Skip to main content
Springer Nature Link
Log in

Resource utilization and cost optimization oriented container placement for edge computing in industrial internet

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

Abstract

With the continuous evolution of the modern industrial internet in an intelligent direction, intelligent devices generate many delay-sensitive task requests. Container-based edge computing service deployment can save the bandwidth resources of the core network and reduce service delay. However, an unreasonable container deployment leads to the waste of edge server resources and fails to meet the requirements of real-time processing of services. In this paper, we establish a basic container deployment model to optimize the resource utilization and deployment cost. On this basis, we additionally establish a fault-tolerant deployment model for containers, which enables the edge computing system still provide services and improves the fault-tolerant ability of the factory when the container deployment fails or deployment speed drops caused by hardware failures. To solve the optimal deployment strategy, we propose an improved genetic-simulated annealing algorithm (IGSAA). The proposed algorithm can achieve optimal container deployment by improving initialization, crossover and mutation operations of the genetic algorithm. The simulation results show that the established model has remarkable effect in resource utilization and cost optimization. Compared with the existing deployment algorithms, IGSAA outperforms them by at least 22% in optimizing resource utilization and deployment costs.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12

Similar content being viewed by others

Abbreviations

EC:

Edge computing

MCC:

Mobile cloud computing

MECO:

Mobile edge cloud offload

IoT:

Internet of things

ECHO:

Edge cloud heuristic

VR:

Virtual reality

IGSAA:

Improved genetic-simulated annealing algorithm

DQN:

Deep Q-network

GA:

Genetic algorithm

SA:

Simulated annealing algorithm

HC:

Hill climbing algorithm

GWO:

Grey wolf optimizer

WOA:

Whale optimization algorithm

References

  1. Chen M, Li W, Fortino G et al (2019) A dynamic service migration mechanism in edge cognitive computing. ACM Trans Internet Technol 19(2):1–15

    Article  Google Scholar 

  2. Chen M, Zhou J, Tao G et al (2018) Wearable affective robot. IEEE. Access 6(1):64766–64776

    Article  Google Scholar 

  3. Jin X, Hua W, Wang Z et al (2022) A survey of research on computation offloading in mobile cloud computing. Wireless Netw 28(1):1563–1585

    Article  Google Scholar 

  4. Akbari MR, Barati H, Barati A (2022) An efficient gray system theory-based routing protocol for energy consumption management in the internet of things using fog and cloud computing. Computing 104(6):1307–1335

    Article  Google Scholar 

  5. Akbari MR, Barati H, Barati A (2022) An overlapping routing approach for sending data from things to the cloud inspired by fog technology in the large-scale IoT ecosystem. Wireless Netw 28(2):521–538

    Article  Google Scholar 

  6. Lin K, Song J, Luo J et al (2017) Green video transmission in the mobile cloud networks. IEEE Trans Circuits Syst Video Technol 27(1):159–169

    Article  Google Scholar 

  7. Lin K, Chen M, Deng J et al (2016) Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings. IEEE Trans Autom Sci Eng 13(3):1294–1307

    Article  Google Scholar 

  8. Zhang L, Ansari N (2020) Latency-aware IoT service provisioning in UAV-aided mobile-edge computing networks. IEEE Internet Things J 7(10):10573–10580

    Article  Google Scholar 

  9. He T, Khamfroush H, Wang S et al. (2018) It’s hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources. In: 2018 IEEE 38th International Conference on Distributed Computing Systems. IEEE, pp 365-375

  10. Qian Y, Jiang Y, Chen J et al (2018) Towards decentralized IoT security enhancement: a blockchain approach. Comput Elect Eng 72(1):266–273

    Article  Google Scholar 

  11. Yan C, Zhang Y, Zhong W et al (2021) A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing. Tsinghua Sci Technol 27(2):315–324

    Article  Google Scholar 

  12. Lin K, Luo J, Hu L et al (2016) Localization based on social big data analysis in the vehicular networks. IEEE Trans Industr Inf 13(4):1932–1940

    Article  Google Scholar 

  13. Yang P, Zhang N, Zhang S et al (2018) Content popularity prediction towards location-aware mobile edge caching. IEEE Trans Multimedia 21(4):915–929

    Article  Google Scholar 

  14. Sultan S, Ahmad I, Dimitriou T (2019) Container security: issues, challenges, and the road ahead. IEEE Access 7(1):52976–52996

    Article  Google Scholar 

  15. Zhang J, Zhou X, Ge T et al (2021) Joint task scheduling and containerizing for efficient edge computing. IEEE Trans Parallel Distrib Syst 32(8):2086–2100

    Article  Google Scholar 

  16. Dong L, Wu W, Guo Q et al (2019) Reliability-aware offloading and allocation in multilevel edge computing system. IEEE Trans Reliab 70(1):200–211

    Article  Google Scholar 

  17. Huang J, Liang J, Ali S (2020) A simulation-based optimization approach for reliability-aware service composition in edge computing. IEEE Access 8(1):50355–50366

    Article  Google Scholar 

  18. Lavanya S, Prasanth A, Jayachitra S et al (2021) A tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications. Measurement 183(1):109771

    Article  Google Scholar 

  19. Prasanth A (2021) Certain investigations on energy-efficient fault detection and recovery management in underwater wireless sensor networks. J Circuits, Syst Comput 30(8):2150137

