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Controller deployment based on network partition and collaborative scheduling on MEC

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Abstract

In software defined networks (SDN), the controller deployment solution directly affects the service quality of SDN. A multi-controller deployment algorithm based on network partition (MCDNP) is proposed by integrating the idea of a genetic algorithm with a time latency to be the optimization target, which divides the network firstly, then calculates the web's time latency, and finally decides the deployment location. Experiments based on OS3E networks show that the MCDNP algorithm effectively reduces the latency. In addition, for the collaborative task scheduling problem based on task dependency relationship, this paper also proposes a collaborative task scheduling algorithm based on dependency relationship (CTSSR) in which the optimization goal is to minimize the mean power loss of tasks while guaranteeing the system QoS. The CTSSR algorithm considers the task dependency model to ensure the dependency constraints in task execution. The CTSSR algorithm prioritizes the tasks with the shortest scheduling time and the dependency constraints for scheduling and considers the power usage of the edge server during scheduling. The experiment indicates that the CTSSR algorithm effectively decreases the duration for task completion and the energy consumption of the edge base station.

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References

  1. Kekki, S., Featherstone, W., Fang, Y., et al.: MEC in 5G networks. ETSI White Pap. 2018(28), 1–28 (2018)

    Google Scholar 

  2. Li, C., Cai, Q., Youlong, L.: Low-latency edge cooperation caching based on base station cooperation in SDN based MEC. Expert Syst. Appl. 191, 116252 (2022)

    Article  Google Scholar 

  3. Ranaweera, P., Jurcut, A., Liyanage, M.: MEC-enabled 5G use cases: a survey on security vulnerabilities and countermeasures. ACM Comput. Surv. 54(9), 1–37 (2021)

    Article  Google Scholar 

  4. Li, C., Zhang, Y., Gao, X., Luo, Y.: Energy-latency tradeoffs for edge caching and dynamic service migration based on DQN in mobile edge computing. J. Parallel Distrib. Comput. 166, 15–31 (2022)

    Article  Google Scholar 

  5. Sezer, S., Scott-Hayward, S., Chouhan, P.K., et al.: Are we ready for SDN? Implementation challenges for software-defined networks. IEEE Commun. Mag. 51(7), 36–43 (2013)

    Article  Google Scholar 

  6. Balasubramanian, V., Aloqaily, M., Reisslein, M.: An SDN architecture for time sensitive industrial IoT. Comput. Netw. 186, 107739 (2021)

    Article  Google Scholar 

  7. Hu, T., Yi, P., Zhang, J., et al.: Reliable and load balance-aware multi-controller deployment in SDN. China Commun. 15(11), 184–198 (2018)

    Article  Google Scholar 

  8. Xu, J., Wang, L., Song, C., Xu, Z.: Minimizing multi-controller deployment cost in software-defined networking. In: 2019 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6 (2019). https://doi.org/10.1109/ISCC47284.2019.8969671.

  9. Li, G., Wang, X., Zhang, Z.: SDN-based load balancing scheme for multi-controller deployment. IEEE Access 7, 39612–39622 (2019). https://doi.org/10.1109/ACCESS.2019.2906683

    Article  Google Scholar 

  10. Hou, X., Muqing, W., Bo, L., et al.: Multi-controller deployment algorithm in hierarchical architecture for SDWAN. IEEE Access 7, 65839–65851 (2019)

    Article  Google Scholar 

  11. Liao, Z., Chen, C., Ju, Y., et al.: Multi-controller deployment in SDN-enabled 6G space–air–ground integrated network. Remote Sens. 14(5), 1076 (2022)

    Article  Google Scholar 

  12. Firouz, N., Masdari, M., Sangar, A.B., et al.: A novel controller placement algorithm based on network portioning concept and a hybrid discrete optimization algorithm for multi-controller software-defined networks. Clust. Comput. 24, 2511–2544 (2021)

    Article  Google Scholar 

  13. Fan, Z., Yao, J., Yang, X., et al.: A multi-controller placement strategy based on delay and reliability optimization in SDN. IN: 2019 28th Wireless and Optical Communications Conference (WOCC), pp 1–5. IEEE (2019).

  14. Li, Y., Sun, W., Guan, S.: A Multi-controller deployment method based on PSO algorithm in SDN environment. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, Vol. 1, pp. 351–355 (2020)

  15. Wei, D., Wei, N., Yang, L., et al.: SDN-based multi-controller optimization deployment strategy for satellite network. In: 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), pp 467–473. IEEE (2020).

  16. Fang, M., Wang, Y., Ye, M.: A multi-controller deployment method of SDN network based on FPGA. In: 2019 15th International Conference on Computational Intelligence and Security (CIS), pp 262–266. IEEE (2019)

  17. Wan, Y., Qian, K.: A multi-controller deployment method for satellite networks based on network delay. In: 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 1055–1059 (2022). https://doi.org/10.1109/ICSP54964.2022.9778556.

  18. Qu, H., Xu, X., Zhao, J., Yue, P.: An SDN-based space-air-ground integrated network architecture and controller deployment strategy. In: 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET), pp. 138–142 (2020). https://doi.org/10.1109/CCET50901.2020.9213109.

