Skip to main content

Advertisement

SpringerLink
Log in

Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity

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

Abstract

With the development for the technology of mobile edge computing (MEC) and the grave situation for the shortage of global energy, the problem of computation offloading in a cloud computing framework is getting more attention by network managers. In order to improve the experience quality of users and increase the energy efficiency of the system, we focus on the issue of task offloading strategy in MEC system. In this paper, we propose a task offloading strategy in the MEC system with a heterogeneous edge. By considering the execution and transmission of tasks under the task offloading strategy, we present an architecture for the MEC system. We establish a system model composed of M/M/1, M/M/c and M/M/\(\infty\) queues to capture the execution process of tasks in local mobile device (MD), MEC server and remote cloud servers, respectively. Moreover, by trading off the average delay of tasks, the energy consumption level of the MD and the offloading expend of the system, we construct a cost function for serving one task and formulate a joint optimization problem for the task offloading strategy accordingly. Furthermore, under the constraints of steady state and proportion scope, we use the Lagrangian function and the corresponding Karush–Kuhn–Tucker (KKT) condition to obtain the optimal task offloading strategy with the minimum system cost. Finally, we carry out numerical experiments on the MEC system to investigate the influence of system parameters on the task offloading strategy and to obtain the optimal results. The experiment results show that the task offloading strategy proposed in this paper can balance the average delay, the energy consumption level and the offloading expend with the optimal allocation ratio.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Taleb T, Samdanis K, Mada B et al (2017) On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun Surv Tutor 19:1657–1681

    Article  Google Scholar 

  2. Ullaha R, Rehmanb MAU, Naeemc MA et al (2020) ICN with edge for 5G: exploiting in-network caching in ICN-based edge computing for 5G networks. Future Gener Comput Syst 111:159–174

    Article  Google Scholar 

  3. Shaohua W, Xiang L, Yuan X et al (2020) Efficient computation offloading for internet of vehicles in edge computing-assisted 5G networks. J Supercomput 76:2518–2547

    Article  Google Scholar 

  4. You L, Liutong X (2019) The service computational resource management strategy based on edge-cloud collaboration. In: Proceedings of the IEEE 10th International Conference on Software Engineering and Service Science, pp 400–404

  5. Yousefpour A, Fung C, Tam N et al (2019) All one needs to know about fog computing and related edge computing paradigms: a complete survey. J Syst Archit 98:289–330

    Article  Google Scholar 

  6. Vahabi M, Ghazvini M, Mohd Fadlee AR et al (2007) Trade-off between energy consumption and target delay for wireless sensor network. In: Proceedings of the 2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, pp 545–549

  7. Weisong S, Jie C, Quan Z et al (2016) Edge computing: vision and challenges. IEEE Internet Th J 3:637–646

    Article  Google Scholar 

  8. Lichao Y, Heli Z, Xi L et al (2018) A distributed computation offloading strategy in small-cell networks integrated with mobile edge computing. IEEE/ACM Trans Net 26:2762–2773

    Article  Google Scholar 

  9. Yan W, Haibo G, Anqi F et al (2020) Computation offloading strategy based on deep reinforcement learning in cloud-assisted mobile edge computing. In: Proceedings of the IEEE 5th International Conference on Cloud Computing and Big Data Analytics, pp 108–113

  10. Arash B, Daniele T, Emanuele CG (2019) Centralized and distributed architectures for energy and delay efficient fog network-based edge computing services. IEEE Trans Green Commun Net 3:250–263

    Article  Google Scholar 

  11. Julius S, Dalius N (2017) Edge computing in IoT: Preliminary results on modeling and performance analysis. In: Proceedings of the IEEE 5th Workshop on Advances in Information, Electronic and Electrical Engineering, pp 1–4

  12. Xihua L, Xiaolong X, Yuan Y et al (2019) Energy-efficient computation offloading with privacy preservation for edge computing-enabled 5G networks. In: Proceedings of the 2019 International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data, pp 176–181

