Skip to main content
Log in

Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading

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

A Correction to this article was published on 21 June 2022

This article has been updated

Abstract

With the rapid development of Internet of things, the traditional city model is no longer applicable. Therefore, the emerging concept of smart city meets the needs of users. Smart medical system needs to meet the service needs of different priority users in emergency. In order to improve the service quality of users, we apply mobile edge computing technology to the smart medical system. In this article, we consider the priority of the offloading tasks and establish a priority-mobile edge cloud ecosystem (priority-MECE). The system taked into account user priorities, the combined cost value genereted by the delay and energy consumption was reduced. In the priority-MECE computing offloading system, an optimization problem is established with the task offloading cost as the optimization goal. The result of this optimization problem can provide an optimal offloading scheme for priority-MECE. In order to solve this optimization problem, we propose a priority constraint optimal offloading algorithm(priority-COFA) based on dynamic programming. In order to prove that the performance of this algorithm is better than random selection, we design a simulation experiment based on previous studies. Finally, the simulation results show that the proposed algorithm is superior to random offloading and no priority offloading.

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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Change history

References

  1. Liu P, Zhang Y, Fu T et al (2021) Intelligent Mobile Edge Caching for Popular Contents in Vehicular Cloud Toward 6G. IEEE Transactions on Vehicular Technology 70(6):5265–5274

    Article  Google Scholar 

  2. Yu K, Lin L, Alazab M, Tan L, Gu B (2020) Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5g-enabled intelligent transportation system. IEEE Trans Intell Trans Syst

  3. Chen M, Miao Y, Gharavi H, Hu L, Humar I (2019) Intelligent traffic adaptive resource allocation for edge computing-based 5g networks. IEEE Transactions on Cognitive Communications and Networking 6(2):499–508

    Article  Google Scholar 

  4. Miao Y, Song J, Wang H, Hu L, Hassan MM, Chen M (2020) Smart micro-gas: A cognitive micro natural gas industrial ecosystem based on mixed blockchain and edge computing. IEEE Internet of Things Journal 8(4):2289–2299

    Article  Google Scholar 

  5. Chen M, Hao Y, Gharavi H, Leung VC (2019) Cognitive information measurements: A new perspective. Information Sciences 505:487–497

    Article  MathSciNet  Google Scholar 

  6. Muhammad G, Hossain MS, Yassine A (2019) Tree-based deep networks for edge devices. IEEE Transactions on Industrial Informatics 16(3):2022–2028

    Article  Google Scholar 

  7. Khan S, Teng Y, Cui J (2021) Pedestrian traffic lights classification using transfer learning in smart city application. In: 2021 13th International conference on communication software and networks (ICCSN), pp 352–356

  8. Xu X, Huang Q, Yin X, Abbasi M, Khosravi MR, Qi L (2020) Intelligent offloading for collaborative smart city services in edge computing. IEEE Internet of Things Journal 7(9):7919–7927

    Article  Google Scholar 

  9. Chen M, Jiang Y, Guizani N, Zhou J, Tao G, Yin J, Hwang K (2020) Living with i-fabric: Smart living powered by intelligent fabric and deep analytics. IEEE Network 34(5):156–163

    Article  Google Scholar 

  10. Hao Y, Chen M, Cao D, Zhao W, Petrov I, Antonenko V, Smeliansky R (2020) Cognitive-caching: cognitive wireless mobile caching by learning fine-grained caching-aware indicators. IEEE Wireless Communications 27(1):100–106

    Article  Google Scholar 

  11. Lin H, Zeadally S, Chen Z, et al (2020) A survey on computation offloading modeling for edge computing. Journal of Network and Computer Applications 169:102781

  12. Heidari A, Jabraeil Jamali MA, Jafari Navimipour N, Akbarpour S (2020) Internet of things offloading: ongoing issues, opportunities, and future challenges. International Journal of Communication Systems 33(14):e4474

    Article  Google Scholar 

  13. Yu K-P, Tan L, Aloqaily M, Yang H, Jararweh Y (2021) Blockchain-enhanced data sharing with traceable and direct revocation in iiot. IEEE Trans Ind Informa

  14. Xia J, Fan L, Yang N, Deng TQ, Duong GKK, Nallanathan A (2020) Opportunistic access point selection for mobile edge computing networks. IEEE Transactions on Wireless Communications 20(1):695–709

    Article  Google Scholar 

  15. Ha D-B, Truong V-T, Lee Y (2021) Performance analysis for rf energy harvesting mobile edge computing networks with simo/miso-noma schemes. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 8(27):e2

