Skip to main content
Log in

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Cloud computing is characterized by strong computing and storage capabilities, and edge computing has the advantages of low latency and low power consumption. Many service providers have begun to combine the advantages of cloud and edge computing to provide better quality of service, but the heterogeneity of cloud and edge computing represents a challenge for service deployment and resource allocation. This paper proposes a framework for cloud-edge collaboration based on live video webcast services and transforms the resource allocation problem into a constrained integer programming (IP) model. Additionally, we introduce an auction mechanism to solve the problem of resource competition among the anchor users in live services. By solving the IP resource allocation problem and Vickrey–Clarke–Groves mechanism, we obtain the optimal resource allocation mechanism. Based on the dominant resource proportion and matching model, we design a resource allocation mechanism for the online environment. These mechanisms can be used for reservation and live webcast scenarios. Furthermore, we prove that the two mechanisms have individual rationality and truthfulness. Our approach is characterized by high social welfare, high resource utilization and a short execution time.

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

Access this article

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

Availability of data and material

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Alibaba (2020) Alibaba Live [Online]. https://www.aliyun.com/product/live

  2. Angelelli E, Filippi C (2011) On the complexity of interval scheduling with a resource constraint. Theoret Comput Sci 412(29):3650–3657

    Article  MathSciNet  Google Scholar 

  3. Angelelli E, Bianchessi N, Filippi C (2014) Optimal interval scheduling with a resource constraint. Comput Oper Res 51:268–281

    Article  MathSciNet  Google Scholar 

  4. Bharti S, Pattanaik K (2014) Dynamic distributed flow scheduling for effective link utilization in data center networks. J High Speed Netw 20(1):1–10

    Article  Google Scholar 

  5. Carvalho G, Cabral B, Pereira V, Bernardino J (2021) Edge computing: current trends, research challenges and future directions. Computing 103:993–1023

  6. Chen M, Hao Y (2018) Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J Sel Areas Commun 36(3):587–597

    Article  Google Scholar 

  7. Chen X, Hu X, Liu T, Ma W, Qin T, Tang P, Wang C, Zheng B (2016) Efficient mechanism design for online scheduling. J Artif Intell Res 56(1):429–461

    Article  MathSciNet  Google Scholar 

  8. Deng S, Xiang Z, Zhao P, Taheri J, Gao H, Yin J, Zomaya AY (2020) Dynamical resource allocation in edge for trustable internet-of-things systems: a reinforcement learning method. IEEE Trans Ind Inf 16(9):6103–6113

    Article  Google Scholar 

  9. Deng S, Xiang Z, Taheri J, Khoshkholghi MA, Yin J, Zomaya AY, Dustdar S (2021) Optimal application deployment in resource constrained distributed edges. IEEE Trans Mob Comput 20(5):1907–1923

    Article  Google Scholar 

  10. Duan Q, Wang S, Ansari N (2020) Convergence of networking and cloud/edge computing: Status, challenges, and opportunities. IEEE Netw 34(6):148–155

    Article  Google Scholar 

  11. Guo H, Liu J, Qin H (2018) Collaborative mobile edge computation offloading for iot over fiber-wireless networks. IEEE Netw 32(1):66–71

    Article  Google Scholar 

  12. Iimedia (2019) 2019q3 china online live broadcast industry development research report [Online]. https://www.iimedia.cn/c400/66897.html

  13. Jiao Y, Wang P, Niyato D, Xiong Z (2018) Social welfare maximization auction in edge computing resource allocation for mobile blockchain. In: IEEE international conference on communications, pp 1–6

  14. Li K (2019) How to stabilize a competitive mobile edge computing environment: a game theoretic approach. IEEE Access 7:69960–69985

    Article  Google Scholar 

  15. Liu C, Li K, Liang J, Li K (2019) Cooper-match: job offloading with a cooperative game for guaranteeing strict deadlines in mec. IEEE Trans Mobile Comput. https://doi.org/10.1109/TMC.2019.2921713

  16. Liu C, Li K, Liang J, Li K (2019) Cooper-sched: a cooperative scheduling framework for mobile edge computing with expected deadline guarantee. IEEE Trans Parallel Distrib Syst 1

  17. Liu X, Li W, Zhang X (2018) Strategy-proof mechanism for provisioning and allocation virtual machines in heterogeneous clouds. IEEE Trans Parallel Distrib Syst 29(7):1650–1663

    Article  Google Scholar 

  18. Mashayekhy L, Fisher N, Grosu D (2016) Truthful mechanisms for competitive reward-based scheduling. IEEE Trans Comput 65(7):2299–2312

