Abstract
Service caching is an emerging solution to addressing massive service request in a distributed environment for supporting rapidly growing services and applications. With the explosive increases in global mobile data traffic, service caching over the edge computing architecture, Mobile edge computing (MEC), emerges for alleviating traffic congestion as well as for optimizing the efficiency of task processing. In this manuscript, we propose a novel profit-driven service caching method based on a federated learning model for service prediction and a deep reinforcement learning mode for yielding caching decisions (FPDRD) in an edge environment. The proposed method is temporal service popularity and user preference-aware. It aims to ensure quality of service (QoS) of delivery of cached service while maximizing the profits of network service providers. Experimental results clearly demonstrate that the FPDRD method outperforms traditional methods in multiple aspects.
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References
Wu, C., Peng, Q., Xia, Y., Jin, Y., Hu, Z.: Towards cost-effective and robust AI microservice deployment in edge computing environments. Futur. Gener. Comput. Syst. 141, 129–142 (2023). https://doi.org/10.1016/j.future.2022.10.015
Hu, Q., Peng, Q., Shang, J., Li, Y., He, J.: EBA: an adaptive large neighborhood search-based approach for edge bandwidth allocation. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds.) CollaborateCom 2022. LNICST, vol. 460, pp. 249–268. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-24383-7-14
Cruz, P., Achir, N., Viana, A.C.: On the edge of the deployment: a survey on multi-access edge computing. ACM Comput. Surv. 55(5) (2022). https://doi.org/10.1145/3529758
Liu, G., et al.: An adaptive DNN inference acceleration framework with end-edge-cloud collaborative computing. Futur. Gener. Comput. Syst. 140, 422–435 (2023). https://doi.org/10.1016/j.future.2022.10.033
Sharghivand, N., Derakhshan, F., Mashayekhy, L., Mohammadkhanli, L.: An edge computing matching framework with guaranteed quality of service. IEEE Trans. Cloud Comput. 10(3), 1557–1570 (2022). https://doi.org/10.1109/TCC.2020.3005539
Huang, C.K., Shen, S.H.: Enabling service cache in edge clouds. ACM Trans. Internet Things 2(3) (2021). https://doi.org/10.1145/3456564
Gao, J., Kuang, Z., Gao, J., Zhao, L.: Joint offloading scheduling and resource allocation in vehicular edge computing: a two layer solution. IEEE Trans. Veh. Technol. 72(3), 3999–4009 (2023). https://doi.org/10.1109/TVT.2022.3220571
Liu, T., Zhang, Y., Zhu, Y., Tong, W., Yang, Y.: Online computation offloading and resource scheduling in mobile-edge computing. IEEE Internet Things J. 8(8), 6649–6664 (2021). https://doi.org/10.1109/JIOT.2021.3051427
Xue, Z., Liu, C., Liao, C., Han, G., Sheng, Z.: Joint service caching and computation offloading scheme based on deep reinforcement learning in vehicular edge computing systems. IEEE Trans. Veh. Technol. 72(5), 6709–6722 (2023). https://doi.org/10.1109/TVT.2023.3234336
Zong, T., Li, C., Lei, Y., Li, G., Cao, H., Liu, Y.: Cocktail edge caching: ride dynamic trends of content popularity with ensemble learning. IEEE/ACM Trans. Networking 31(1), 208–219 (2023). https://doi.org/10.1109/TNET.2022.3193680
Li, T., Li, D., Xu, Y., Wang, X., Zhang, G.: Temporal-spatial collaborative mobile edge caching with user satisfaction awareness. IEEE Trans. Netw. Sci. Eng. 9(5), 3643–3658 (2022). https://doi.org/10.1109/TNSE.2022.3188658
Li, Y., et al.: Collaborative content caching and task offloading in multi-access edge computing. IEEE Trans. Veh. Technol. 72(4), 5367–5372 (2023). https://doi.org/10.1109/TVT.2022.3222596
Li, Z., Yang, C., Huang, X., Zeng, W., Xie, S.: Coor: collaborative task offloading and service caching replacement for vehicular edge computing networks. IEEE Trans. Veh. Technol. 72(7), 9676–9681 (2023). https://doi.org/10.1109/TVT.2023.3244966
Xu, Z., et al.: Energy-aware collaborative service caching in a 5g-enabled MEC with uncertain payoffs. IEEE Trans. Commun. 70(2), 1058–1071 (2022). https://doi.org/10.1109/TCOMM.2021.3125034
Lin, C.C., Chiang, Y., Wei, H.Y.: Collaborative edge caching with multiple virtual reality service providers using coalition games. In: 2023 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2023). https://doi.org/10.1109/WCNC55385.2023.10118763
Zhou, H., Zhang, Z., Li, D., Su, Z.: Joint optimization of computing offloading and service caching in edge computing-based smart grid. IEEE Trans. Cloud Comput. 11(2), 1122–1132 (2023). https://doi.org/10.1109/TCC.2022.3163750
Ma, X., Zhou, A., Zhang, S., Wang, S.: Cooperative service caching and workload scheduling in mobile edge computing. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pp. 2076–2085 (2020). https://doi.org/10.1109/INFOCOM41043.2020.9155455
Wu, R., Tang, G., Chen, T., Guo, D., Luo, L., Kang, W.: A profit-aware coalition game for cooperative content caching at the network edge. IEEE Internet Things J. 9(2), 1361–1373 (2022). https://doi.org/10.1109/JIOT.2021.3087719
Xu, Z., et al.: Near-optimal and collaborative service caching in mobile edge clouds. IEEE Trans. Mob. Comput. 22(7), 4070–4085 (2023). https://doi.org/10.1109/TMC.2022.3144175
Li, Y., Liang, W., Li, J.: Profit driven service provisioning in edge computing via deep reinforcement learning. IEEE Trans. Netw. Serv. Manage. 19(3), 3006–3019 (2022). https://doi.org/10.1109/TNSM.2022.3159744
Wang, Z., Du, H.: Collaborative coalitions-based joint service caching and task offloading for edge networks. Theoret. Comput. Sci. 940, 52–65 (2023). https://doi.org/10.1016/j.tcs.2022.10.037
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4) (2015). https://doi.org/10.1145/2827872
Liu, Y., Jia, J., Cai, J., Huang, T.: Deep reinforcement learning for reactive content caching with predicted content popularity in three-tier wireless networks. IEEE Trans. Netw. Serv. Manage. 20(1), 486–501 (2023). https://doi.org/10.1109/TNSM.2022.3207994
Somesula, M.K., Rout, R.R., Somayajulu, D.: Greedy cooperative cache placement for mobile edge networks with user preferences prediction and adaptive clustering. Ad Hoc Netw. 140, 103051 (2023). https://doi.org/10.1016/j.adhoc.2022.103051
Acknowledgement
This work was supported in part by the Key Research and Development Project of Henan Province under Grant No. 231111211900, in part by the Henan Province Science and Technology Project under Grant No. 232102210024.
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Ouyang, Z., Xia, Y., Peng, Q., Li, Y., Chen, P., Wang, X. (2024). A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 563. Springer, Cham. https://doi.org/10.1007/978-3-031-54531-3_7
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