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An edge server deployment approach for delay reduction and reliability enhancement in the industrial internet

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Abstract

Edge computing provides a real-time guarantee for the industrial internet. Server deployment is the first step to building an edge computing system, and the deployment strategy has a huge impact on system delay and reliability. Industrial environments place higher demands on reliability than other common environments. However, existing research on edge server deployment assumes that the server will not fail and thus rarely considers edge server reliability while reducing delay. This paper focuses on edge server deployment approach for delay reduction and reliability enhancement in the industrial internet. Firstly, a basic edge server deployment model is established to optimize server load balancing and delay. On this basis, an edge server deployment model with enhanced reliability is established. This model improves system reliability by adding fault-tolerant servers when deploying edge servers. When some edge servers in the system fail, the fault-tolerant servers will intervene in time and start working to replace the failed edge servers. To solve the optimal deployment strategy, an Improved Grey Wolf-Genetic Algorithm-based deployment algorithm is proposed. The deployment algorithm improves the traditional genetic algorithm’s performance to find the global optimal solution by improving the initialization and mutation part of the genetic algorithm. The deployment algorithm selects appropriate wireless access points (WAPs) to deploy fault-tolerant edge servers among a large number of WAPs. Simulation results show that the established fault-tolerant model can significantly improve system reliability while reducing system delay. The proposed algorithm performs better compared with existing algorithms in terms of convergence speed and finding the optimal deployment location.

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References

  1. Li, J., Yu, F., Deng, G., Luo, C., Ming, Z., & Yan, Q. (2017). Industrial internet: A survey on the enabling technologies, applications, and challenges. IEEE Communications Surveys and Tutorials, 19(3), 1504–1526.

    Article  Google Scholar 

  2. Jin, X., Hua, W., Wang, Z., & Chen, Y. (2022). A survey of research on computation offloading in mobile cloud computing. Wireless Networks, 28(4), 1563–1585.

    Article  Google Scholar 

  3. Serror, M., Hack, S., Henze, M., Schuba, M., & Wehrle, K. (2021). Challenges and opportunities in securing the industrial internet of things. IEEE Transactions on Industrial Informatics, 17(5), 2985–2996.

    Article  Google Scholar 

  4. Wang, Z., Gao, F., & Jin, X. (2020). Optimal deployment of cloudlets based on cost and latency in Internet of Things networks. Wireless Network, 26(8), 6077–6093.

    Article  Google Scholar 

  5. Gedeon, J., Stein, M., Krisztinkovics, J., Felka, P., Keller, K., Meurisch, C., Wang, L., & Mühlhäuser, M. (2018). From cell towers to smart street lamps: Placing cloudlets on existing urban infrastructures. In 3rd ACM/IEEE Symposium on Edge Computing (SEC 2018), IEEE (pp. 182–202).

  6. Yin, H., Zhang, X., Liu, H., Luo, Y., Tian, C., Zhao, S., & Li, F. (2017). Edge provisioning with flexible server placement. IEEE Transactions on Parallel and Distributed Systems, 28(4), 1031–1045.

    Article  Google Scholar 

  7. Bouet, M., & Conan, V. (2018). Mobile edge computing resources optimization: A geo-clustering approach. IEEE Transactions on Network and Service Management, 15(2), 787–796.

    Article  Google Scholar 

  8. Jiao, J., Chen, L., Hong, X., & Shi, J. (2017). A heuristic algorithm for optimal facility placement in mobile edge networks. KSII Transactions on Internet and Information Systems (TIIS), 11(7), 3329–3350.

    Google Scholar 

  9. Farahani, R., & Hekmatfar, M. (Eds.). (2009). Facility location: Concepts, models, algorithms and case studies. Berlin: Springer.

    Google Scholar 

  10. Guo, Y., Wang, S., Zhou, A., Xu, J., Yuan, J., & Hsu, C. (2019). User allocation-aware edge cloud placement in mobile edge computing. Software Practice and Experience. https://doi.org/10.1002/spe.2685

    Article  Google Scholar 

  11. Jia, M., Cao, J., & Liang, W. (2017). Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, 5(4), 725–737.

    Article  Google Scholar 

  12. Wang, S., Zhao, Y., Xu, J., Yuan, J., & Hsu, C. (2019). Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing, 127, 160–168.

