Skip to main content

Optimize the placement of edge server between workload balancing and system delay in smart city

Abstract

With the advent of mobile Internet and IoT era, various smart terminals generate a large amount of data at the edge of the network, and how to transmit and process these data at high speed poses a challenge to the traditional communication networks. Edge computing, as an emerging framework, can improve the communication capability and data processing capacity of traditional communication networks by improving their architecture. Edge server placement (ESP) technology is one of the key technologies of edge computing, which can effectively reduce data transmission delay and improve data processing efficiency by placing edge servers (ESs) with computing and data storage functions at base stations to sink some functions of the core network to the edge of the network. In this paper, we study the k edge servers placement problem (KESP problem) in smart cities. We first elaborate it as a multi-objective optimization problem for optimal workload balancing and system delay under constraints. Then a modified multi-objective non-dominated sorting genetic algorithm with elite policy (MNSGA-II) is proposed to optimize this problem. Finally, simulations are performed based on real network datasets. The simulation results show that MNSGA-II reduces the system overhead by about 38.4%, 40.6%, and 59.3% on average compared to Random, K-Means, and Top-K.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Ksentini A, Iqbal M, Taleb, Tarik, et al. (2017) Mobile edge computing potential in making cities smarter. IEEE Communications Magazine Articles News & Events of Interest to Communications Engineers

  2. 2.

    Deng S, Xiang Z, Zhao P, Taheri J, Zomaya AY. (2020) Dynamical resource allocation in edge for trustable iot systems: a reinforcement learning method. IEEE Trans Industrial Informatics, PP(99), 1-1

  3. 3.

    Shi W, Jie C, Quan Z, Li Y, Xu L (2016) Edge computing: vision and challenges. Internet Things J IEEE 3(5):637–646

    Article  Google Scholar 

  4. 4.

    Xu X, Shen B, Yin X, Khosravi MR, Wan S (2020). Edge server quantification and placement for offloading social media services in industrial cognitive iov. IEEE Transactions on Industrial Informatics, PP(99), 1-1

  5. 5.

    Shi W, Sun H, Cao J, Zhang Q, Liu W (2017) Edge computing-an emerging computing model for the internet of everything era. J Comput Res Develop

  6. 6.

    Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81

    Article  Google Scholar 

  7. 7.

    Ning Z, Dong P, Wang X, Wang S, Kwok R (2020) Distributed and dynamic service placement in pervasive edge computing networks. IEEE Trans Parallel Distrib Syst PP(99)

  8. 8.

    Ahmed A, Ahmed E (2016) A survey on Mobile edge computing. International Conference on Intelligent Systems & Control. IEEE

  9. 9.

    Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Communications Surveys & Tutorials, PP(99), 1-1

  10. 10.

    Wang S , Zhang X, Zhang Y, Wang L, Yang J, Wang W (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access, PP(99), 1-1

  11. 11.

    Satyanarayanan M, Bahl P, Ca Ceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

    Article  Google Scholar 

  12. 12.

    Pang Z, Sun L, Zhi W, Tian E, Yang S (2015) A Survey of Cloudlet Based Mobile Computing. 2015 International conference on cloud computing and big data (CCBD). IEEE

  13. 13.

    Chien WC , Lai CF, Chao HC (2019) Dynamic resource prediction and allocation in c-ran with edge artificial intelligence. IEEE Transactions on Industrial Informatics, 1-1

  14. 14.

    Deng S, Zhang C, Li C, Yin J, Zomaya AY (2021) Burst load evacuation based on dispatching and scheduling in distributed edge networks. IEEE Trans Parallel Distrib Syst 32(8):1918–1932

    Article  Google Scholar 

  15. 15.

    Deng S, Xiang Z, Taheri J, Mohammad KA, Dustdar S (2020) Optimal application deployment in resource constrained distributed edges. IEEE Transactions on Mobile Computing, PP(99), 1-1

  16. 16.

    H Zhao, Deng S, Liu Z, Yin J, Dustdar S (2019) Distributed redundancy scheduling for microservice-based applications at the edge

  17. 17.

    Qiang F, Ansari N (2017) Cost Aware cloudlet Placement for big data processing at the edge. ICC 2017–2017 IEEE international conference on communications. IEEE

  18. 18.

