Skip to main content

Caching-based task scheduling for edge computing in intelligent manufacturing

Abstract

Tasks have high requirements for response delay and security in intelligent manufacturing. Industrial data have the characteristics of high privacy. However, cloud services are difficult to implement for low latency-sensitive applications and privacy data tasks. Therefore, the offloading technology in edge computing can offload the computing tasks of terminal devices to the edge of the network, which can effectively reduce the delay and match the needs of intelligent manufacturing. Unreasonable task scheduling cannot meet the needs of real-time scheduling between edge servers and cloud servers. In this paper, we establish a joint low-delay optimization model of task scheduling and dynamic replacement-release caching (DRRC) mechanism, which couples a privacy selection strategy for tasks to protect privacy. Tasks are scheduled to different location by the privacy of sensitive data, which can improve the security of data and meet the calculation request of different tasks. DRRC mechanism caches tasks according to the size of the task and replaces it with the weight of the task data, and adds automatic release mechanism. To solve the task scheduling strategy, we design the improved genetic-differential evolution algorithm. Extensive simulations reveal that the proposed algorithm has a better performance in minimizing latency compared with other scheduling algorithms. At the same time, the caching mechanism has a better hit rate.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Sun W, Liu JJ, Yue YJ et al (2018) Double auction-based resource allocation for mobile edge computing in industrial internet of things. IEEE Trans Industr Inf, 4692–4701. https://doi.org/10.1109/TII.2018.2855746

  2. 2.

    Govindaraj K, John JP, Artemenko A et al (2019) Smart resource planning for live migration in edge computing for industrial scenario. In: 7th IEEE Interna-tional Conference on Mobile Cloud Computing, Services, and Engineering, pp 30–37. https://doi.org/10.1109/MobileCloud.2019.00012

  3. 3.

    Li XM, Wan JF, Dai HN et al (2019) A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Trans Industr Inf 15(7):4225–4234. https://doi.org/10.1109/TII.2019.2899679

    Article  Google Scholar 

  4. 4.

    Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Int Serv Appl 1(1):7–18. https://doi.org/10.1007/s13174-010-0007-6

    Article  Google Scholar 

  5. 5.

    Li CL, Wang CY, Luo YL (2020) An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment. J Supercomput 76(9):6941–6968. https://doi.org/10.1007/s11227-019-03133-9

    Article  Google Scholar 

  6. 6.

    Zhao M (2020) Survey on technology and application of edge computing. Comput Sci 47(S1):268–272

    Google Scholar 

  7. 7.

    Wang S, Zhang X, Zhang Y et al (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5(1):6757–6779. https://doi.org/10.1109/ACCESS.2017.2685434

    Article  Google Scholar 

  8. 8.

    Cui Y, He W, Ni C et al (2017) Energy-efficient resource allocation for cache-assisted mobile edge computing. In: IEEE 42nd Conference on Local Computer Networks (LCN), pp 640–648. https://doi.org/10.1109/LCN.2017.112

  9. 9.

    Hao YX, Chen M, Hu L et al (2018) Energy efficient task caching and offloading for mobile edge computing. IEEE Access 6(1):11365–11373. https://doi.org/10.1109/ACCESS.2018.2805798

    Article  Google Scholar 

  10. 10.

    Zhou J, Shen HJ, Lin ZY et al (2020) Research advances on privacy preserving in edge computing. J Comput Res Develop 57(10):2027–2051

    Google Scholar 

  11. 11.

    Feng WJ, Yang CH, Zhou XS (2019) Multi-user and multi-task offloading decision algorithms based on imbalanced edge cloud. IEEE Access 7(1):95970–95977. https://doi.org/10.1109/ACCESS.2019.2928377

    Article  Google Scholar 

  12. 12.

    Fang J, Zeng WZ (2020) Offloading strategy for edge computing tasks based on cache mechanism. In: Proceedings of the 6th International Conference on Computing and Artificial Intelligence, pp 129–134. https://doi.org/10.1145/3404555.3404575

  13. 13.

    Tran TX, Pompili D (2018) Adaptive bitrate video caching and processing in mobile-edge computing networks. IEEE Trans Mob Comput 18(9):1965–1978. https://doi.org/10.1109/WONS.2017.7888772

    Article  Google Scholar 

  14. 14.

    Zhang C, Pang H, Liu JC et al (2019) Toward edge-assisted video content intelligent caching with long short-term memory learning. IEEE Access 7(1):152832–152846. https://doi.org/10.1109/ACCESS.2019.2947067

    Article  Google Scholar 

  15. 15.

    Mehteroglu C, Durmus Y, Onur E (2017) Semantic edge caching and prefetching in 5G. In: 14th IEEE Annual Consumer Communications & Networking Conference, pp 692–695. https://doi.org/10.1109/CCNC.2017.7983215

  16. 16.

    Ren DW, Gui XL, Lu W et al (2018) Ghcc: Grouping-based and hierarchical collaborative caching for mobile edge computing. In: 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp 1–6

  17. 17.

    Bi SZ, Huang L, Angela Zhang YJ (2020) Joint optimization of service caching placement and computation offloading in mobile edge computing systems. IEEE Trans Wireless Commun 19(7):4947–4963. https://doi.org/10.1109/TWC.2020.2988386

    Article  Google Scholar 

  18. 18.

    Jalali F, Hinton K, Ayre R et al (2016) Fog computing may help to save energy in cloud computing. IEEE J Sel Areas Commun 34(5):1728–1739. https://doi.org/10.1109/JSAC.2016.2545559

    Article  Google Scholar 

  19. 19.

