Skip to main content

Advertisement

SpringerLink
Log in

An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With the development of the intelligent manufacturing, a large amount of data will be generated at the edge of the network. Industrial data have unprecedented requirements for transmission speed and quality. Edge computing makes full use of the computing power of the terminal, which realizes real-time processing of data. As the first step of the edge computing, the deployment of edge servers is the foundation and key. However, unreasonable edge server deployment strategies cannot meet the requirements of data processing in intelligent manufacturing. In this paper, we establish an edge server deployment optimization model to optimize deployment cost and load balance. Reliability is an important point in intelligent manufacturing, so we propose a fault-tolerant server deployment scheme. When the edge server fails, the fault-tolerant server can replace the fault server in time to ensure the normal workflow. To solve the above model, we propose a binary-based gray wolf genetic strategy algorithm, which improves the global optimal solution of the algorithm. Simulations reveal that up to 10.97\(\%\) of the total load can be saved by using the gray wolf genetic algorithm and an average of 16.15\(\%\) time savings can be achieved using our proposed method during hours.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Khalfi B, Hamdaoui B, Guizani M (2017) Extracting and exploiting inherent sparsity for efficient IoT 5G: challenges and potential solutions. IEEE Wirel Commun 24(5):68–73

    Article  Google Scholar 

  2. Wan J, Tang S, Li D (2018) Reconfigurable smart factory for drug packing in healthcare industry 4.0. IEEE Trans Ind Inf 15(1):507–516

    Article  Google Scholar 

  3. Wang J (2019) Research on key technologies of the fog computing in intelligent manufacturing. Eng Sci Technol Ser II 12(4):004–117

    Google Scholar 

  4. Xia M, Li T, Shu T et al (2019) A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans Ind Inf 15(6):3703–3711

    Article  Google Scholar 

  5. Aazam M, Zeadally S, Harras KA (2018) Deploying fog computing in industrial internet of things and industry 4.0. IEEE Trans Ind Inf 14(10):4674–4682

    Article  Google Scholar 

  6. Mahmud R, Toosi AN, Ramamohanarao K et al (2019) Context-aware placement of Industry 4.0 applications in fog computing environments. IEEE Trans Ind Inf 16(11):7004–7013

    Article  Google Scholar 

  7. Govindaraj K, John J P, Artemenko A, et al (2019) Smart resource planning for live migration in edge computing for industrial scenario. In: 2019 7th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).IEEE,pp 30-37

  8. Mondal S, Das G, Wong E (2018) Supporting Low-Latency Applications through Hybrid Cost-Optimised Cloudlet Placement. In: 2018 20th International Conference on Transparent Optical Networks (ICTON).IEEE,pp 1-4

  9. Ren Y, Zeng F, Li W, et al (2018) A low-cost edge server placement strategy in wireless metropolitan area networks. In: 2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE,pp 1-6

  10. Cao B, Wei Q, Lv Z et al (2020) Many-objective deployment optimization of edge devices for 5G networks. IEEE Trans Netw Sci Eng 7(4):2117–2125

    Article  MathSciNet  Google Scholar 

  11. Premsankar G, Ghaddar B, Di Francesco M, et al (2018) Efficient placement of edge computing devices for vehicular applications in smart cities. In: NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium.IEEE,pp 1-9

  12. Wang X, Ji Y, Zhang J et al (2019) Joint optimization of latency and deployment cost over TDM-PON based MEC-enabled cloud radio access networks. IEEE Access 8:681–696

    Article  Google Scholar 

  13. Jiang C, Wan J, Abbas H (2020) An edge computing node deployment method based on improved k-means clustering algorithm for smart manufacturing. IEEE Systems Journal. IEEE PP(99):1–11

  14. Lin CC, Yang JW (2018) Cost-efficient deployment of fog computing systems at logistics centers in industry 4.0. IEEE Trans Ind Inf 14(10):4603–4611

    Article  Google Scholar 

  15. Li B, Wang K, Xue D, et al (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.IEEE,pp 1169-1174

  16. Mao Y, You C, Zhang J et al (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–2358

    Article  Google Scholar 

  17. Wong E, Mondal S, Das G (2017) Latency-aware optimisation framework for cloudlet placement. In: 2017 International Conference on Transparent Optical Networks (ICTON).IEEE,pp 1-2

  18. Xu Z, Liang W, Xu W et al (2015) Efficient algorithms for capacitated cloudlet placements. IEEE Trans Parallel Distrib Syst 27(10):2866–2880

    Article  Google Scholar 

  19. Wang J, Li D, Hu M Y (2020) Fog Nodes Deployment Based on Space-Time Characteristics in Smart Factory. In: IEEE Transactions on Industrial Informatics. IEEE,PP(99):1-1

  20. Kasi SK, Kasi MK, Ali K et al (2020) Heuristic edge server placement in industrial Internet of Things and cellular networks. IEEE Internet Things J 8(13):10308–10317

    Article  Google Scholar 

  21. Rezazadeh Z, Rezaei M, Nickray M (2019) Lamp: A hybrid fog-cloud latency-aware module placement algorithm for iot applications. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI).IEEE,pp 845-850

  22. Meng J, Shi W, Tan H, et al (2017) Cloudlet placement and minimum-delay routing in cloudlet computing. In: 2017 International Conference on Big Data Computing and Communications (BIGCOM).IEEE,pp 297-304

  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(8):6077–6093

    Article  Google Scholar 

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

  25. Jia M, Liang W, Xu Z, et al (2016) Cloudlet load balancing in wireless metropolitan area networks. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications.IEEE,pp 1-9

  26. 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

  27. Thananjeyan S, Chan C A, Wong E, et al (2018) Energy-efficient mobile edge hosts for mobile edge computing system. In: 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS). IEEE, pp 1-6

  28. Deniz F, Bagci H, Korpeoglu I (2021) Energy-efficient and fault-tolerant drone-BS placement in heterogeneous wireless sensor networks. Wirel Netw 27(1):825–838

    Article  Google Scholar 

  29. Chaudhary D, Tailor A K, Sharma V P, et al (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

  30. Lu C, Xiao S, Li X et al (2016) An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Adv Eng Softw 99:161–176

    Article  Google Scholar 

  31. 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

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

  33. Ming L, Wang Y, Cheung Y M (2006) On convergence rate of a class of genetic algorithms. In: 2006 World Automation Congress. IEEE, pp 1-6

  34. Wang Z, Rajasekaran S (2019) Efficient randomized feature selection algorithms. In: 2019 IEEE International Conference on High Performance Computing and Communications. IEEE, pp 796-803

  35. 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

  36. Majeed M A M, Rao P S (2017) Optimization of CMOS analog circuits using grey wolf optimization algorithm. In: 2017 IEEE India Council International Conference (INDICON). IEEE, pp 1-6

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

Download references

Acknowledgements

This work was supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2021JQ-719), the Young Teachers Research Foundation of Xi’an University of Posts and Telecommunications, and the Special Funds for Construction of Key Disciplines in Universities in Shaanxi.

Author information

Authors and Affiliations

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

    Zhongmin Wang, Weiye Zhang & Xiaomin Jin

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

    Zhongmin Wang, Weiye Zhang & Xiaomin Jin

  3. ZTE Corporation, No.55, Hi-tech Road South, Shenzhen, 518057, China

    Yihua Huang & Chen Lu

Authors
  1. Zhongmin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiye Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaomin Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yihua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chen Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiye Zhang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Zhang, W., Jin, X. et al. An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing. J Supercomput 78, 4032–4056 (2022). https://doi.org/10.1007/s11227-021-04017-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04017-7

Keywords

Search

Navigation