Skip to main content

Advertisement

Springer Nature Link
Log in

Cost-aware resource management based on market pricing mechanisms in edge federation environments

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

Abstract

The edge federation meets the needs of different users through resource allocation and service configuration across edge-independent computing providers and clouds, promoting the better collaboration of resources among edge clusters and effectively improving the user service quality. Existing edge federation solutions focus mainly on enabling resource sharing to satisfy users with different resource requirements, ignoring the actual profits of edge service providers (ESPs) and the fact that many resource owners may not be motivated to work with other providers in edge federation environments. In this paper, we are the first to consider a situation in that ESPs rent resources from other ESPs under specific circumstances in edge federation environments, and a reasonable rental price determines the participation and profitability of ESPs. Therefore, we propose three pricing mechanisms based on economic models to reasonably set ESPs’ external rental resources prices to maximize the service providers’ profit. Extensive simulation studies evaluate the effectiveness of the algorithms and revenue models and also demonstrate that pricing mechanism 2 (PriM2) is the closest to the optimal solution, and ESPs adopt PriM2 to bring higher revenue than no pricing mechanism and pricing mechanism 1.

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

Similar content being viewed by others

Data availability statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

References

  1. Baghban H, Huang CY, Hsu CH (2021) Latency minimization model towards high efficiency edge-iot service provisioning in horizontal edge federation. Multimed Tools Appl, pp. 1–18

  2. Hba B, Cyh A, Chhcd E (2020) Resource provisioning towards opex optimization in horizontal edge federation. Comput Commun 158:39–50

    Article  Google Scholar 

  3. Ma X, Zhang S, Li W, Zhang C (2017) Cost-efficient workload scheduling in cloud assisted mobile edge computing. Paper presented at the 25th International Symposium on Quality of Service, Vilanova i la Geltrú, Spain, 14–16 June 2017

  4. Cao X, Tang G, Guo D, Zhang W (2020) Edge federation: towards an integrated service provisioning model. J Netw Comput Appl 28(3):1116–1129

    Google Scholar 

  5. Zhang JK (2020) Edge Federation: a dependency-aware multi-task dispatching and co-location in federated edge container-instances. Paper presented at 2020 IEEE International Conference on Edge Computing (EDGE), Beijing, China, 19–23 October 2020

  6. Ying-Dar L, Duc-Tai T, Asad A (2020) Proxy-based federated authentication: a transparent third-party solution for cloud-edge federation. IEEE Netw 34(6):220–227

    Article  Google Scholar 

  7. Dimitrios K, Angelos R, Vera S (2016) A federated edge cloud-IoT architecture. Paper presented at 2016 European Conference on Networks and Communications, Athens, Greece, 230–234 June 2016

  8. Luo YS, Qiu S (2019) Optimal resource reservation scheme for maximizing profit of service providers in edge computing federation. Paper presented at 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Fukuoka, Japan, 876–879 November 2019

  9. Xiong ZH, Feng SH, Wang WB (2019) Cloud/fog computing resource management and pricing for blockchain networks. IEEE Internet Things 6(3):4585–4600

    Article  Google Scholar 

  10. Liu JX, Wang Y, Han X (2020) Research on edge cloud resource pricing mechanism based on stackelberg game. J Front Comput Sci Technol, pp 1–12

  11. Guo KK, Qiu MK, Zhao H (2020) Resource dynamic pricing strategy based on stackelberg game. J Huazhong Univ Sci Technol 48(4):121–126

    Google Scholar 

  12. Yang B, Li ZY, Jiang SL (2018) Envy-free auction mechanism for VM pricing and allocation in clouds. Future Gener Comput Syst 86:680–693

    Article  Google Scholar 

  13. Zhang TN (2020) A QoS-aware virtual resource pricing service based on game theory in federated clouds. Int J Intell Syst 19(2):191–206

    Google Scholar 

  14. Jin AL, Song W, Zhuang W (2018) Auction-based resource allocation for sharing cloudlets in mobile cloud computing. IEEE Trans Emerg Top Commun 6(1):45–57

    Article  Google Scholar 

  15. Sun W, Liu JK, Yue Y (2018) Double auction-based resource allocation for mobile edge computing in industrial internet of things. IEEE Trans Ind Inform 14(10):4692–4701

    Article  Google Scholar 

  16. Javed B, Bloodsworth P, Rasool RU (2016) Cloud market maker: an automated dynamic pricing marketplace for cloud users. J Netw Comput Appl 54:52–67

    Google Scholar 

  17. Nguyen DT, Le LB, Bhargava V (2018) Price-based resource allocation for edge computing: a market equilibrium approach. IEEE Trans Cloud Comput 9(1):302–317

    Article  Google Scholar 

  18. Hou D, Huang LS, Xu HL (2018) Revenue maximization for dynamic expansion of geo-distributed cloud data centers. IEEE Trans Cloud Comput 8(3):899–913

    Google Scholar 

  19. Zhou LS, Huang XW, Yang JM (2022) Review on resources allocation and pricing methods in mobile edge computing. Telecommun Sci 38(3):113–132

    Google Scholar 

  20. Wang QY (2011) Supply and demand functions in economic mathematics. MGTt Technol SME 3(57)

  21. Petri I, Diaz-Montes J, Zou M (2015) Market models for federated clouds. IEEE Trans Cloud Comput 3(3):398–410

    Article  Google Scholar 

  22. Xu J, Palanisamy B (2017) Cost-aware resource management for federated clouds using resource sharing contracts. Paper presented at IEEE International Conference on Cloud Computing , Honololu, HI, USA, 25–30 June 2017

  23. Jing C, Zhu YS, Li ML (2015) Customer satisfaction-aware scheduling forutility maximization on geo-distributed data centers. Concurr Comput-Pract Exp 27(5):1334–1354

    Article  Google Scholar 

  24. Randriamasinoro NM, Nguyen KK, Cheriet, M (2018) Optimized resource allocation in edge-cloud environment. Paper presented at 2018 Annual IEEE International Systems Conference, Vancouver, BC, Canada, 1–8 May 2018

  25. Yao H, Bai C, Xionf M (2017) Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing. Concurr Comput-Pract Exp 29(16):1–14

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China [Grant number 62262011], the Guangxi Natural Science Foundation [Grant number 2020GXNSFAA159038], the Foundation of Guilin University of Technology [Grant number GUTQDJJ2002018], and the Guangxi Universities Key Laboratory Director Fund of Embedded Technology and Intelligent Information Processing [Grant number 2020-1-7].

Author information

Author notes

  1. Hengzhou Ye and Wei Hao have contributed equally to this work.

Authors and Affiliations

  1. Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Yanshan, Guilin, 541006, China

    Fengyi Huang, Hengzhou Ye & Wei Hao

  2. Guangdong Key Lab of AI and Multi-Modal Data Processing, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, 519000, China

    Fengyi Huang

Authors
  1. Fengyi Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hengzhou Ye
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wei Hao
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Fengyi Huang was involved in conceptualization, methodology, validation, formal analysis, investigation, data curation, writing–original draft, writing–review and editing, visualization. Hengzhou Ye contributed to conceptualization, methodology, validation, writing-review and editing, supervision, project administration, funding acquisition. Wei Hao was involved in validation, investigation, formal analysis, writing–review and editing.

Corresponding author

Correspondence to Hengzhou Ye.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing fanancial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Huang, F., Ye, H. & Hao, W. Cost-aware resource management based on market pricing mechanisms in edge federation environments. J Supercomput 79, 5939–5961 (2023). https://doi.org/10.1007/s11227-022-04870-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04870-0

Keywords