    Article  Google Scholar 

  20. Bhaskar KBR, Prasanth A (2022) Saranya P (2022) An energy efficient blockchain approach for secure communication in IoT enabled electric vehicles. Int J Commun Syst 1:e5189

    Google Scholar 

  21. Prasanth A, Jayachitra S (2020) A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer-to-Peer Netw Appl 13(6):1905–1920

    Article  Google Scholar 

  22. Hao Y, Chen M, Gharavi H et al (2020) Deep reinforcement learning for edge service placement in softwarized industrial cyber-physical system. IEEE Trans Industr Inf 17(8):5552–5561

    Article  Google Scholar 

  23. Yousefpour A, Ishigaki G, Gour R et al (2018) On reducing IoT service delay via fog offloading. IEEE Internet Things J 5(2):998–1010

    Article  Google Scholar 

  24. Yousefpour A, Ishigaki G, Jue JP (2017) Fog computing: towards minimizing delay in the internet of things. In: 2017 IEEE International Conference on Edge Computing. IEEE, pp 17-24

  25. Velasquez K, Abreu DP, Curado M et al (2017) Service placement for latency reduction in the internet of things. Ann Telecommun 72(1):105–115

    Article  Google Scholar 

  26. Deng R, Lu R, Lai C et al (2015) Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing. In: 2015 IEEE International Conference on Communications. IEEE, pp 3909-3914

  27. Tao O, Zhi Z, Xu C (2018) Follow me at the edge: mobility-aware dynamic service placement for mobile edge computing. IEEE J Sel Areas Commun 36(10):2333–2345

    Article  Google Scholar 

  28. Sarkar S, Chatterjee S, Misra S (2015) Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans Cloud Comput 6(1):46–59

    Article  Google Scholar 

  29. Sarkar S, Misra S (2016) Theoretical modelling of fog computing: a green computing paradigm to support IoT applications. IET Netw 5(2):23–29

    Article  Google Scholar 

  30. Deng R, Lu R, Lai C et al (2016) Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J 3(6):1171–1181

    Google Scholar 

  31. Mahmoud MME, Rodrigues JJPC, Saleem K et al (2018) Towards energy-aware fog-enabled cloud of things for healthcare. Comput Elect Eng 67(1):58–69

    Article  Google Scholar 

  32. Hao Y, Chen M, Hu L et al (2018) Energy efficient task caching and offloading for mobile edge computing. IEEE Access 6(1):11365–11373

    Article  Google Scholar 

  33. Bahreini T, Grosu D (2017) Efficient placement of multi-component applications in edge computing systems. In: IEEE Symposium on Edge Computing. IEEE, pp 1-11

  34. Wang S, Urgaonkar R, Zafer M et al (2015) Dynamic service migration in mobile edge-clouds. In: 2015 IFIP Networking Conference. IEEE, pp 1-9

  35. Gu L, Zeng D, Guo S et al (2015) Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans Emerg Top Comput 5(1):108–119

    Article  Google Scholar 

  36. De Maio V, Brandic I (2018) First hop mobile offloading of dag computations. In: 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE, pp 83-92

  37. Dell’Amico M, Delorme M, Iori M et al (2019) Mathematical models and decomposition methods for the multiple knapsack problem. Eur J Oper Res 274(3):886–899

    Article  MATH  Google Scholar 

  38. Hamdia KM, Zhuang X, Rabczuk T (2021) An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Comput Appl 33(6):1923–1933

    Article  Google Scholar 

  39. Wang F, Jiang D, Qi S et al (2021) A dynamic resource scheduling scheme in edge computing satellite networks. Mobile Netw Appl 26(2):597–608

    Article  Google Scholar 

  40. Han X, Dong Y, Yue L et al (2019) State transition simulated annealing algorithm for discrete-continuous optimization problems. IEEE Access 7(1):44391–44403

    Article  Google Scholar 

  41. Zahid M, Javaid N, Ansar K et al (2018) Hill climbing load balancing algorithm on fog computing. International Conference on P2P. Parallel, Grid, Cloud and Internet Computing. Springer, pp 238–251

    Google Scholar 

  42. Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166(1):113917

    Article  Google Scholar 

  43. Pham QV, Mirjalili S, Kumar N et al (2020) Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Trans Veh Technol 69(4):4285–4297

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Communication Soft Science Program of Ministry of Industry and Information Technology of China (No. 2022-R-43), the Natural Science Basic Research Program of Shaanxi (No. 2021JQ-719), the Graduate Innovation Fund of Xi’an University of Posts and Telecommunications (No. CXJJYL2021021), 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.

Author information

Authors and Affiliations

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

    Yanping Chen, Shengsheng He, Xiaomin Jin & Zhongmin Wang

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

    Yanping Chen, Shengsheng He, Xiaomin Jin & Zhongmin Wang

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

    Yanping Chen, Shengsheng He, Xiaomin Jin & Zhongmin Wang

  4. ZTE Corporation, No.55, Hi-tech Road South, Shenzhen, 51805, China

    Fengwei Wang & Lei Chen

Authors
  1. Yanping Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shengsheng He
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiaomin Jin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhongmin Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Fengwei Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Lei Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Shengsheng He.

Ethics declarations

Competing interests

The authors declare that they have no competing interest.

Data availability

The data and material used to support the findings of this study are available from the corresponding author upon request.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., He, S., Jin, X. et al. Resource utilization and cost optimization oriented container placement for edge computing in industrial internet. J Supercomput 79, 3821–3849 (2023). https://doi.org/10.1007/s11227-022-04801-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04801-z

Keywords