  13. Yongmin Z, Xiaolong L, Ju R et al (2020) Efficient computing resource sharing for mobile edge-cloud computing networks. IEEE/ACM Trans Net 28:1227–1240

    Article  Google Scholar 

  14. Ye Yinghui H, Rose Qingyang L et al (2020) Enhance latency-constrained computation in MEC networks using uplink NOMA. IEEE Trans Commun 68:2409–2425

    Article  Google Scholar 

  15. Danial ASS, Ping ZH, Hoon K (2018) Mobile edge computing: A promising paradigm for future communication systems. In: Proceedings of the TENCON 2018-2018 IEEE Region 10 Conference, pp 1183–1187

  16. Chunlin L, Hezhi S, Yi C et al (2019) Edge cloud resource expansion and shrinkage based on workload for minimizing the cost. Future Gener Comput Syst 101:327–340

    Article  Google Scholar 

  17. Mashhadi F, Salinas SA, Monroy AB et al (2020) Optimal auction for delay and energy constrained task offloading in mobile edge computing. Comput Net 183:107527

    Article  Google Scholar 

  18. Delfin S, Sivasanker NP, Nishant R et al (2019) Fog computing: a new era of cloud computing. In: Proceedings of the 3rd International Conference on Computing Methodologies and Communication, pp 1106–1111

  19. Xiaojuan W, Shangguang W, Ao Z et al (2017) MVR: an architecture for computation offloading in mobile edge computing. In: Proceedings of the 2017 IEEE International Conference on Edge Computing, pp 232–235

  20. Huasheng N, Yunfei L, Feifei S et al (2020) Heterogeneous edge computing open platforms and tools for Internet of Things. Future Gener Comput Syst 106:67–76

    Article  Google Scholar 

  21. Yichao C, Enchang S, Yanhua Z (2017) Joint optimization of transmission and processing delay in fog computing access networks. In: Proceedings of the 9th International Conference on Advanced Infocomm Technology. pp 155–158

  22. Wen-Hsing K, Yung-Cheng W (2019) An energy-saving edge computing and transmission scheme for IoT mobile devices. In: Proceedings of the IEEE 8th Global Conference on Consumer Electronics, pp 1–2

  23. Xin L, Jigang W, Long C (2018) Energy-efficient offloading in mobile edge computing with edge-cloud collaboration. In: Proceedings of the 2018 International Conference on Algorithms and Architectures for Parallel Processing, pp 460–475

  24. Ashkan Y, Genya I, Jue Jason P (2017) Fog computing: towards minimizing delay in the Internet of Things. In: Proceedings of the IEEE International Conference on Edge Computing, pp 17–24

  25. Zhenyu Z, Junhao F, Zheng C et al (2019) Energy-efficient edge computing service provisioning for vehicular networks: a consensus ADMM approach. IEEE Trans Veh Technol 68:5087–5099

    Article  Google Scholar 

  26. Yuan F, Baochun L, Bo L (2014) Price competition in an oligopoly market with multiple IaaS cloud providers. IEEE Trans Comput 63:59–73

    Article  MathSciNet  MATH  Google Scholar 

  27. Tianyu Y, Yao Z, Yulin H et al (2019) Energy minimization of delay-constrained offloading in vehicular edge computing networks. In: Proceedings of the 2019 IEEE Wireless Communications and Networking Conference Workshop, pp 1–6

  28. Wonsuk Y, Wonsik Y, Jong-Moon C (2020) Energy consumption minimization of smart devices for delay-constrained task processing with edge computing. In: Proceedings of the IEEE International Conference on Consumer Electronics, pp 1–3

  29. Yang Y, Kunlun W, Guowei Z et al (2018) MEETS: maximal energy efficient task scheduling in homogeneous fog networks. IEEE Internet Th J 5:4076–4087

    Article  Google Scholar 

  30. Kyaw TY, Madyan A, Rai PS et al (2019) Energy efficient multi-tenant resource slicing in virtualized multi-access edge computing. In: Proceedings of the 20th Asia-Pacific Network Operations and Management Symposium, pp 1–4

