Abstract
The paper focused on cache optimization and service selection algorithms in the cloud-edge environment. In order to solve the problem of cached content in edge servers, factors such as energy consumption and cost are considered, and finally a cache optimization model based on popularity was proposed. As for service selection, a Q-learning-based service selection algorithm is proposed to address the problems of dynamic task allocation and the optimization of Qos in the edge computing environment. The experimental results show that the proposed cache optimization and service selection algorithms in cloud-edge environment can better improve the cache hit ratio, minimize the transmission overhead, and ensure the server load balancing in the cloud-edge environment.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Chen, W., & Poor, H. V. (2017). Content pushing with request delay information. IEEE Transactions on Communications, 65(3), 1146–1161.
Cheng, Y., Li, X., (2020). A Compute-intensive service migration strategy based on deep reinforcement learning algorithm.In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1385-1388.
Duan, J., Ren, K., Zhou, W., Xu, Y., Dou, W. (2021). A service migration method for resource competition in mobile edge computing. In: 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), pp. 1–8.
Gao, Z., Jiao, Q., Xiao, K., Wang, Q., Mo, Z., & Yang, Y. (2019). Deep reinforcement learning based service migration strategy for edge computing. IEEE International Conference on Service-Oriented System Engineering (SOSE), 2019, 116–1165.
Hasslinger, G., Heikkinen, J., Ntougias, K. et al., (2018). Optimum caching versus LRU and LFU: comparison and combined limited look-ahead strategies. In: 2018 16th International Symposium on Modeling and Optimization in Mobile,Ad Hoc, and Wireless Networks (WiOpt), Shanghai, pp. 1–6
He, W., Su, Y., Huang L., Zhao, Y. (2018). Research on streaming media cache optimization based on mobile edge computing. In: 2018 13th International Conference on Computer Science & Education (ICCSE), pp. 1–6.
Huang, X., Zhang, Q. (2021). Reinforcement learning based service migration strategy to minimize service cost with delay constraint in edge computing. In: 2021 7th International Conference on Computer and Communications (ICCC), pp. 1341–1348.
Kang, G., Liu, J., Cao, B., & Xiao, Y. (2020). Diversified QoS-centric service recommendation for uncertain QoS preferences. IEEE International Conference on Services Computing (SCC), 2020, 288–295.
Li, L., Chan, C. A., Erfani, S., & Leckie, C. (2019). Adaptive edge caching based on popularity and prediction for mobile networks. International Joint Conference on Neural Networks (IJCNN), 2019, 1–10.
Li, C., Xiao, Y., Tu, Z., Chu, D., Wang, C., & Wang, L. (2021). A fast real-time qos-aware service selection algorithm. IEEE World Congress on Services (SERVICES), 2021, 72–77.
Li, C., Zhang, Y., Gao, X., et al. (2022). Energy-latency tradeoffs for edge caching and dynamic service migration based on DQN in mobile edge computing. Journal of Parallel and Distributed Computing, 166, 15–31.
Li, C., Liu, J., Wang, M., et al. (2022). Fault-tolerant scheduling and data placement for scientific workflow processing in geo-distributed clouds[J]. Journal of Systems and Software, 187, 111227.
Li, C., Qianqian, C., & Luo, Y. (2022). Low-latency edge cooperation caching based on base station cooperation in SDN based MEC[J]. Expert Systems with Applications, 191, 116252.
Li, C., Zhang, Y., & Luo, Y. (2022). Intermediate data placement and cache replacement strategy under Spark platform. Journal of Parallel and Distributed Computing, 163, 114–135.
Li, C., Liang, S. Y., Zhang, J., et al. (2022). Blockchain-based data trading in edge-cloud computing environment. Information Processing and Management, 59(1), 102786.
Liu, Y., He, Q., Zheng, D., Zhang, M., Chen, F., & Zhang, B. (2019). Data caching optimization in the edge computing environment. IEEE International Conference on Web Services (ICWS), 2019, 99–106.
Liu, Y., et al. (2021). QoE-aware data caching optimization with budget in edge computing. IEEE International Conference on Web Services (ICWS), 2021, 324–334.
Ma, L., Yi, S., Li, Q. (2017). Efficient service handoff across edge servers via docker container migration. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing. California, pp. 1–13.
Mehrizi, S., Tsakmalis, A., Chatzinotas S., Ottersten, B. (2019). A feature-based Bayesian method for content popularity prediction in edge-caching networks. In: Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), pp. 1–6, May 2019.
Nakayama, H., Ata, S., Oka, I. (2015). Caching algorithm for content-oriented networks using prediction of popularity of contents. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), Ottawa, pp. 1171–1176.
Oikonomou E., Rouskas, A. (2020). Selection of service nodes in edge computing environments. In: 2020 7th international conference on internet of things: systems, management and security (IOTSMS), pp. 1–6
Ren, D. , Gui, X., Lu, W. et al. (2018). GHCC: Grouping-based and hierarchical collaborative caching for mobile edge computing. In: 2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE.
Wang, Y., Zhou, N., Lang H., Li, Y. (2021). An optimal composite service selection model based on edge-cloud collaboration. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1170–1175.
Wu, H., et al. (2019). Mobility-aware service selection in mobile edge computing systems. IEEE International Conference on Web Services (ICWS), 2019, 201–208.
Xia, X., Chen, F , Cui, G., Abdelrazek, M., Grundy, J., Jin, H. et al., (2020). Budgeted data caching based on k-median in mobile edge computing. In: 2020 IEEE International Conference on Web Services (ICWS), pp. 197–206.
Yang W., Deng, F. (2020). A Service selection method based on QoS in IOT. In: 2020 5th International Conference on Computer and Communication Systems (ICCCS), pp. 791–795
Zhang, N., Zheng K., Tao M. (2018). Using grouped linear prediction and accelerated reinforcement learning for online content caching. In: Proceedingsof th IEEE International Conference on Communications Workshops (ICC Workshops’18), pp. 1–6.
Zhang, Y., Wu, L., He, Q., Chen, F., Deng, S., Yang, Y. (2019). Diversified quality centric service recommendation. In: IEEE International Conference on Web Services, pp. 126–133.
Acknowledgements
The work was supported by Open Fund of Fujian Key Laboratory of Data Science and Statistics (Minnan Normal University) (No. 2020L0708), Open Fund of Fujian Key Laboratory of Energy Measurement (Fujian Metrology Institute) (NYJL-KFKT-2021-04), any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wu, M., Guo, J., Li, C. et al. Cost-efficient edge caching and Q-learning-based service selection policies in MEC. Wireless Netw 29, 285–301 (2023). https://doi.org/10.1007/s11276-022-03102-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-022-03102-w