Burst load scheduling latency optimization through collaborative content caching in edge-cloud computing (original) (raw)

References

  1. Gharehpasha, S., Masdari, M., Jafarian, A.: Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm. Artif. Intell. Rev. 54(3), 2221–2257 (2021). https://doi.org/10.1007/s10462-020-09903-9
    Article Google Scholar
  2. Yang, Y., Wang, A., Tan, H., Yue, W., Xin, A.: Output consensus of general linear networked multiagent systems with unknown disturbances via cloud computing. IEEE Syst. J. 16(4), 5639–5650 (2022). https://doi.org/10.1109/JSYST.2021.3138930
    Article Google Scholar
  3. Zhao, F., Chen, Y., Yongchao Liu, Z., Chen, X.: Dynamic offloading and resource scheduling for mobile-edge computing with energy harvesting devices. IEEE Trans. Netw. Serv. Manag. 18(2), 2154–2165 (2021). https://doi.org/10.1109/TNSM.2021.3069993
    Article Google Scholar
  4. Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017). https://doi.org/10.1109/COMST.2017.2745201
    Article Google Scholar
  5. Li, h, Li, X., Xiong, Q., Wen, J., Cheng, L., Xing, B.: Edge computing for the industrial internet: architecture, applications and challenges. Comput. Sci. 48(1), 1–10 (2021). https://doi.org/10.11896/jsjkx.200900150
    Article Google Scholar
  6. Wang, S., Zhang, X., Yan, Z., Wang, W.: Cooperative edge computing with sleep control under nonuniform traffic in mobile edge networks. IEEE Internet Things J. 6(3), 4295–4306 (2019). https://doi.org/10.1109/JIOT.2018.2875939
    Article Google Scholar
  7. Xia, X., Chen, F., He, Q., Cui, G., Grundy, J.C.: Data, user and power allocations for caching in multi-access edge computing. IEEE Trans. Parallel Distrib. Syst. 33(5), 1144–1155 (2022). https://doi.org/10.1109/TPDS.2021.3104241
    Article Google Scholar
  8. Gharaibeh, A., Khreishah, A., Ji, B., Ayyash, M.: A provably efficient online collaborative caching algorithm for multicell-coordinated systems. IEEE Trans. Mob. Comput. 15(8), 1863–1876 (2016). https://doi.org/10.1109/TMC.2015.2474364
    Article Google Scholar
  9. Chousainov, I.-A., Moscholios, I., Sarigiannidis, P., Kaloxylos, A., Logothetis, M.: An analytical framework of a C-RAN supporting bursty traffic. In: IEEE International Conference on Communications 2020 (2020-June). https://doi.org/10.1109/ICC40277.2020.9149219
  10. Chien, W.C., Lai, C.F., Chao, H.C.: Dynamic resource prediction and allocation in c-ran with edge artificial intelligence. IEEE Trans. Ind. Inf. 15(7), 4306–4314 (2019). https://doi.org/10.1109/TII.2019.2913169
    Article Google Scholar
  11. Liu F, Zhang Z, Xing Y: Ecc: Edge collaborative caching strategy for differentiated services load-balancing. Computers, Materials & Continua. 69(2), 2045-2059 (2021). https://doi.org/10.32604/cmc.2021.018303
  12. Ioannou, A., Weber, S.: A survey of caching policies and forwarding mechanisms in information-centric networking. IEEE Commun. Surv. Tutor. 18(4), 2847–2886 (2016). https://doi.org/10.1109/10.1109/COMST.2016.2565541
    Article Google Scholar
  13. Khanal, S., Thar, K., Huh, E.-N.: Dcol: Distributed collaborative learning for proactive content caching at edge networks. IEEE Access 9, 73495–73505 (2021). https://doi.org/10.1109/ACCESS.2021.3080512
    Article Google Scholar
  14. Chunlin, L., Yong, Z., Qinqin, S., Youlong, L.: Collaborative caching strategy based on optimization of latency and energy consumption in mec. Knowl.-Based Syst. 233, 107523 (2021). https://doi.org/10.1016/j.knosys.2021.107523
    Article Google Scholar
  15. Wang, H.: Burst load frequency prediction based on google cloud platform server. IEEE Trans. Cloud Comput. (2024). https://doi.org/10.1109/TCC.2024.3449884
    Article Google Scholar
  16. Xu, J., Yu, H., Fan, G., Zhang, J., Zengpeng Li, Q.T.: Adaptive edge service deployment in burst load scenarios using deep reinforcement learning. J. Supercomput. 80(4), 5446–5471 (2024). https://doi.org/10.1007/s11227-023-05656-8
    Article Google Scholar
  17. Ataie, I., Taami, T., Azizi, S., Mainuddin, M., Schwartz, D.: \(D^{2}FO\): distributed dynamic offloading mechanism for time-sensitive tasks in fog-cloud IoT-based systems. In: 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC), pp. 360–366 (2022-November). https://doi.org/10.1109/IPCCC55026.2022.9894304
  18. Zhou, J., Tian, D., Sheng, Z., Duan, X., Shen, X.: Distributed task offloading optimization with queueing dynamics in multiagent mobile-edge computing networks. J. Supercomput. 8(15), 12311–12328 (2021). https://doi.org/10.1109/jiot.2021.3063509