    Article  Google Scholar 

  16. Edinger J, Breitbach M, Gabrisch N, Schafer D, Becker C, Rizk A (2021) Decentralized low-latency task scheduling for ad-hoc computing. In: 2021 IEEE International parallel and distributed processing symposium (IPDPS), pp 776–785

  17. Liu Y, Hu Q, Cai Y et al (2021) Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems. IEEE Communications Letters 25(9):3046–3050

    Article  Google Scholar 

  18. Dai M, Su Z, Xu Q, Zhang N (2021) Vehicle assisted computing offloading for unmanned aerial vehicles in smart city. IEEE Transactions on Intelligent Transportation Systems 22(3):1932–1944

    Article  Google Scholar 

  19. Huang H, Peng K, Xu X (2020) Collaborative computation offloading for smart cities in mobile edge computing. In: 2020 IEEE 13th International conference on cloud computing (CLOUD), pp 176–183

  20. Zhao B, Peng K, Zhang H, Xu X (2020) Energy- and time-efficient tasks offloading and dynamic resource allocation in smart city. In: 2020 International conferences on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData) and IEEE congress on cybermatics (Cybermatics), pp 705–712

  21. Ligo A, Peha JM, Ferreira P, Barros J (2015) Comparison between benefits and costs of offload of mobile internet traffic via vehicular networks. TPRC

  22. Hmimz Y, Chanyour T, El Ghmary M, Malki MOC (2021) Joint radio and local resources optimization for tasks offloading with priority in a mobile edge computing network. Pervasive and Mobile Computing 73:101368

    Article  Google Scholar 

  23. Tran-Dang H, Kim D-S (2021) Task priority-based resource allocation algorithm for task offloading in fog-enabled iot systems. In: 2021 International conference on information networking (ICOIN). IEEE, pp 674–679

  24. Banez RA, Tembine H, Li L et al (2020) Mean-field-type game-based computation offloading in multi-access edge computing networks. IEEE Transactions on Wireless Communications 19(12):8366–8381

    Article  Google Scholar 

  25. Xu J, Hao Z, Sun X (2019) Optimal offloading decision strategies and their influence analysis of mobile edge computing. Sensors 19(14):3231

    Article  Google Scholar 

  26. Vogeleer KD, Memmi G, Jouvelot P, Coelho F (2014) The energy/frequency convexity rule: Modeling and experimental validation on mobile devices

  27. Kwak J, Kim Y, Lee J, Chong S (2015) Dream: Dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE Journal on Selected Areas in Communications 33(12):2510–2523

    Article  Google Scholar 

  28. Liao Z, Peng J, Huang J, Wang J, Wang J, Sharma PK, Ghosh U (2020) Distributed probabilistic offloading in edge computing for 6g-enabled massive internet of things. IEEE Internet Things J 1–1

  29. Liu B, Zhou W, Jiang J, Wang K (2016) K-source: Multiple source selection for traffic offloading in mobile social networks. In: 2016 8th International conference on wireless communications signal processing (WCSP), pp 1–5

  30. Rimal BP, Maier M, Satyanarayanan M (2018) Experimental testbed for edge computing in fiber-wireless broadband access networks. IEEE Communications Magazine 56(8):160–167

    Article  Google Scholar 

  31. Hao Y, Miao Y, Chen M, Gharavi H, Leung V (2021) 6g cognitive information theory: A mailbox perspective. Big Data and Cognitive Computing 5(4):56

    Article  Google Scholar 

  32. Hao Y, Chen M, Gharavi H, Zhang Y, Hwang K (2020) Deep reinforcement learning for edge service placement in softwarized industrial cyber-physical system. IEEE Transactions on Industrial Informatics 17(8):5552–5561

    Article  Google Scholar 

  33. Li L, Zhang X, Liu K, Jiang F, Peng J (2018) An energy-aware task offloading mechanism in multiuser mobile-edge cloud computing. Mobile Information Systems, 2018

  34. Ma X, Zhang S, Li W, Zhang P, Lin C, Shen X (2017) Cost-efficient workload scheduling in cloud assisted mobile edge computing. In: 2017 IEEE/ACM 25th International symposium on quality of service (IWQoS), pp 1–10

Download references

Acknowledgements

This work was supported by the China National Science Foundation under Grant 62172079. This work was also funded by Natural Science Foundation of Hubei Province NO. 2020CFB697.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ying Hu.

Additional information

The original version of this article, published on 26 February 2022 needs modification on first author updated affiliation. Since the first author's work unit has changed, the information of "Hubei University of Education" should be removed. This is as per Author‘s preference.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Xiong, N., Zhang, Y. et al. Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading. Mobile Netw Appl 27, 1768–1777 (2022). https://doi.org/10.1007/s11036-022-01930-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-022-01930-w

Keywords

Navigation