Abstract
In mobile edge-cloud networks, multiple edge nodes form a mesh network to cooperate with each other. To maximize the benefit of resource-limited edge nodes, the content providers jointly optimize the content caching and recommendation decisions. However, the cooperation between edge nodes complicates both the content caching and recommendation decisions. To solve this problem, in this paper, we propose an efficient joint cooperative content caching and recommendation scheme in edge-cloud networks. Specifically, we formulate the joint cooperative content caching and recommendation problem as an integer-linear programming problem to minimize the average download delay, with controllable user preference distortion tolerance. We propose an efficient heuristic algorithm to solve the formulated problem due to its NP-hardness. We evaluate the performance of the proposed scheme with the MovieLens dataset. The simulation results demonstrate that the proposed scheme can decrease the average download latency by up to 37% and improve average cache hit rate by up to 24%, as compared with state-of-the-art solutions.
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Chatzieleftheriou, L.E., Karaliopoulos, M., Koutsopoulos, I.: Caching-aware recommendations: nudging user preferences towards better caching performance. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)
Chatzieleftheriou, L.E., Karaliopoulos, M., Koutsopoulos, I.: Jointly optimizing content caching and recommendations in small cell networks. IEEE Trans. Mob. Comput. 18(1), 125–138 (2018)
Chen, N., Qiu, T., Zhou, X., Li, K., Atiquzzaman, M.: An intelligent robust networking mechanism for the internet of things. IEEE Commun. Mag. 57(11), 91–95 (2019)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A., et al.: Collaborative filtering recommender systems. Found. Trends® Hum.-Comput. Interact. 4(2), 81–173 (2011)
Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. (TMIS) 6(4), 1–19 (2015)
Guo, K., Yang, C.: Temporal-spatial recommendation for caching at base stations via deep reinforcement learning. IEEE Access 7, 58519–58532 (2019)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2016)
Jiang, W., Feng, G., Qin, S.: Optimal cooperative content caching and delivery policy for heterogeneous cellular networks. IEEE Trans. Mob. Comput. 16(5), 1382–1393 (2016)
Jiang, W., Feng, G., Qin, S., Liang, Y.C.: Learning-based cooperative content caching policy for mobile edge computing. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2019)
Li, L., Zhao, G., Blum, R.S.: A survey of caching techniques in cellular networks: research issues and challenges in content placement and delivery strategies. IEEE Commun. Surv. Tutorials 20(3), 1710–1732 (2018)
Liu, D., Yang, C.: A learning-based approach to joint content caching and recommendation at base stations. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–7. IEEE (2018)
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutorials 19(4), 2322–2358 (2017)
Palomar, D.P., Chiang, M.: A tutorial on decomposition methods for network utility maximization. IEEE J. Sel. Areas Commun. 24(8), 1439–1451 (2006)
Qiu, L., Cao, G.: Popularity-aware caching increases the capacity of wireless networks. IEEE Trans. Mob. Comput. (2019)
Qiu, T., Li, B., Zhou, X., Song, H., Lee, I., Lloret, J.: A novel shortcut addition algorithm with particle swarm for multi-sink internet of things. IEEE Trans. Ind. Inform. (2019)
Siegel, J.E., Erb, D.C., Sarma, S.E.: A survey of the connected vehicle landscape–architectures, enabling technologies, applications, and development areas. IEEE Trans. Intell. Transp. Syst. 19(8), 2391–2406 (2017)
Vazirani, V.V.: Approximation Algorithms. Springer, Heidelberg (2013)
Yang, L., Chen, Y., Li, L., Jiang, H.: Cooperative caching and delivery algorithm based on content access patterns at network edge. In: Leung, V.C.M., Zhang, H., Hu, X., Liu, Q., Liu, Z. (eds.) 5GWN 2019. LNICST, vol. 278, pp. 99–123. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17513-9_8
Yao, J., Han, T., Ansari, N.: On mobile edge caching. IEEE Commun. Surv. Tutorials 21(3), 2525–2553 (2019)
Zhong, C., Gursoy, M.C., Velipasalar, S.: Deep multi-agent reinforcement learning based cooperative edge caching in wireless networks. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2019)
Acknowledgments
This work is supported in part by National Key R&D Program of China under Grant 2018YFB1004700, in part by the National Natural Science Foundation of China under Grant No. 61702365, and also in part by the Natural Science Foundation of Tianjin under Grant No. 18ZXZNGX00040 and 18ZXJMTG00290.
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Ke, Z., Cheng, M., Zhou, X., Li, K., Qiu, T. (2020). Joint Cooperative Content Caching and Recommendation in Mobile Edge-Cloud Networks. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_31
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DOI: https://doi.org/10.1007/978-3-030-60259-8_31
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