  19. Li, C., Cai, Q., Youlong, L.: Optimal data placement strategy considering capacity limitation and load balancing in geographically distributed cloud. Futur. Gener. Comput. Syst. 127, 142–159 (2022)

    Article  Google Scholar 

  20. Siddesha, K., Jayaramaiah, G.V., Singh, C.: A novel deep reinforcement learning scheme for task scheduling in cloud computing. Clust. Comput. 25, 4171–4188 (2022)

    Article  Google Scholar 

  21. Menouer, T., Khedimi, A., Cérin, C., Chahbar, M.: Scheduling service function chains with dependencies in the cloud. In: 2020 IEEE 9th International Conference on Cloud Networking (CloudNet), pp. 1–3 (2020). https://doi.org/10.1109/CloudNet51028.2020.9335790.

  22. Javanmardi, S., Shojafar, M., Mohammadi, R., et al.: FUPE: a security driven task scheduling approach for SDN-based IoT–Fog networks. J. Inf. Secur. Appl. 60, 102853 (2021)

    Google Scholar 

  23. Shang, F., Chen, X., Yan, C., et al.: The bandwidth-aware backup task scheduling strategy using SDN in Hadoop. Clust. Comput. 22(3), 5975–5985 (2019)

    Article  Google Scholar 

  24. Lin, T.H., Chakraborty, G., Chu, H.C.: An Adaptive Task Scheduling Control Mechanism for SDN. IEICE Tech. Rep. 118(118), 53–58 (2018)

    Google Scholar 

  25. Al-Mansoori, A., Abawajy, J., Chowdhury, M.: BDSP in the cloud: scheduling and load balancing utlizing SDN and CEP. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE, pp. 827–835 (2020)

  26. Xie, Z., Song, X., Cao, J., et al.: Energy efficiency task scheduling for battery level-aware mobile edge computing in heterogeneous networks. ETRI J. (2022). https://doi.org/10.4218/etrij.2021-0312

    Article  Google Scholar 

  27. Heller, B., Sherwood, R., McKeown, N.: The controller placement problem. ACM SIGCOMM Comput. Commun. Rev. 42(4), 473–478 (2012)

    Article  Google Scholar 

  28. Shuai, L., Hua, W., Yi, S., et al.: NCPSO: A solution of the controller placement problem in software defined networks. In: International Conference on Algorithms and Architectures for Parallel Processing. Springer, New York (2015).

  29. Ahmadi, V., Khorramizadeh, M.: An adaptive heuristic for multi-objective controller placement in software-defined networks. Comput. Electr. Eng. 66, 204–28 (2017)

    Article  Google Scholar 

  30. Srichandan, S., Kumar, T.A., Bibhudatta, S.: Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Comput. Inform. J. 3(2), 210–30 (2018)

    Article  Google Scholar 

  31. Yi, Na., Jianjun, Xu., Yan, L., Huang, L.: Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm. Futur. Gener. Comput. Syst. 109, 134–148 (2020)

    Article  Google Scholar 

  32. Liu, J., Zhang, L., Li, C., Bai, J., Lv, H., Lv, Z.: Blockchain-based secure communication of intelligent transportation digital twins system. IEEE Trans. Intell. Transport. Syst. 23(11), 22630–22640 (2022)

    Article  Google Scholar 

  33. Liu, J., Li, C., Bai, J., Luo, Y., Lv, H., Lv, Z.: Security in IoT-enabled digital twins of maritime transportation systems. IEEE Trans. Intell. Transportation Syst. (2021). https://doi.org/10.1109/TITS.2021.3122566

    Article  Google Scholar 

  34. Li, C., Zhang, Y., Luo, Y.: Intermediate data placement and cache replacement strategy under Spark platform. J. Parallel Distrib. Comput. 163, 114–135 (2022)

    Article  Google Scholar 

  35. Li, C., Liu, J., Wang, M., Luo, Y.: Fault-tolerant scheduling and data placement for scientific workflow processing in geo-distributed clouds. J. Syst. Softw. 187, 111227 (2022)

    Article  Google Scholar 

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Acknowledgements

The work was supported by Open project of Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application,Ministry of Natural Resources (KFJJ20220203), Electronic Commerce Fujian University Application Technology Engineering Center (DZSW22-02) .Open project of Jiangsu Wind Power Engineering Technology Center(ZK22-03-03).

Funding

The work was supported by Open project of Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, Ministry of Natural Resources (KFJJ20220203), Electronic Commerce Fujian University Application Technology Engineering Center (DZSW22-02).Open project of Jiangsu Wind Power Engineering Technology Center(ZK22-03-03).

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Authors and Affiliations

  1. Electronic Commerce Fujian University Application Technology Engineering Center, Quanzhou Vocational and Technical University, Quanzhou, People’s Republic of China

    Chunlin Li & Youlong Luo

  2. Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, Ministry of Natural Resources, Zhangzhou, 363000, People’s Republic of China

    Chunlin Li

  3. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li, Yong Zhang & Youlong Luo

Authors
  1. Chunlin Li
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  2. Yong Zhang
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Contributions

CL, YZ, YL designed the study, developed the methodology, performed the analysis, and wrote the manuscript. CL, Z collected the data.

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Correspondence to Chunlin Li.

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Li, C., Zhang, Y. & Luo, Y. Controller deployment based on network partition and collaborative scheduling on MEC. Cluster Comput 26, 4085–4099 (2023). https://doi.org/10.1007/s10586-022-03822-w

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  • DOI: https://doi.org/10.1007/s10586-022-03822-w

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