Skip to main content
Log in

Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

For fog computing network, how to effectively and quickly offload the task to fog nodes is a big challenge. In this paper, a task offloading scheme based on improved contract net protocol and beetle antennae search algorithm in fog computing networks is proposed. Firstly, the system mode of task offloading in fog computing network is described. Then, a contract net protocol is presented to obtain the information from the fog nodes. Based on the information, the agent will allocate the sub-tasks to the fog nodes. And the task offloading issue in fog computing network is formulated and analyzed. Next, an efficient task offloading scheme based on improved contract net protocol and beetle antennae search algorithm is proposed. Finally, the analysis and simulation results validate the efficiency of the proposed scheme compared with other algorithms.

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

References

  1. Xenakis D, Kountouris M, Merako L, Passas N, Verikoukis C (2016) Performance analysis of Network-Assisted D2D discovery in random spatial networks. IEEE Trans Wirel Commun 15 (8):5695–5707

    Article  Google Scholar 

  2. Asadi A, Mancuso V, Gupta R (2017) DORE: An experimental framework to enable outband D2D relay in cellular networks. IEEE/ACM Trans Networking 25(5):2930–2943

    Article  Google Scholar 

  3. Ma R, Chang YJ, Chen HH, Chiu CY (2017) On relay selection schemes for Relay-Assisted D2D communications in LTE-a systems. IEEE Trans Veh Technol 66(9):8303–8314

    Article  Google Scholar 

  4. Wang T, Zhou J, Chen X, Wang G, Liu A, Liu Y (2018) A Three-Layer privacy preserving cloud storage scheme based on computational intelligence in fog computing. IEEE Trans Emerg Topics Comput Intell 2(1):3–12

    Article  Google Scholar 

  5. Cuff PW, Per muter HH, Cover TM (2010) Coordination capacity. IEEE Trans Inf Theory 56(9):4181–4206

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  7. Wang P, Yao C, Zheng Z, Sun G, Song L (2019) Joint task assignment, transmission, and computing resource allocation in multilayer mobile edge computing systems. IEEE Internet Things J 6 (2):2872–2884

    Article  Google Scholar 

  8. Shi W, Zhang J, Zhang R (2019) Share-based Edge Computing Paradigm With Mobile-to-Wired Offloading Computing. IEEE Commun Lett 23(11):1953–1957

    Article  Google Scholar 

  9. Li L, Zhou H, Xiong SX, Yang J, Mao Y (2019) Compound model of task arrivals and Load-Aware offloading for vehicular mobile edge computing networks. IEEE Access7 26631–26640

  10. Xing H, Liu L, Xu J, Nallanathan A (2019) Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing. IEEE Trans Commun 67(6):4193–4207

    Article  Google Scholar 

  11. Zhang G, Shen F, Liu Z, Yang Y, Wang K, Zhou M (2019) FEMTO: Fair And Energy-Minimized Task Offloading for Fog-Enabled IoT Networks. IEEE Internet Things J 6(3):4388–4400

    Article  Google Scholar 

  12. Chiu T, Pang A, Chung W, Zhang J (2019) Latency-Driven Fog cooperation approach in fog radio access networks. IEEE Trans Serv Comput 12(5):698–711

    Article  Google Scholar 

  13. Zhu C, et al. (2019) Folo: latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J 6(3):4150–4161

    Article  Google Scholar 

  14. Yin L, Luo J, Luo H (2018) Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans Indust Inform 14(10):4712–4721

    Article  Google Scholar 

  15. Zhang G, Shen F, Chen N, Zhu P, Dai X, Yang Y (2019) DOTS: Delay-Optimal Task scheduling among voluntary nodes in fog networks. IEEE Internet Things J 6(2):3533–3544

    Article  Google Scholar 

  16. Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2018) Multiobjec-tive optimization for computation offloading in fog computing. IEEE Internet Things J 5(1):283–294

    Article  Google Scholar 

  17. Liu L, Chang Z, Guo X (2018) Socially aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet of Things J 5(3):1869–1879

    Article  Google Scholar 

  18. Baccarelli E, Scarpiniti M, Momenzadeh A (2019) Ecomobifog–design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications. IEEE Access7 55565–55608