    Article  Google Scholar 

  13. Liu, J., Paul, U., Troia, S., Falowo, O., & Maier, G. (2018). K-means based spatial base station clustering for facility location problem in 5G. In Proceedings of Southern Africa Telecommunication Networks and Applications Conference, SATNAC (pp. 406–409).

  14. Mohan, N., Zavodovski, A., Zhou, P., & Kangasharju, J. (2018). Anveshak: Placing edge servers in the wild. In Proceedings of the 2018 Workshop on Mobile Edge Communications, ACM, United States (pp. 7–12).

  15. Sinky, H., Khalfi, B., Hamdaoui, B., & Rayes, A. (2019). Adaptive edge-centric cloud content placement for responsive smart cities. IEEE Network, 33(3), 177–183.

    Article  Google Scholar 

  16. Yao, H., Bai, C., Xiong, M., Zeng, D., & Fu, Z. (2017). Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing. Concurrency and Computation: Practice and Experience, 29(16), e3975.

    Article  Google Scholar 

  17. Zeng, F., Ren, Y., Deng, X., & Li, W. (2018). Cost-effective edge server placement in wireless metropolitan area networks. Sensors, 19(1), 32.

    Article  Google Scholar 

  18. Chen, L., Wu, J., Zhou, G., & Ma, L. (2018). QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks. The Journal of Supercomputing, 74(8), 4037–4059.

    Article  Google Scholar 

  19. LeyvaPupo, I., Santoyo-González, A., & Cervelló-Pastor, C. (2019). A framework for the joint placement of edge service infrastructure and user plane functions for 5G. Sensors, 19(18), 3975.

    Article  Google Scholar 

  20. Silva, D., & Fonseca, N. (2019). On the location of fog nodes in fog-cloud infrastructures. Sensors, 19(11), 2445.

    Article  Google Scholar 

  21. Meng, J., Shi, W., Tan, H., & Li, X. (2017). Cloudlet placement and minimum-delay routing in cloudlet computing. In 3rd International Conference on Big Data Computing and Communications, IEEE (pp. 297–304).

  22. Mondal, S., Das, G., & Wong, E. (2018). CCOMPASSION: A hybrid cloudlet placement framework over passive optical access networks. In IEEE INFOCOM 2018 IEEE Conference on Computer Communications (pp. 216–224).

  23. Machen, A., Wang, S., Leung, K., Ko, B., & Salonidis, T. (2018). Live service migration in mobile edge clouds. IEEE Wireless Communications, 25(1), 140–147.

    Article  Google Scholar 

  24. Cui, G., He, Q., Chen, F., Jin, H., & Yang, Y. (2020). Trading off between user coverage and network robustness for edge server placement. IEEE Transactions on Cloud Computing, 10(3), 2178–2189.

    Article  Google Scholar 

  25. Li, B., Wang, K., Xue, D., & Pei, Y. (2018). K-means based edge server deployment algorithm for edge computing environments. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) IEEE (pp. 1169–1174).

  26. Ma, L., Wu, J., Chen, L., & Liu, Z. (2017). Fast algorithms for capacitated cloudlet placements. In IEEE 21st International Conference on Computer Supported Cooperative Work in Design (pp. 439–444).

  27. Xu, Z., Liang, W., Xu, W., Jia, M., & Guo, S. (2016). Efficient algorithms for capacitated cloudlet placements. IEEE Transactions on Parallel and Distributed Systems, 27(10), 2866–2880.

    Article  Google Scholar 

  28. Li, Y., & Wang, S. (2018). An energy-aware edge server placement algorithm in mobile edge computing. In 2018 IEEE International Conference on Edge Computing (EDGE), IEEE (pp. 66–73).

  29. Fan, Q., & Ansari, N. (2017). Cost aware cloudlet placement for big data processing at the edge. In 2017 IEEE International Conference on Communications, ICC (pp. 1–6).

