Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2021, 4(4); doi: 10.25236/AJCIS.2021.040412.

Edge Caching Strategy Based on User Preference and Game Theory

Author(s)

Xichen Jia

Corresponding Author:
Xichen Jia
Affiliation(s)

Jiangxi Normal University, Nanchang, Jiangxi, 330022, China

Abstract

Caching video content to the mobile edge server is an effective solution to avoid multiple repeated requests from mobile terminal devices, reduce latency costs, and improve user QoE. In addition, mobile users in nearby areas tend to request the same video resource task, so reasonable deployment of video content to edge servers can effectively reduce the response time of user requests. This fact prompted us to design a caching strategy based on user preferences. The system model considered in this article contains multiple mobile users, multiple servers, and remote central servers. Based on the recommendation system predicting the user’s preference for specific video resources, the recommended value ranking of the video resources in the future time period is obtained, and then based on the game theory method, in each edge cache server, each video resource is calculated for the local area and neighboring areas. The cache value of the area, based on the cache value of the video resource to be cached, minimizes the delay for users to obtain the video resource, and obtains the optimal video resource cache distribution strategy. The simulation experiment results show that compared with other caching strategies, the proposed caching strategy is better than other caching strategies in terms of response time and cache task hit rate.

Keywords

Edge caching, Data Caching, Caching Value, Game Theory, Recommendation System

Cite This Paper

Xichen Jia. Edge Caching Strategy Based on User Preference and Game Theory. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 4: 64-68. https://doi.org/10.25236/AJCIS.2021.040412.

References

[1] Zeydan E, Bastug E, Bennis M, et al. Big data caching for networking: Moving from cloud to edge [J]. IEEE Communications Magazine, 2016, 54(9): 36-42. 

[2] Jošilo S, Pacifici V, Dán G. Distributed algorithms for content placement in hierarchical cache networks [J]. Computer Networks, 2017, 125: 160-171. 

[3] Javedankherad M, Zeinalpour-Yazdi Z, Ashtiani F. Cache placement phase based on graph coloring[C]2018 9th International Symposium on Telecommunications (IST). IEEE, 2018: 187-191. 

[4] Sun Y, Chen Z, Liu H. Delay analysis and optimization in cache-enabled multi-cell cooperative networks [C] 2016 IEEE Global Communications Conference (GLOBECOM). IEEE, 2016: 1-7. 

[5] Zhang K, Leng S, He Y, et al. Cooperative content caching in 5G networks with mobile edge computing [J]. IEEE Wireless Communications, 2018, 25(3): 80-87.

[6] Ren D, Gui X, Lu W, et al. Ghcc: Grouping-based and hierarchical collaborative caching for mobile edge computing [C] 2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, 2018: 1-6. 

[7] Tran T X, Hajisami A, Pompili D. Cooperative hierarchical caching in 5G cloud radio access networks [J]. IEEE Network, 2017, 31(4): 35-41. 

[8] Wang Y, Ding M, Chen Z, et al. Caching placement with recommendation systems for cache-enabled mobile social networks [J]. IEEE Communications Letters, 2017, 21(10): 2266-2269.

[9] Bastug E, Bennis M, Debbah M. Living on the edge: The role of proactive caching in 5G wireless networks [J]. IEEE Communications Magazine, 2014, 52(8): 82-89. 

[10] Gu J, Wang W, Huang A, et al. Distributed cache replacement for caching-enable base stations in cellular networks [C] 2014 IEEE International Conference on Communications (ICC). IEEE, 2014: 2648-2653. 

[11] Wang S, Zhang X, Zhang Y, et al. A survey on mobile edge networks: Convergence of computing, caching and communications [J]. Ieee Access, 2017, 5: 6757-6779.

[12] Robbins H, Monro S. A stochastic approximation method [J]. The annals of mathematical statistics, 1951: 400-407. 

[13] Pantisano F, Bennis M, Saad W, et al. Cache-aware user association in backhaul-constrained small cell networks[C]2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, 2014: 37-42. 

[14] Osborne M J, Rubinstein A. A course in game theory [M]. MIT press, 1994.

[15] Chen Z, Liu Y, Zhou B, et al. Caching incentive design in wireless D2D networks: A Stackelberg game approach [C] 2016 IEEE International Conference on Communications (ICC). IEEE, 2016: 1-6.

[16] Hosny S, Alotaibi F, El Gamal H, et al. Towards a mobile content marketplace [C] 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015: 675-679.

[17] Wu D, Huang J, He J, et al. Toward cost-effective mobile video streaming via smart cache with adaptive thresholding [J]. IEEE Transactions on Broadcasting, 2015, 61(4): 639-650.

[18] Luo Z, LiWang M, Lin Z, et al. Energy-efficient caching for mobile edge computing in 5G networks [J]. Applied sciences, 2017, 7(6): 557.

[19] Tran T X, Hajisami A, Pandey P, et al. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges [J]. IEEE Communications Magazine, 2017, 55(4): 54-61.

[20] Rusek K, Chołda P. Message-passing neural networks learn little’s law [J]. IEEE Communications Letters, 2018, 23(2): 274-277.

[21] Shen F, Hamidouche K, Bastug E, et al. A stackelberg game for incentive proactive caching mechanisms in wireless networks [C] 2016 IEEE Global Communications Conference (GLOBECOM). IEEE, 2016: 1-6.

[22] Jiang Y, Chen S Z, Hu B. Stackelberg games-based distributed algorithm of pricing and resource allocation in heterogeneous wireless networks [J]. Journal of China Institute of Communications, 2013, 34(1): 61-68.

[23] Amer R, Butt M M, Bennis M, et al. Delay analysis for wireless D2D caching with inter-cluster cooperation [C] GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, 2017: 1-7. 

[24] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting [J]. The journal of machine learning research, 2014, 15(1): 1929-1958.

[25] Goodfellow I, Bengio Y, Courville A, et al. Deep learning [M]. Cambridge: MIT press, 2016.

[26] Ma N, Tian G D, Zhou X. A lip-reading recognition approach based on long short-term memory [J]. J. Univ. Chinese Acad. Sci, 2018, 35(1): 109-117.

[27] Wang X, Han Y, Wang C, et al. In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning [J]. IEEE Network, 2019, 33(5): 156-165.

[28] Ramasubbareddy S, Ramasamy S, Sahoo K S, et al. CAVMS: Application-Aware Cloudlet Adaption and VM Selection Framework for Multicloudlet Environment [J]. IEEE Systems Journal, 2020.

[29] Bastug E, Bennis M, Debbah M. Living on the edge: The role of proactive caching in 5G wireless networks [J]. IEEE Communications Magazine, 2014, 52(8): 82-89.

[30] Wang X, Chen M, 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.

[31] Su Z, Qi Q, Xu Q, et al. Incentive scheme for cyber physical social systems based on user behaviors [J]. IEEE Transactions on Emerging Topics in Computing, 2017, 8(1): 92-103.

[32] Li Y, Xiao L, Dai H, et al. Game theoretic study of protecting MIMO transmissions against smart attacks [C] 2017 IEEE International Conference on Communications (ICC). IEEE, 2017: 1-6.