Energy-Efficient Cloud-Edge Collaborative Computing: Joint Task Offloading, Resource Allocation, and Service Caching
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5675-9_25
Reference15 articles.
1. Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J.C.S., Dustdar, S., Liu, J.: Task cooffloading for d2d-assisted mobile edge computing in industrial internet of things. IEEE Trans. Ind. Inf. 19(1), 480–490 (2023). https://doi.org/10.1109/TII.2022.3158974
2. Feng, H., Guo, S., Yang, L., Yang, Y.: Collaborative data caching and computation offloading for multi-service mobile edge computing. IEEE Trans. Veh. Technol. 70(9), 9408–9422 (2021). https://doi.org/10.1109/TVT.2021.3099303
3. Lu, L., Yu, J., Du, H., Li, X.: A3c-based load-balancing solution for computation offloading in sdn-enabled vehicular edge computing networks. PEER-TOPEER NETWORKING AND APPLICATIONS (2023)
4. Qian, L., Wu, Y., Jiang, F., Yu, N., Lu, W., Lin, B.: Noma assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial internet of things. IEEE Trans. Industr. Inf. 17(8), 5688–5698 (2021)
5. Ren, D., Gui, X., Zhang, K.: Adaptive request scheduling and service caching for mec-assisted iot networks: An online learning approach. IEEE Internet Things J. 9(18), 17372–17386 (2022)
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