  30. Xun, Y., Qin, J., & Liu, J. (2021). Deep learning enhanced driving behavior evaluation based on vehicle-edge-cloud architecture. IEEE Transactions on Vehicular Technology, 70(6), 6172–6177.

    Article  Google Scholar 

  31. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    Article  Google Scholar 

  32. Bhatta, D., & Mashayekhy, L. (2019). Generalized cost-aware cloudlet placement for vehicular edge computing systems. In: 2019 IEEE International Conference on Cloud Computing Technology and Science, CloudCom (pp. 159–166).

  33. Charikar, M., Guha, S., Tardos, É., & Shmoys, D. (1999). A constantfactor approximation algorithm for the k-median problem. In Proceedings of the 31th ACM symposium on Theory of computing, ACM (pp. 1–10).

  34. Chaudhary, D., Tailor, A., & Sharma, V. (2019). HyGADE: Hybrid of genetic algorithm and differential evolution algorithm. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE (pp. 1–4).

  35. Chaudhary, D., Tailor, A., & Sharma, V. (2019). HyGADE: Hybrid of genetic algorithm and differential evolution algorithm. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE (pp. 1–4)

  36. Bharot, N., & Shukla, S. (2020). A review on task scheduling in cloud computing using parallel genetic algorithm. In 2020 International Conference on Computing and Information Technology (ICCIT-1441). IEEE (pp. 1–4).

  37. Martin, B., Marot, J., & Bourennane, S. (2018). Improved discrete grey wolf optimizer. In: 2018 European Signal Processing Conference (EUSIPCO). IEEE (pp. 494–498).

  38. Ming, L., Wang, Y., & Cheung, Y. (2006). On convergence rate of a class of genetic algorithms. In 2006 World Automation Congress. IEEE (pp. 1–6).

  39. Wang, Z., & Rajasekaran, S. (2019). Efcient randomized feature selection algorithms. In 2019 IEEE International Conference on High Performance Computing and Communications. IEEE (pp. 796–803).

  40. Kupriyashina, N, & Kupriyashin, M. (2021). Evaluating the probability of successful knapsack cipher system analysis with genetic algorithms. In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. IEEE (pp. 2372–2376).

  41. Patra, M., Patel, D., & Sahoo, B. (2020). A randomized algorithm for load balancing in containerized cloud. In 2020 International Conference on Cloud Computing, Data Science & Engineering. IEEE (pp. 410–414).

  42. Nguyen, L., Schmidt, H., Haeseler, A., & Minh, B. (2014). IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular Biology and Evolution, 32(1), 268–274.

    Article  Google Scholar 

  43. Ding, J. Y., Chiong, R., Zhang, R., et al. (2015). An improved iterated greedy algorithm with a tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604–613.

    Article  Google Scholar 

  44. Wang, K. (2020). Migration strategy of cloud collaborative computing for delay-sensitive industrial iot applications in the context of intelligent manufacturing. Computer Communications, 150, 413–420.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the the Communication Soft Science Program of Ministry of Industry and Information Technology, China (No.2022-R-43), the Natural Science Basic Research Program of Shaanxi (No.2021JQ-719), the Special Scientific Research Program of Education Department of Shaanxi (No. 22JK0562), the Youth Innovation Team of Shaanxi Universities “Industial Big Data Analysis and Intelligent Processing”, and the Special Funds for Construction of Key Disciplines in Universities in Shaanxi.

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Author notes

  1. Yichi Zhou, Xiaomin Jin and Yanping Chen have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang, Yichi Zhou, Xiaomin Jin & Yanping Chen

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang, Yichi Zhou, Xiaomin Jin & Yanping Chen

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang, Yichi Zhou, Xiaomin Jin & Yanping Chen

  4. Shenzhen Institute of Information Technology, Shenzhen, 518172, Guangdong, China

    Chen Lu

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Wang, Z., Zhou, Y., Jin, X. et al. An edge server deployment approach for delay reduction and reliability enhancement in the industrial internet. Wireless Netw 30, 5743–5757 (2024). https://doi.org/10.1007/s11276-023-03339-z

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