Skip to main content
Log in

An Incentive-Compatible Offloading Mechanism in Fog-Cloud Environments Using Second-Price Sealed-Bid Auction

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

In a fog-cloud environment, the tasks submitted by end-users are first sent to intermediate nodes called fog nodes. If the computing resources in fog nodes are insufficient, the nodes will offload tasks to the remote cloud. Naturally, intermediate nodes are reluctant to offload tasks to upstream entities, which in turn results in a degradation in network performance. To motivate such reluctant nodes, some previous studies have used game theoretical approaches. We believe that auction theory is one of the most important mathematical tools to motivate fog nodes to participate in offloading operations. In this paper, we propose a second-price sealed-bid auction mechanism to optimize offloading. In our model, the service unit plays the role of the commodity. Also, fog nodes and cloud datacenter play the role of bidders and auctioneers, respectively. We prove that the proposed auction mechanism has two important properties of incentive compatibility and incentive rationality. We formulate the problem using queuing theory in both the edge layer and the cloud layer. In each layer, the auction mechanism is used to allocate resources. We compare the proposed mechanism with state-of-the-art methods. Experimental evaluations using the iFogSim simulator indicated that the proposed method is much better than other methods in terms of significant criteria such as execution time, energy consumption, and network usage.

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

Similar content being viewed by others

Data Availability

The datasets generated during and analyzed during the current study are available in the [MENDELEY] repository, [http://data.mendeley.com/datasets/b7v3rmf9jd/1].

References

  1. Keshavarznejad, M., Rezvani, M.H. and Adabi, S.,: Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms. Cluster Computing, pp.1–29 (2021)

  2. Mostafa, M.A.A.A. and Khater, A.M., : April. Horizontal Offloading Mechanism for IoT Application in Fog Computing Using Microservices Case Study: Traffic Management System. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 640–647). IEEE (2019)

  3. Ning, Z., Huang, J., Wang, X.: Vehicular fog computing: enabling real-time traffic management for smart cities. IEEE Wirel. Commun. 26(1), 87–93 (2019)

    Article  Google Scholar 

  4. Alam, M.G.R., Hassan, M.M., Uddin, M.Z., Almogren, A., Fortino, G.: Autonomic computation offloading in mobile edge for IoT applications. Futur. Gener. Comput. Syst. 90, 149–157 (2019)

    Article  Google Scholar 

  5. Ye, D., Wu, M., Tang, S. and Yu, R., : June. Scalable fog computing with service offloading in bus networks. In 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 247–251). IEEE (2016)

  6. Nguyen, T.T., Ha, V.N., Le, L.B., Schober, R.: Joint data compression and computation offloading in hierarchical fog-cloud systems. IEEE Trans. Wirel. Commun. 19(1), 293–309 (2019)

    Article  Google Scholar 

  7. Tassi, A., Mavromatis, I., Piechocki, R.J. and Nix, A., : April. Secure Data Offloading Strategy for Connected and Autonomous Vehicles. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring) (pp. 1–2). IEEE (2019)

  8. Wang, C., Liang, C., Yu, F.R., Chen, Q., Tang, L.: Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Trans. Wirel. Commun. 16(8), 4924–4938 (2017)

    Article  Google Scholar 

  9. Wang, D., Liu, Z., Wang, X., Lan, Y.: Mobility-aware task offloading and migration schemes in fog computing networks. IEEE Access. 7, 43356–43368 (2019)

    Article  Google Scholar 

  10. Li, Q., Zhao, J., Gong, Y., Zhang, Q.: Energy-efficient computation offloading and resource allocation in fog computing for internet of everything. China Communications. 16(3), 32–41 (2019)

    Google Scholar 

  11. Jošilo, S. and Dán, G., : May. A game theoretic analysis of selfish mobile computation offloading. In IEEE INFOCOM 2017-IEEE Conference on Computer Communications (pp. 1–9). IEEE (2017)

  12. Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Transactions on Parallel and Distributed Systems. 26(4), 974–983 (2014)

    Article  Google Scholar 

  13. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Networking. 24(5), 2795–2808 (2015)