    Article  MathSciNet  Google Scholar 

  19. Mashayekhy L, Nejad M, Grosu D, Vasilakos A (2016) An online mechanism for resource allocation and pricing in clouds. IEEE Trans Comput 65(4):1172–1184

    Article  MathSciNet  Google Scholar 

  20. Nejad M, Mashayekhy L, Grosu D (2015) Truthful greedy mechanisms for dynamic virtual machine provisioning and allocation in clouds. IEEE Trans Parallel Distrib Syst 26(2):594–603

    Article  Google Scholar 

  21. 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

  22. Nisan T, Roughgarden E, Tardos E, Vazirani V (2007) Algorithmic game theory. Cambridge Univ. Press, Cambridge

    Book  Google Scholar 

  23. Ren J, Yu G, He Y, Li GY (2019) Collaborative cloud and edge computing for latency minimization. IEEE Trans Veh Technol 68(5):5031–5044

    Article  Google Scholar 

  24. Shi W, Pallis G, Xu Z (2019) Edge computing [scanning the issue]. Proc IEEE 107(8):1474–1481

    Article  Google Scholar 

  25. Song B, Hassan MM, Alamri A, Alelaiwi A, Tian Y, Pathan M, Almogren A (2016) A two-stage approach for task and resource management in multimedia cloud environment. Computing 98(1–2):119–145

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  27. Tran Tuyen X, Pompili D (2018) Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Trans Veh Technol 68(1):856–868

    Article  Google Scholar 

  28. Wang Y, Sheng M, Wang X, Wang L, Li J (2016) Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Trans Commun 64(10):4268–4282

    Google Scholar 

  29. Wu Q, Hao J (2016) A clique-based exact method for optimal winner determination in combinatorial auctions. Inf Sci 334:103–121

    Article  Google Scholar 

  30. Xiang Z, Deng S, Jiang F, Gao H, Tehari J, Yin J (2020) Computing power allocation and traffic scheduling for edge service provisioning, pp 394–403

  31. You C, Huang K, Chae H, Kim BH (2016) Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans Wirel Commun 16(3):1397–1411

    Article  Google Scholar 

  32. Zafari F, Li J, Leung KK, Towsley D, Swami A (2018) A game-theoretic approach to multi-objective resource sharing and allocation in mobile edge clouds. In: Technologies for the wireless edge workshop, pp 9–13

  33. Zhang H, Guo F, Ji H, Zhu C (2015) Combinational auction-based service provider selection in mobile edge computing networks. IEEE Access 5:13455–13464

    Article  Google Scholar 

  34. Zhang J, Xie N, Zhang X, Li W (2018) An online auction mechanism for cloud computing resource allocation and pricing based on user evaluation and cost. Future Gener Comput Syst 89:286–299

    Article  Google Scholar 

  35. Zhang J, Xie N, Zhang X, Yue K, Li W, Kumar D (2018) Machine learning based resource allocation of cloud computing in auction. Comput Mater Contin 56(1):123–135

    Google Scholar 

  36. Zhang J, Yang X, Xie N, Zhang X, Athanasios V, Li W (2020) An online auction mechanism for time-varying multidimensional resource allocation in clouds. Future Gener Comput Syst 111:27–38

    Article  Google Scholar 

  37. Zhao H, Deng S, Liu Z, Xiang Z, Yin J, Dustdar S, Zomaya A (2021) Dpos: decentralized, privacy-preserving, and low-complexity online slicing for multi-tenant networks. IEEE Trans Mobile Comput. https://doi.org/10.1109/TMC.2021.3074934

  38. Zhou H, Bai G, Deng S (2019) Optimal interval scheduling with nonidentical given machines. Clust Comput 22(5):1007–1015

    Article  Google Scholar 

Download references

Funding

This work is supported in part by the National Natural Science Foundation of China (Nos. 62062065, 61762091, 61662088, 12071417 and 11663007), the Project of the Natural Science Foundation of Yunnan Province of China (2019FB142 and 2018ZF017), and the Program for Excellent Young Talents, Yunnan University, China.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Weidong Li.

Ethics declarations

Conflict of interest

Not applicable

Code availability

The codes generated or used during the study are available from the corresponding author on reasonable request.

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

Zhang, J., Chi, L., Xie, N. et al. Strategy-proof mechanism for online resource allocation in cloud and edge collaboration. Computing 104, 383–412 (2022). https://doi.org/10.1007/s00607-021-00962-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-021-00962-6

Keywords

Mathematics Subject Classification

Navigation