Skip to main content
SpringerLink
Log in

An Efficient Cloudlet Deployment Method Based on Approximate Graph Cut in Large-scale WMANs

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Mobile edge computing provides a low-latency, high-bandwidth cloud computing environment for resource-constrained mobile devices by allowing mobile devices to offload tasks, but user task migration causes greater transmission delays. Cloudlets, a new component of mobile edge computing, can perform tasks offloaded by mobile users nearby to reduce the access latency and meet users’ requirements for system response time. However, deploying cloudlets in large-scale wireless metropolitan area networks (WMANs) to improve the service quality of mobile applications is currently still difficult. To resolve this issue, we design a cloudlet deployment model based on approximate graph cut, which abstracts the wireless communication network into an undirected weighted graph, divides the graph according to the access point location attributes, and minimizes the user access delay of subgraphs to obtain optimal network area segmentation and cloudlet deployment locations. We also develop an efficient kernel method to optimize the objective function of graph cuts. The simulation experimental results demonstrate that our model has low time and space complexity; thus, it is suitable for large-scale cloudlet deployment and has valuable application prospects.

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

Similar content being viewed by others

Data Availability

The network used in the experiment is generated by the GT-ITM tool, which is available at http://www.cc.gatech.edu/projects/gtitm/.

References

  1. Bi S, Huang L, Zhang Y-JA (2020) Joint optimization of service caching placement and computation offloading in mobile edge computing systems. IEEE Transactions on Wireless Communications 19(7):4947–4963

    Article  Google Scholar 

  2. Wang Y, Wang J, Zhang W, Zhan Y, Guo S, Zheng Q, Wang X (2022) A survey on deploying mobile deep learning applications: A systemic and technical perspective. Digital Communications and Networks 8(1):1–17

    Article  Google Scholar 

  3. Wang J (2022) Edge artificial intelligence-based affinity task offloading under resource adjustment in a 5g network. Applied Intelligence 52(7):8167–8188

    Article  Google Scholar 

  4. Wang X, Han Y, Wang C, Zhao Q, Chen X, Chen M (2019) In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network 33(5):156–165

    Article  Google Scholar 

  5. Zhou F, Hu RQ, Li Z, Wang Y (2020) Mobile edge computing in unmanned aerial vehicle networks. IEEE Wireless Communications 27(1):140–146

    Article  Google Scholar 

  6. Zhou F, Hu RQ (2020) Computation efficiency maximization in wireless-powered mobile edge computing networks. IEEE Transactions on Wireless Communications 19(5):3170–3184

    Article  Google Scholar 

  7. Qi C, Zhang J, Jia H, Mao Q, Wang L, Song H (2021) Deep face clustering using residual graph convolutional network. Knowledge-Based Systems 211:106561

    Article  Google Scholar 

  8. Lu F, Gu L, Yang LT, Shao L, Jin H (2020) Mildip: An energy efficient code offloading framework in mobile cloudlets. Information Sciences 513:84–97

    Article  Google Scholar 

  9. Chamola V, Tham C-K, Gurunarayanan S, Ansari N (2020) An optimal delay aware task assignment scheme for wireless sdn networked edge cloudlets. Future Generation Computer Systems 102:862–875

    Article  Google Scholar 

  10. Song S, Ma S, Zhao J, Yang F, Zhai L (2022) Cost-efficient multi-service task offloading scheduling for mobile edge computing. Applied Intelligence 52(4):4028–4040

    Article  Google Scholar 

  11. Bhatta D, Mashayekhy L (2022) A bifactor approximation algorithm for cloudlet placement in edge computing. IEEE Transactions on Parallel and Distributed Systems 33(8):1787–1798

    Article  Google Scholar 

  12. Turkoglu B, Uymaz SA, Kaya E (2022) Binary artificial algae algorithm for feature selection. Applied Soft Computing 120:108630

    Article  Google Scholar 

  13. Turkoglu B, Kaya E (2020) Training multi-layer perceptron with artificial algae algorithm. Engineering Science and Technology, an International Journal 23(6):1342–1350

    Article  Google Scholar 

  14. Turkoglu B, Uymaz SA, Kaya E (2022) Clustering analysis through artificial algae algorithm. International Journal of Machine Learning and Cybernetics 13(4):1179–1196

    Article  Google Scholar 

  15. Turkoglu, B., Uymaz, S.A., Kaya, E.: Chaos theory in metaheuristics. In: Comprehensive Metaheuristics, pp. 1–20. Elsevier, London (2023)

  16. Jiang L, Liu L, Yao J, Shi L (2020) A user interest community evolution model based on subgraph matching for social networking in mobile edge computing environments. Journal of Cloud Computing 9(1):1–15

