometry and convex optimization. Thereafter, real data sets to verify our prediction algorithm and investigate system performance are used. It is shown that the proposed scheme outperforms the comparison schemes in terms of hit ratio.

Key words: content placement, local popularity prediction, cosine similarity

CLC Number:

TN929.53

REN Jia-zhi, TIAN Hui, NIE Gao-feng. Proactive Caching Scheme with Local Content Popularity Prediction[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 80-91.

References

[1] Cisco. Cisco visual networking index:global mobile data traffic forecast update, 2016-2021, White Paper[R]. San Jose:Cisco, 2017.
[2] Wang Xiaofei, Chen Min, Taleb T, et al. Cache in the air:exploiting content caching and delivery techniques for 5G systems[J]. IEEE Communications Magazine, 2014, 52(2):131-139.
[3] Liu Dong, Chen Binqiang, Yang Chenyang, et al. Caching at the wireless edge:design aspects, challenges, and future directions[J]. IEEE Communications Magazine, 2016, 54(9):22-28.
[4] Maddah-Ali M A, Niesen U. Fundamental limits of caching[J]. IEEE Transactions on Information Theory, 2014, 60(5):2856-2867.
[5] Wen Juan, Huang Kaibin, Yang Sheng, et al. Cache-enabled heterogeneous cellular networks:optimal tier-level content placement[J]. IEEE Transactions on Wireless Communications, 2017, 16(9):5939-5952.
[6] Liu Dong, Yang Chenyang. Caching policy toward maximal success probability and area spectral efficiency of cache-enabled HetNets[J]. IEEE Transactions on Communications, 2017, 65(6):2699-2714.
[7] Chen Min, Hao Yixue, Hu Long, et al. Green and mobility-aware caching in 5G networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(12):8347-8361.
[8] Kiskani M K, Sadjadpour H R. Throughput analysis of decentralized coded content caching in cellular networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(1):663-672.
[9] Liu Tingting, Li Jun, Shu Feng, et al. Design of contract-based trading mechanism for a small-cell caching system[J]. IEEE Transactions on Wireless Communications, 2017, 16(10):6602-6617.
[10] Shi Fang, Tan Weiqiang, Xia Junjuan, et al. Hybrid content placement for physical-layer security in cooperative networks[J]. IEEE Access, 2018, 6:8098-8108.
[11] Chen Youjia, Ding Ming, Li Jun, et al. Probabilistic small-cell caching:performance analysis and optimization[J]. IEEE Transactions on Vehicular Technology, 2017, 66(5):4341-4354.
[12] Zeng Ming, Lin T H, Chen Min, et al. Temporal-spatial mobile application usage understanding and popularity prediction for edge caching[J]. IEEE Wireless Communications, 2018, 25(3):36-42.
[13] Wang Yanfeng, Ding Mingyang, Chen Zhiyong, et al. Caching placement with recommendation systems for cache-enabled mobile social networks[J]. IEEE Communications Letters, 2017, 21(10):2266-2269.
[14] He Shuo, Tian Hui, Lyu Xinchen. Edge popularity prediction based on social-driven propagation dynamics[J]. IEEE Communications Letters, 2017, 21(5):1027-1030.
[15] Traverso S, Ahmed M, Garetto M, et al. Temporal locality in today's content caching:why it matters and how to model it[J]. ACM SIGCOMM Computer Communication Review, 2013, 43(5):6-12.
[16] Szabo G, Huberman B A. Predicting the popularity of online content[J]. Communications of the ACM, 2010, 53(8):80-88.
[17] Harper F M, Konstan J A. The MovieLens datasets:history and context[J]. ACM Transactions on Interactive Intelligent Systems (TiiS), 2016, 5(4):1-19.
[18] Blaszczyszyn B, Giovanidis A. Optimal geographic caching in cellular networks[C]//2015 IEEE International Conference on Communications (ICC). New York:IEEE Press, 2015:15437268.

Proactive Caching Scheme with Local Content Popularity Prediction

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