  19. Lee G, Saad W, Bennis M (2019) An online optimization framework for distributed fog network formation with minimal latency. IEEE Trans Wirel Commun 18(4):2244–2258

    Article  Google Scholar 

  20. Balevi E, Gitlin RD (2018) Optimizing the number of fog nodes for Cloud-Fog-Thing networks. IEEE Access6 11173–11183

  21. Deng R, Lu R, Lai C, Luan TH, Liang H (2016) Optimal workload allocation in Fog-Cloud computing toward balanced delay and power consumption. IEEE Internet Things J 3(6):1171– 1181

    Google Scholar 

  22. Gu Y, Chang Z, Pan M, Song L, Han Z (2018) Joint Radio and Computational Resource Allocation in IoT Fog Computing. IEEE Trans Veh Technol 67(8):7475–7484

  23. Joint optimization of transmission and processing delay in fog computing access networks (China, 2017)

  24. Khakimov A, Muthanna A, Muthanna MSA (2018) Studyof fog computing structure. In: 2018 IEEE Conference of russian young researchers in electrical and electronic engineering. (EIConrus), Russia, pp 51-54

  25. Bonomi F, Milito R, Zhu J (2012). In: Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing. Helsinki, Finland, pp 13-16

  26. Yin L, Luo J, Luo H (2018) Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans Indust Inform 14(10):4712–4721

    Article  Google Scholar 

  27. Mtshali M, Kobo H, Dlamini S, Adigun M, Mudali P (2019) Multi-Objective Optimization approach for task scheduling in fog computing. In: 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). Winterton, South Africa, pp 1-6

  28. Zhai X, Guan X, Zhu C, Shu L, Yuan J (2018) Optimization algorithms for multiaccess green communications in internet of things. IEEE Internet Things J 5(3):1739–1748

    Article  Google Scholar 

  29. Zhai XB, Liu X, Zhu C, Zhu K, Chen B (2019) Fast admission control and power optimization with adaptive rates for communication fairness in wireless networks. IEEE Transactions on Mobile Computing PP(99):1–12

  30. Holland JH (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2(2):88–105

    Article  MathSciNet  Google Scholar 

  31. Linhares A (1998) Preying on optima: a predatory search strategy for combinatorial problems. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, pp 2974–2978

  32. Zhu Z, Zhang Z, ManW, Tong X, Qiu J, Li F (2018) A new beetle antennae search algorithm for multi-objective energy management in microgrid, (ICIEA), China

  33. Hojjati SH, Ebrahimzadeh A, Najimi M, Reihanian A (2016) Sensor selection for cooperative spectrum sensing in multiantenna sensor networks based on convex optimization and genetic algorithm. IEEE Sensors J 16(10):3486–3487

    Article  Google Scholar 

  34. Chang Y, Yuan X, Li B, Niyato D, Al-Dhahir N (2018) A joint unsupervised learning and genetic algorithm approach for topology control in Energy-Efficient Ultra-Dense wireless sensor networks. IEEE Commun Lett 22(11):2370–2373

    Article  Google Scholar 

  35. Hadji HE, Babes M (2016) Integrating Tabu Search in Particle Swarm Optimization for the frequency assignment problem. China Commun 13(3):137–155

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University(No. 2020D16), in part by the Provincial Key Research and Development Program of Jiangsu under Grant BE2019017, in part by the Six Talent Peaks project in Jiangsu under Grant DZXX-008, in part by the Open Research Fund Key Laboratory of Wireless Sensor Network and Communication, Chinese Academy of Sciences, under Grant 20190914, in part by National Natural Science Foundation of China under grant No. 61871370, in part by Natural Science Foundation of Shanghai under grant No. 18ZR1437500, and in part by The Hundred Talent Program of Chinese Academy of Sciences under grant No. Y86BRA1001.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fei Shen.

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

Li, X., Zang, Z., Shen, F. et al. Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks. Mobile Netw Appl 25, 2517–2526 (2020). https://doi.org/10.1007/s11036-020-01593-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-020-01593-5

Keywords

Navigation