    Article  Google Scholar 

  14. Safianowska, M.B., Gdowski, R. and Huang, C., : December. Combinatorial recurrent multi-unit auctions for fog services. In 2016 International Computer Symposium (ICS) (pp. 736–741). IEEE (2016)

  15. Khan, J.A., Westphal, C. and Ghamri-Doudane, Y., : September. Offloading content with self-organizing mobile fogs. In 2017 29th International Teletraffic Congress (ITC 29) (Vol. 1, pp. 223–231). IEEE (2017)

  16. Cheng, N., Lu, N., Zhang, N., Zhang, X., Shen, X.S., Mark, J.W.: Opportunistic WiFi offloading in vehicular environment: a game-theory approach. IEEE Trans. Intell. Transp. Syst. 17(7), 1944–1955 (2016)

    Article  Google Scholar 

  17. Zhou, H., Chen, X., He, S., Chen, J., Wu, J.: DRAIM: a novel delay-constraint and reverse auction-based incentive mechanism for WiFi offloading. IEEE Journal on Selected Areas in Communications. 38(4), 711–722 (2020)

    Article  Google Scholar 

  18. Zhang, Y., Tang, S., Chen, T. and Zhong, S., : April. Competitive auctions for cost-aware cellular traffic offloading with optimized capacity gain. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (pp. 1–9). IEEE. (2016)

  19. Mashhadi, F., Monroy, S.A.S., Bozorgchenani, A., Tarchi, D.: Optimal auction for delay and energy constrained task offloading in mobile edge computing. Comput. Netw. 183, 107527 (2020)

    Article  Google Scholar 

  20. Deb, K., Agrawal, S., Pratap, A., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  21. Besharati, R. and Rezvani, M.H., : February. A prototype auction-based mechanism for computation offloading in fog-cloud environments. In 2019 5th conference on knowledge based engineering and innovation (KBEI) (pp. 542–547). IEEE (2019)

  22. Rezvani, M.H., Analoui, M.: Strategic behavior modeling of multi-service overlay multicast networks based on auction mechanism design. Journal of Parallel and Distributed Computing. 71(8), 1125–1141 (2011)

    Article  Google Scholar 

  23. Analoui, M., Rezvani, M.H.: A framework for resource allocation in multi-service multi-rate overlay networks based on microeconomic theory. J. Netw. Syst. Manag. 19(2), 178–208 (2011)

    Article  Google Scholar 

  24. Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things. Edge and Fog computing environments. Software: Practice and Experience. 47(9), 1275–1296 (2017)

  25. Yi, S., Li, C. and Li, Q., : June. A survey of fog computing: concepts, applications and issues. In Proceedings of the 2015 workshop on mobile big data (pp. 37–42) (2015)

  26. Mahmud, R., Kotagiri, R. and Buyya, R.,: Fog computing: A taxonomy, survey and future directions. In Internet of everything (pp. 103–130). Springer, Singapore (2018)

  27. Misra, S., Saha, N.: Detour: dynamic task offloading in software-defined fog for IoT applications. IEEE Journal on Selected Areas in Communications. 37(5), 1159–1166 (2019)

    Article  Google Scholar 

  28. Liu, C.F., Bennis, M., Debbah, M., Poor, H.V.: Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing. IEEE Trans. Commun. 67(6), 4132–4150 (2019)

    Article  Google Scholar 

  29. Zhou, S., Sun, Y., Jiang, Z., Niu, Z.: Exploiting moving intelligence: delay-optimized computation offloading in vehicular fog networks. IEEE Commun. Mag. 57(5), 49–55 (2019)

    Article  Google Scholar 

  30. Vu, T.T., Nguyen, D.N., Hoang, D.T., Dutkiewicz, E. and Nguyen, T.V.,: Optimal Energy Efficiency with Delay Constraints for Multi-layer Cooperative Fog Computing Networks. arXiv preprint arXiv:1906.03567 (2019)

  31. Chen, L., Wu, J., Long, X. and Zhang, Z.,: ENGINE: Cost Effective Offloading in Mobile Edge Computing with Fog-Cloud Cooperation. arXiv preprint arXiv:1711.01683 (2017)