    Ma L, Wu J, Long C, Liu Z (2017) Fast algorithms for capacitated cloudlet placements. 2017 IEEE 21st international conference on computer supported cooperative work in design (CSCWD). IEEE

  19. 19.

    Ma L, Wu J, Long C, Liu Z (2017) Fast algorithms for capacitated cloudlet placements. 2017 IEEE 21st international conference on computer supported cooperative work in design (CSCWD). IEEE

  20. 20.

    Xiang H, Xu X, Zheng H, Shu L, Shui Y (2017) An adaptive cloudlet placement method for mobile applications over GPS big data. Global Communications Conference. IEEE

  21. 21.

    Jia M, Cao J, Liang W (2017) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput (4) 1–1

  22. 22.

    Ceselli A, Premoli M, Secci S (2015) Cloudlet network design optimization. IEEE, 1–9

  23. 23.

    Wang Z, Gao F, Jin X (2020) Optimal deployment of cloudlets based on cost and latency in internet of things networks. Wirel Netw 26(5):6077–6093

    Article  Google Scholar 

  24. 24.

    Xiaoming J, Lei C, Xu, Wenzhong. (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking: A Joint Publication of the IEEE Communications Soceity, the IEEE Computer Society, and the ACM with Its Special Interest Group on Data Communication, 24(5), 2795–2808

  25. 25.

    Huang L, Bi S, Zhang Y (2018) Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks

  26. 26.

    Badri H, Bahreini T, Grosu D, Yang K (2020) Energy-aware application placement in mobile edge computing: a stochastic optimization approach. IEEE Trans Parallel Distrib Syst 31:909–922

    Article  Google Scholar 

  27. 27.

    Wang S, Zhao Y, Xu J, Jie Y, Hsu CH (2018) Edge server placement in mobile edge computing. J Parallel Distrib Comput 127

  28. 28.

    Li Y, Wang S (2018) An Energy-Aware Edge Server Placement Algorithm in Mobile Edge Computing. 2018 IEEE international conference on EDGE computing (EDGE). IEEE

  29. 29.

    Li B, Hou P, Wang K, Peng Z, Niu L (2020) Deployment of edge servers in 5g cellular networks. Trans Emerg Telecommun Technol (8)

  30. 30.

    Kleinrock L (1975) Queueing systems, volume i: theory,” pp. 101–103

  31. 31.

    Bahadir B, De K, Lastrapes WD (2020) Household debt, consumption and inequality. Working Papers

  32. 32.

    Quintela P, Barral P, Gómez D, Pena FJ, Rodríguez J, Salgado P, et al. (2017) [mathematics in industry] progress in industrial mathematics at ecmi 2016 volume 26 aerodynamic web forming: pareto-optimized mass distribution. , 10.1007/978-3-319-63082-3(Chapter 31), 207–213

  33. 33.

    Mirjalili S, Saremi S, Mirjalili SM, Coelho L (2015) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Article  Google Scholar 

  34. 34.

    Pan X, et al. (2015) A differential evolution-based hybrid NSGA-II for multi-objective optimization. IEEE International Conference on Cybernetics & Intelligent Systems IEEE

  35. 35.

    Du B, Tan T, Guo J, Li Y, Guo S (2021) Energy-cost-aware resource-constrained project scheduling for complex product system with activity splitting and recombining Expert Systems with Applications, 114754

  36. 36.

    Feng D, Qi R, Limin DU (2013) Binary cuckoo search algorithm. J Comput Appl 33(6):1566–1570

    Google Scholar 

  37. 37.

    Wagstaff K (2001) Constrained k-means clustering with background knowledge. Proceedings of ICML-2001

Download references

Acknowledgments

This work was supported in part by the foundation of Research on Theory and Control Protocol of Convergence Multiple Access Communication Network (Grant No. 61461053) and Research on Analysis and Improvement of Polling Control System in Wireless Network (Grant No. 61461054).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hongwei Ding.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Convergence of Edge Computing and Next Generation Networking

Guest Editors: Deze Zeng, Geyong Min, Qiang He, and Song Guo

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Zeng, Y., Ding, H. et al. Optimize the placement of edge server between workload balancing and system delay in smart city. Peer-to-Peer Netw. Appl. (2021). https://doi.org/10.1007/s12083-021-01208-0

Download citation

Keywords

  • Edge computing
  • Mobile edge computing
  • Edge server
  • Placement
  • NSGA-II