    Article Google Scholar
  19. Tran-Dang, H., Kim, D.-S.: Dynamic collaborative task offloading for delay minimization in the heterogeneous fog computing systems. J. Commun. Netw. 25(2), 244–252 (2023). https://doi.org/10.23919/jcn.2023.000008
    Article Google Scholar
  20. Deng, S., Zhang, C., Li, C., Yin, J., Dustdar, S., Zomaya, A.Y.: Burst load evacuation based on dispatching and scheduling in distributed edge networks. IEEE Trans. Parallel Distrib. Syst. 32(8), 1918–1932 (2021). https://doi.org/10.1109/TPDS.2021.3052236
    Article Google Scholar
  21. Zhang, Y., Tang, B., Luo, J., Zhang, J.: Deadline-aware dynamic task scheduling in edge-cloud collaborative computing. Electronics 11(15), 2464 (2022). https://doi.org/10.3390/electronics11152464
    Article Google Scholar
  22. Ali, B., Gregory, M.A., Li, S.: Trust-aware task load balancing in multi-access edge computing based on blockchain and a zero trust security capability framework. Trans. Emerg. Telecommun. Technol. 34(12), 4845 (2023). https://doi.org/10.1002/ett.4845
    Article Google Scholar
  23. Nayyer, M.Z., Raza, I., Hussain, S.A., Jamal, M.H., Gillani, Z.: Lbro: Load balancing for resource optimization in edge computing. IEEE Access 10, 97439–97449 (2022). https://doi.org/10.1109/ACCESS.2022.3205741
    Article Google Scholar
  24. Saoud, A., Recioui, A.: Hybrid algorithm for cloud-fog system based load balancing in smart grids. Bull. Electr. Eng. Inform. 11(1), 477–487 (2022). https://doi.org/10.11591/eei.v11i1.3450
    Article Google Scholar
  25. Aghazadeh, R., Shahidinejad, A., Ghobaei-Arani, M.: Proactive content caching in edge computing environment: a review. Softw.: Pract. Exp. 53(3), 811–855 (2023). https://doi.org/10.1002/spe.3033
    Article Google Scholar
  26. Li, C., Zhang, Y., Song, M., Yan, X., Luo, Y.: An optimized content caching strategy for video stream in edge-cloud environment. J. Netw. Comput. Appl. 191, 103158 (2021). https://doi.org/10.1016/j.jnca.2021.103158
    Article Google Scholar
  27. Li, C., Zhang, Y., Sun, Q., Luo, Y.: Collaborative caching strategy based on optimization of latency and energy consumption in mec. Knowl.-Based Syst. 233, 107523 (2021). https://doi.org/10.1016/j.knosys.2021.107523
    Article Google Scholar
  28. Deka, V., Islam, A., Ghose, M.: Cloud-assisted dynamic and cooperative content caching in mobile edge computing. In: 2022 IEEE 19th India Council International Conference (INDICON) (2022). https://doi.org/10.1109/INDICON56171.2022.10039991
  29. Yasir, M., Zaman, S.K.U., Maqsood, T., Rehman, F., Mustafa, S.: Copup: content popularity and user preferences aware content caching framework in mobile edge computing. Clust. Comput. 26(1), 267–281 (2023). https://doi.org/10.1007/s10586-022-03624-0
    Article Google Scholar
  30. Liang, H., Xu, F., Anqi, F., Yupin, H., LiPing, Q.: Distributed deep learning-based offloading for mobile edge computing networks. Mob. Netw. Appl. 27(3), 1123–1130 (2022). https://doi.org/10.1007/s11036-018-1177-x
    Article Google Scholar
  31. Liu, J., Ren, J., Dai, W., Zhang, D., Zhou, P., Zhang, Y., Min, G., Najjari, N.: Online multi-workflow scheduling under uncertain task execution time in iaas clouds. IEEE Trans. Cloud Comput. 9(3), 1180–1194 (2019). https://doi.org/10.1109/TCC.2019.2906300
    Article Google Scholar
  32. Dou, W., Xu, X., Meng, S., Zhang, X., Hu, C., Yu, S., Yang, J.: An energy-aware virtual machine scheduling method for service qos enhancement in clouds over big data. Concurr. Comput.: Pract. Exp. 29(14), 3909 (2016). https://doi.org/10.1002/cpe.3909
    Article Google Scholar
  33. Wang, S., Liu, Z., Zheng, Z., Sun, Q., Yang, F.: Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In: IEEE 2013 International Conference on Parallel and Distributed Systems (ICPADS), pp. 102–109 (2013). https://doi.org/10.1109/ICPADS.2013.26
  34. Li, R., Li, Q., Zhang, Y., Zhao, D., Xiao, X., Jiang, Y.: Genos: general in-network unsupervised intrusion detection by rule extraction. In: IEEE INFOCOM 2024 - IEEE Conference on Computer Communications, pp. 561–570 (2024). https://doi.org/10.1109/INFOCOM52122.2024.10621157
  35. Li, R., Li, Q., Zhang, Y., Zhao, D., Jiang, Y., Yang, Y.: Interpreting unsupervised anomaly detection in security via rule extraction. In: Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 (2023)

Download references