  31. Surong X, Chubo L, Kenli L et al (2020) System delay optimization for mobile edge computing. Future Gener Comput Syst 109:17–28

    Article  Google Scholar 

  32. Molin L, Tong C, Jiaxin Z et al (2019) D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In: Proceedings of the 20th International Conference on Parallel and Distributed Computing, Applications and Technologies. 90–95

  33. Nhu-Ngoc D, Yunseong L, Sungrae C et al (2017) Multi-tier multi-access edge computing: the role for the fourth industrial revolution. In: Proceedings of the 8th International Conference on Information and Communication Technology Convergence, pp 1280–1282

  34. Richard O, Olamilekan F, Muthucumaru M (2018) Opportunistic edge computing: concepts, opportunities and research challenges. Future Gener Comput Syst 89:633–645

    Article  Google Scholar 

  35. Mbacke BCS, Doudou F, Shigeru K et al (2020) Hierarchical load balancing and clustering technique for home edge computing. IEEE Access 8:127593–127607

    Article  Google Scholar 

  36. Shi Y (2020) A joint optimization scheme for task offloading and resource allocation based on edge computing in 5G communication networks. Comput Commun 160:759–768

    Article  Google Scholar 

  37. Lei L, Huijuan X, Xiong X et al (2019) Joint computation offloading and multiuser scheduling using approximate dynamic programming in NB-IoT edge computing system. IEEE Internet Th J 6:5345–5362

    Article  Google Scholar 

  38. Balos C, Vega DL De, Abuelhaj Z et al (2018) A2Cloud: An analytical model for application-to-cloud matching to empower scientific computing. In: Proceedings of the IEEE 11th International Conference on Cloud Computing, pp 548–555

  39. Wenchen Z, Weiwei F, Yangyang L et al (2019) Markov approximation for task offloading and computation scaling in mobile edge computing. Mob Inf Syst 2019:1–12

    Google Scholar 

  40. Juan L, Yuyi M, Jun Z et al (2016) Delay-optimal computation task scheduling for mobile-edge computing systems. In: Proceedings of the 2016 IEEE International Symposium on Information Theory, pp 1451–1455

  41. Shuo W, Xing Z, Yan Z et al (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access. 5:6757–6779

    Article  Google Scholar 

  42. Stefania S, Gesualdo S, Sergio B (2015) Joint optimization of radio and computational resources for multicell mobileedge computing. IEEE Trans Signal Inf Proc over Net 1:89–103

    Article  Google Scholar 

  43. Jeongho K, Okyoung C, Song C et al (2014) Dynamic speed scaling for energy minimization in delay-tolerant smartphone applications. In: Proceedings of the 2014 IEEE Conference on Computer Communications, pp 2292–2300

  44. Liqing L, Zheng C, Xijuan G (2018) Socially aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet Th J 5:1869–1879

    Article  Google Scholar 

  45. Anwesha M, Priti D, Debashis D et al (2018) C2OF2N: a low power cooperative code offloading method for femtolet-based fog network. J Supercomput 74:2412–2448

    Article  Google Scholar 

  46. Guanglin Z, Wenqian Z, Cao Y et al (2018) Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Trans Ind Inf 14:4642–4655

    Article  Google Scholar 

  47. Jiehui Z, Tianyao J, Mengshi L et al (2013) Constrained optimization applying decomposed unlimited point method based on KKT condition. In: Proceedings of the 5th Computer Science and Electronic Engineering Conference, pp 87–91

  48. Deepak S, Ahmad DB, Do-sang K (2016) KKT optimality conditions in interval valued multiobjective programming with generalized differentiable functions. Europ J Op Res 254:29–39

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation (No. 61872311, No. 61973261), China.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Wei Li & Shunfu Jin

  2. Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Wei Li & Shunfu Jin

Authors
  1. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shunfu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shunfu Jin.

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, W., Jin, S. Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity. J Supercomput 77, 12486–12507 (2021). https://doi.org/10.1007/s11227-021-03781-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03781-w

Keywords

Search

Navigation