Data Scheduling and Resource Optimization for Fog Computing Architecture in Industrial IoT

  • Wei WangEmail author
  • Guanyu Wu
  • Zhe Guo
  • Liang Qian
  • Lianghui Ding
  • Feng Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)


In the actual industrial environment, how the system processes and analyzes big data stably in real time is the main challenge of industrial Internet of Things (IIoT) currently. Although fog computing, as a significant extension of cloud computing, provides a distributed solution to real-time data processing in the industrial environment, it is an unavoidable problem that non-negligible network latency and fluctuations in the industrial network and limited computing power of fog nodes make it difficult to process big data timely and stably. We integrate the decentralized resources of fog nodes to form a cluster which can deliver sufficient processing power to deal with a complicated computational task. And then we propose an optimal data scheduling policy with multiple communication channels to minimize real-time processing delay and increase stability of the system. A series of experiments are designed to evaluate the behaviors with three different scheduling policies. Simulation results show that over 15% performance gain, in the system adopted optimal data scheduling policy, can be achieved according to different working scenarios, in which network communication conditions and processing power make the decisive contributions. Meanwhile, the fluctuating range of system delay curve is lower with the fluctuating of the network than the other two, which means the system has a better stability.


Fog computing IIoT Real-time Stability Optimal scheduling 



This paper is supported in part by NSFC China (61771309, 61671301, 61420106008, 61521062), Shanghai Key Laboratory Funding (STCSM15DZ2270400), CETC Key Laboratory of Data Link Technology Foundation (CLDL-20162306), and Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (YG2017QN47).


  1. 1.
    O’Donovan, P., Gallagher, C., Bruton, K., et al.: A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications. Manuf. Lett. 15, 139–142 (2018)CrossRefGoogle Scholar
  2. 2.
    Gonçalves, P., Ferreira, J., Pedreiras, P., Corujo, D.: Adapting SDN datacenters to support Cloud IIoT applications. In: 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4. IEEE (2015)Google Scholar
  3. 3.
    Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., Bilbao, J.: Fog computing based efficient IoT scheme for the Industry 4.0. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1–6 (2017)Google Scholar
  4. 4.
    Ghaderi, J., Shakkottai, S., Srikant, R.: Scheduling storms and streams in the cloud. In: ACM SIGMETRICS Performance Evaluation Review, vol. 43, no. 1, pp. 439–440 (2015)Google Scholar
  5. 5.
    da Silva Morais, T.: Survey on frameworks for distributed computing: Hadoop, Spark and storm. In: Proceedings of the 10th Doctoral Symposium in Informatics Engineering-DSIE, vol. 15 (2015)Google Scholar
  6. 6.
    Mukherjee, M., Shu, L., Wang, D., Li, K., Chen, Y.: A fog computing-based framework to reduce traffic overhead in large-scale industrial applications. In: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1008–1009. IEEE (2017)Google Scholar
  7. 7.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16 (2012)Google Scholar
  8. 8.
    Yan, J., Meng, Y., Lu, L., et al.: Industrial big data in an industry 4.0 environment: challenges, schemes and applications for predictive maintenance. IEEE Access PP(99), 1 (2017)Google Scholar
  9. 9.
    Wu, D., et al.: A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. J. Manuf. Syst. 43, 25–34 (2017)CrossRefGoogle Scholar
  10. 10.
    Su, K., Li, J., Fu, H.: Smart city and the applications. In: 2011 International Conference on Electronics, Communications and Control (ICECC), pp. 1028–1031. IEEE (2011)Google Scholar
  11. 11.
    Zhang, W., Zhang, Z., Chao, H.C.: Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management. IEEE Commun. Mag. 55(12), 60–67 (2017)CrossRefGoogle Scholar
  12. 12.
    Lin, C.C., Yang, J.W.: Cost-efficient deployment of fog computing systems at logistics centers in industry 4.0. IEEE Trans. Ind. Inf. PP(99), 1 (2018)MathSciNetGoogle Scholar
  13. 13.
    Yen, C.T., Liu, Y.C., Lin, C.C., Kao, C.C., Wang, W.B., Hsu, Y.R.: Advanced manufacturing solution to industry 4.0 trend through sensing network and cloud computing technologies. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1150–1152. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Wang
    • 1
    • 2
    • 3
    Email author
  • Guanyu Wu
    • 1
  • Zhe Guo
    • 2
    • 3
  • Liang Qian
    • 1
  • Lianghui Ding
    • 1
  • Feng Yang
    • 1
  1. 1.Department of Electronic EngineeringShanghai JiaoTong UniversityShanghaiChina
  2. 2.Shanghai Microwave Research InstituteShanghaiChina
  3. 3.CETC Key Laboratory of Data Link TechnologyShanghaiChina

Personalised recommendations