    Jeon Y, Baek H, Pack S (2021) Mobility-aware optimal task offloading in distributed edge computing. In: 2021 International Conference on Information Networking (ICOIN), pp 65–68. https://doi.org/10.1109/ICOIN50884.2021.9334008

  20. 20.

    Sarra M, Samia B, Khaled S et al (2019) New caching system under uncertainty for mobile edge computing. In: Fourth International Conference on Fog and Mobile Edge Computing. pp 129–134. https://doi.org/10.1109/FMEC.2019.8795356

  21. 21.

    Hamzah H, Le DT, Kim M et al (2021) Location-aware task offloading for MEC-based high mobility service. In: 2021 International Conference on Information Networking, pp 708–711. https://doi.org/10.1109/ICOIN50884.2021.9333924

  22. 22.

    Paschos G, Bastug E, Land I et al (2016) Wireless caching: technical misconceptions and business barriers. IEEE Commun Mag 54(8):16–22. https://doi.org/10.1109/MCOM.2016.7537172

    Article  Google Scholar 

  23. 23.

    Breslau L, Pei C, Li F et al (1999) Web caching and zipf-like distributions: evidence and implications. In: Infocom 99 Eighteenth Joint Conference of the IEEE Computer & Communications Societies, pp 126–134. https://doi.org/10.1109/INFCOM.1999.749260

  24. 24.

    Hou TT, Feng G, Qin S et al (2018) Proactive content caching by exploiting transfer learning for mobile edge computing. Int J Commun Syst 31(11):e3706. https://doi.org/10.1109/GLOCOM.2017.8254636

    Article  Google Scholar 

  25. 25.

    Li Q, Zhang YM, Pandharipande A et al (2019) Edge caching in wireless infostation networks: Deployment and cache content placement. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops, pp 1–6

  26. 26.

    Zhang N, Guo ST, Dong YF et al (2020) Joint task offloading and data caching in mobile edge computing networks. Comput Netw 182(1):107446. https://doi.org/10.1016/j.comnet.2020.107446

    Article  Google Scholar 

  27. 27.

    Chilukuri S, Pesch D (2021) NimbleCache - Low Cost, Dynamic cache allocation in constrained edge environments. In: 2021 IEEE Wireless Communications and Networking Conference, pp 1-7. https://doi.org/10.1109/WCNC49053.2021.9417473

  28. 28.

    Ndikumana A, Ullah S, LeAnh T et al (2017) Collaborative cache allocation and computation offloading in mobile edge computing. In: 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp 366–369. https://doi.org/10.1109/APNOMS.2017.8094149

  29. 29.

    Wu H, Luo Y, Li C (2020) Optimization of heat-based cache replacement in edge computing system. J Supercomput 77(3):2268–2301. https://doi.org/10.1007/s11227-020-03356-1

    Article  Google Scholar 

  30. 30.

    Ale L, Zhang N, Wu HC et al (2019) Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network. IEEE Internet Things J 6(3):5520–5530. https://doi.org/10.1109/JIOT.2019.2903245

    Article  Google Scholar 

  31. 31.

    Poularakis K, Iosifidis G, Tassiulas L (2013) Approximation caching and routing algorithms for massive mobile data delivery. In: IEEE Global Communications Conference (GLOBECOM), pp 3534–3539. https://doi.org/10.1109/GLOCOM.2013.6831621

  32. 32.

    Pham XQ, Nguyen TD, Nguyen V et al (2020) Joint service caching and task offloading in multi-access edge computing. IEEE Commun Lett, 965–969. https://doi.org/10.1109/LCOMM.2020.3034668

  33. 33.

    Huynh LNT, Pham QV, Nguyen TDT et al (2021) Joint computational offloading and data-content caching in NOMA-MEC Networks, IEEE. Access 12943–12954. https://doi.org/10.1109/ACCESS.2021.3051278

  34. 34.

    Kang L, Tang B, Zhang L, et al (2019) Mobility-aware and data caching-based task scheduling strategy in mobile edge computing. In: 2019 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking, pp 1071-1077. https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00153

  35. 35.

    Tang YY (2019) Minimizing energy for caching resource allocation in information-centric networking with mobile edge computing. In: 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, pp 301–304. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00062

  36. 36.

    Fang YQ, Xiao X, Ge JW (2019) Cloud computing task scheduling algorithm based on improved genetic algorithm. In: IEEE 3rd Information Technology, Networking. Electronic and Automation Control Conference (ITNEC), pp 852–856. https://doi.org/10.1109/ITNEC.2019.8728996

  37. 37.

    Cheng LX, Liu YY (2010) An algorithm for resource constrainted scheduling clock selection. J Comput Aided Des Graphics 22(02):240-246. https://doi.org/CNKI:SUN:JSJF.0.2010-02-011

Download references

Acknowledgements

This work was supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2021JQ-719), the Science and Technology Project of Shaanxi (Program No.2019ZDLGY07-08), the Young Teachers Research Foundation of Xi’an University of Posts and Telecommunications, and the Special Funds for Construction of Key Disciplines in Universities in Shaanxi.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Gang Wang.

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

Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Wang, G., Jin, X. et al. Caching-based task scheduling for edge computing in intelligent manufacturing. J Supercomput (2021). https://doi.org/10.1007/s11227-021-04071-1

Download citation

Keywords

  • Edge computing
  • Intelligent manufacturing
  • Task scheduling
  • Task caching
  • Privacy protection