Skip to main content

Advertisement

Springer Nature Link
Log in

Multi-objective Optimization of Resource Scheduling in Fog Computing Using an Improved NSGA-II

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In conventional cloud computing technology, cloud resources are provided centrally by large data centers. For the exponential growth of cloud users, some applications, such as health monitoring and emergency response with the requirements of real-time and low-latency, cannot achieve efficient resource support. Therefore, fog computing technology has been proposed, where cloud services can be extended to the edge of the network to decrease the network congestion. In fog computing, the idle resources within many distributed devices can be used for providing services. An effective resource scheduling scheme is important to realize a reasonable management for these heterogeneous resources. Therefore, in this paper, a two-level resource scheduling model is proposed. In addition, we design a resource scheduling scheme among fog nodes in the same fog cluster based on the theory of the improved non-dominated sorting genetic algorithm II (NSGA-II), which considers the diversity of different devices. MATLAB simulation results show that our scheme can reduce the service latency and improve the stability of the task execution effectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Evans, D. (2011). The internet of things: How the next evolution of the internet is changing everything[J]. CISCO White Paper, 2011(1), 1–11.

    Google Scholar 

  2. Bonomi, F. (2011). Connected vehicles, the internet of things, and fog computing[C]. In The eighth ACM international workshop on vehicular inter-networking (VANET), Las Vegas, USA (pp. 13–15).

  3. Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your way in the fog: Towards a comprehensive definition of fog computing[J]. ACM SIGCOMM Computer Communication Review, 44(5), 27–32.

    Article  Google Scholar 

  4. Gupta, H., Dastjerdi, A. V., Ghosh, S. K., et al. (2016). iFogSim: a toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments[J]. arXiv preprint arXiv:1606.02007.

  5. Aazam, M., & Huh, E. N. (2015). Dynamic resource provisioning through fog micro datacenter[C]. In 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops) (pp. 105–110). IEEE.

  6. Stojmenovic, I., & Wen, S. (2014). The fog computing paradigm: Scenarios and security issues[C]. In 2014 federated conference on computer science and information systems (FedCSIS) (pp. 1–8). IEEE.

  7. Hu, P., Ning, H., Qiu, T., et al. (2017). Fog computing-based face identification and resolution scheme in internet of things[J]. IEEE Transactions on Industrial Informatics, 13(4), 1910–1920.

  8. Pham, X. Q., & Huh, E. N. (2016). Towards task scheduling in a cloud-fog computing system[C]. In 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) (pp. 1–4). IEEE.

  9. Sarkar, S., & Misra, S. (2016). Theoretical modelling of fog computing: A green computing paradigm to support IoT applications[J]. IET Networks, 5(2), 23–29.

    Article  Google Scholar 

  10. Nishio, T., Shinkuma, R., Takahashi, T., et al. (2013). Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud[C]. In Proceedings of the first international workshop on Mobile cloud computing & networking (pp. 19–26). ACM.

  11. Al Faruque, M. A., & Vatanparvar, K. (2016). Energy management-as-a-service over fog computing platform[J]. IEEE Internet of Things Journal, 3(2), 161–169.

    Article  Google Scholar 

  12. Deng, R., Lu, R., Lai, C., et al. (2015). Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing[C]. In 2015 IEEE International Conference on Communications (ICC) (pp. 3909-3914). IEEE.

  13. Xu, Y., Li, K., He, L., et al. (2013). A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization[J]. Journal of Parallel and Distributed Computing, 73(9), 1306–1322.

    Article  Google Scholar 

  14. Xu, Y., Li, K., Khac, T. T., et al. (2012). A multiple priority queueing genetic algorithm for task scheduling on heterogeneous computing systems[C]. In 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS) (pp. 639–646). IEEE.

  15. Deb, K., Pratap, A., Agarwal, S., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  16. Aazam, M., & Huh, E. N. (2015). Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT[C]. In 2015 IEEE 29th international conference on advanced information networking and applications (AINA) (pp. 687–694). IEEE.

  17. Wang, D., Yang, Y., & Mi, Z. (2015). A genetic-based approach to web service composition in geo-distributed cloud environment[J]. Computers & Electrical Engineering, 43, 129–141.

    Article  Google Scholar 

  18. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance[J]. Journal of the American Statistical Association, 32(200), 675–701.

    Article  MATH  Google Scholar 

Download references

Acknowledgements

We thank American Journal Experts (AJE) for its linguistic assistance during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Yan Sun, Fuhong Lin & Haitao Xu

Authors
  1. Yan Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fuhong Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haitao Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Fuhong Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Lin, F. & Xu, H. Multi-objective Optimization of Resource Scheduling in Fog Computing Using an Improved NSGA-II. Wireless Pers Commun 102, 1369–1385 (2018). https://doi.org/10.1007/s11277-017-5200-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-5200-5

Keywords