  32. Wei, Z., Jiang, H.: Optimal offloading in fog computing systems with non-orthogonal multiple access. IEEE Access. 6, 49767–49778 (2018)

    Article  Google Scholar 

  33. Chen, X. and Zhang, J., : May. When D2D meets cloud: Hybrid mobile task offloadings in fog computing. In 2017 IEEE international conference on communications (ICC) (pp. 1–6). IEEE (2017)

  34. Funai, C., Tapparello, C. and Heinzelman, W., : December. Mobile to mobile computational offloading in multi-hop cooperative networks. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1–7). IEEE (2016)

  35. Kim, J., Ha, T., Yoo, W., Chung, J.M.: Task popularity-based energy minimized computation offloading for fog computing wireless networks. IEEE Wireless Communications Letters. 8(4), 1200–1203 (2019)

    Article  Google Scholar 

  36. Meng, X., Wang, W., Zhang, Z.: Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE Access. 5, 21355–21367 (2017)

    Article  Google Scholar 

  37. Tassi, A., Mavromatis, I., Piechocki, R., Nix, A., Compton, C., Poole, T. and Schuster, W., : April. Agile data offloading over novel fog computing infrastructure for CAVs. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring) (pp. 1–6). IEEE (2019)

  38. Liu, L., Chang, Z., Ristaniemi, T., Niu, Z.: Multi-Objective Optimization for Computation Offloading in Fog Computing. In: IEEE Internet of Things J. doi: https://doi.org/10.1109/jiot. (2017)

  39. Cui, Y., Song, J., Ren, K., Li, M., Li, Z., Ren, Q., Zhang, Y.: Software defined cooperative offloading for mobile cloudlets. IEEE/ACM Trans. Networking. 25(3), 1746–1760 (2017)

    Article  Google Scholar 

  40. Wang, Y., Lin, X. and Pedram, M., : March. A nested two stage game-based optimization framework in mobile cloud computing system. In 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering (pp. 494–502). IEEE (2013)

  41. Jia, M., Cao, J., Liang, W.: Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing. 5(4), 725–737 (2015)

    Article  Google Scholar 

  42. Sztrik, J.: Basic queueing theory. University of Debrecen, Faculty of Informatics. 193, 60–67 (2012)

    Google Scholar 

  43. Bolch, G., Greiner, S., De Meer, H. and Trivedi, K.S.,: Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. John Wiley & Sons (2006)

  44. Kleinrock, L.,: Queuing systems. Wiley (1975)

  45. Krishna, V.,: Auction theory. Academic press (2009)

  46. Mohammadi, A., Rezvani, M.H.: A novel optimized approach for resource reservation in cloud computing using producer–consumer theory of microeconomics. J. Supercomput. 75(11), 7391–7425 (2019)

    Article  Google Scholar 

  47. Aboutorabi, S.J.S. and Rezvani, M.H.,: An Optimized Meta-heuristic Bees Algorithm for Players’ Frame Rate Allocation Problem in Cloud Gaming Environments. The Computer Games Journal, pp.1–24 (2020)

  48. Kowalski, J. and Tu, X.M.,: Modern applied U-statistics (Vol. 714). John Wiley & Sons (2008)

  49. Esfandiari, S.. and Rezvani, M.H.,: An Optimized Content Delivery Approach based on Demand-supply Theory in Disruption-tolerant Networks. Cluster Computing, pp.1–24 (2020)

  50. Klose, B.S. and Schweinzer, P.,: Auctioning risk: The all-pay auction under mean-variance preferences. University of Zurich, Department of Economics, Working Paper, (97) (2017)

  51. Jafari, V., Rezvani, M.H.: Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II Metaheuristic algorithm. J. Ambient. Intell. Humaniz. Comput. 2021, (in press)

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Hossein Rezvani.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Besharati, R., Rezvani, M.H. & Sadeghi, M.M.G. An Incentive-Compatible Offloading Mechanism in Fog-Cloud Environments Using Second-Price Sealed-Bid Auction. J Grid Computing 19, 37 (2021). https://doi.org/10.1007/s10723-021-09576-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-021-09576-w

Keywords

Navigation