    Google Scholar 

  17. Xu X, Shen B, Yin X, Khosravi MR, Wu H, Qi L, Wan S (2021) Edge server quantification and placement for offloading social media services in industrial cognitive iov. IEEE Transactions on Industrial Informatics 17(4):2910–2918

    Article  Google Scholar 

  18. Zhang X, Wang Y (2023) Deepmecagent: multi-agent computing resource allocation for uav-assisted mobile edge computing in distributed iot system. Applied Intelligence 53(1):1180–1191

    Article  Google Scholar 

  19. Ma, L., Wu, J., Chen, L.: Dota: Delay bounded optimal cloudlet deployment and user association in wmans. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 196–203 (2017). IEEE

  20. Chen Y, Lin Y, Zheng Z, Yu P, Shen J, Guo M (2022) Preference-aware edge server placement in the internet of things. IEEE Internet of Things Journal 9(2):1289–1299

    Article  Google Scholar 

  21. Yang S, Li F, Shen M, Chen X, Fu X, Wang Y (2019) Cloudlet placement and task allocation in mobile edge computing. IEEE Internet of Things Journal 6(3):5853–5863

    Article  Google Scholar 

  22. Zhang Y, Jiao L, Yan J, Lin X (2019) Dynamic service placement for virtual reality group gaming on mobile edge cloudlets. IEEE Journal on Selected Areas in Communications 37(8):1881–1897

    Article  Google Scholar 

  23. Sun X, Ansari N (2020) Green cloudlet network: A sustainable platform for mobile cloud computing. IEEE Transactions on Cloud Computing 8(1):180–192

    Article  Google Scholar 

  24. Li Y, Zhou A, Ma X, Wang S (2022) Profit-aware edge server placement. IEEE Internet of Things Journal 9(1):55–67

    Article  Google Scholar 

  25. Zhang X, Li Z, Lai C, Zhang J (2022) Joint edge server placement and service placement in mobile edge computing. IEEE Internet of Things Journal 9(13):11261–11274

    Article  Google Scholar 

  26. Sterz A, Felka P, Simon B, Klos S, Klein A, Hinz O, Freisleben B (2022) Multi-stakeholder service placement via iterative bargaining with incomplete information. IEEE/ACM Transactions on Networking 30(4):1822–1837

    Article  Google Scholar 

  27. Nguyen DT, Nguyen HT, Trieu N, Bhargava VK (2022) Two-stage robust edge service placement and sizing under demand uncertainty. IEEE Internet of Things Journal 9(2):1560–1574

    Article  Google Scholar 

  28. Jia H, Zhu D, Huang L, Mao Q, Wang L, Song H (2023) Global and local structure preserving nonnegative subspace clustering. Pattern Recognition 138:109388

    Article  Google Scholar 

  29. Jia H, Wang L, Song H, Mao Q, Ding S (2021) An efficient nyström spectral clustering algorithm using incomplete cholesky decomposition. Expert Systems with Applications 186:115813

    Article  Google Scholar 

  30. Song K, Yao X, Nie F, Li X, Xu M (2021) Weighted bilateral k-means algorithm for fast co-clustering and fast spectral clustering. Pattern Recognition 109:107560

    Article  Google Scholar 

  31. Jia H, Ren Q, Huang L, Mao Q, Wang L, Song H (2023) Large-scale non-negative subspace clustering based on nyström approximation. Information Sciences 638:118981

  32. GT-ITM. http://www.cc.gatech.edu/projects/gtitm/

  33. 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 

  34. Song H, Gu B, Son K, Choi W (2022) Joint optimization of edge computing server deployment and user offloading associations in wireless edge network via a genetic algorithm. IEEE Transactions on Network Science and Engineering 9(4):2535–2548

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under (Nos. 61906077, 62102168, 61902156), the Natural Science Foundation of Jiangsu Province under (Nos. BK20190838, BK20200888), the Postdoctoral Research Foundation of China under (Nos. 2020M671376, 2020T130257), and the Postdoctoral Science Foundation of Jiangsu Province under (No. 2021K596C).

Author information

Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Longxia Huang, Changzhi Huo, Xing Zhang & Hongjie Jia

  2. Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications, Zhenjiang, 212013, China

    Hongjie Jia

Authors
  1. Longxia Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changzhi Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongjie Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongjie Jia.

Ethics declarations

Conflicts of interest

The authors have no competing interests to declare that are relevant to the content of this article.

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 (e.g. a society or other partner) 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

Huang, L., Huo, C., Zhang, X. et al. An Efficient Cloudlet Deployment Method Based on Approximate Graph Cut in Large-scale WMANs. Appl Intell 53, 22635–22647 (2023). https://doi.org/10.1007/s10489-023-04672-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-04672-8

Keywords

Search

Navigation