Skip to main content

Advertisement

SpringerLink
Log in

QoS-Aware Service Discovery and Selection Management for Cloud-Edge Computing Using a Hybrid Meta-Heuristic Algorithm in IoT

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cloud-edge computing is an emerging computing model based on Service Oriented Architecture that provides reliable and available cloud services as scalable resources by collaborating fog nodes on Internet of Things (IoT) environments. One of the important issues on service discovery is energy efficiency and security for existing cloud providers and fog nodes. An optimal service discovery and selection approach as an NP-Hard problem can effective on decreasing time and cost in cloud providers to achieve through maximum capacity of Quality of Service (QoS) factors. To address of the above challenges, this paper focuses on above-mentioned outcomes and presents a QoS-aware cloud-edge service discovery and selection model in IoT environment. This model is evaluated based on a hybrid multi-objective meta-heuristic algorithm based on a Grey Wolf Optimizer and a Genetic Algorithm (GWO-GA) for evaluating QoS factors as non-functional properties. The proposed model is meant to guarantee QoS factors such as the response time, energy consumption and cost factors for the service discovery and selection problem in the IoT environment. Experimental showed that the proposed method performs 30% better than the other algorithms for decreasing cost factor.

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. Pan, J., & McElhannon, J. (2017). Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things Journal, 5(1), 439–449.

    Article  Google Scholar 

  2. Badawy, M. M., Ali, Z. H., & Ali, H. A. (2020). QoS provisioning framework for service-oriented internet of things (IoT). Cluster Computing, 23(2), 575–591. https://doi.org/10.1007/s10586-019-02945-x

    Article  Google Scholar 

  3. Alshafaey, M. S., Saleh, A. I., & Alrahamawy, M. F. (2021). A new cloud-based classification methodology (CBCM) for efficient semantic web service discovery. Cluster Computing. https://doi.org/10.1007/s10586-021-03245-z

    Article  Google Scholar 

  4. Zhang, M., Chen, Y., & Susilo, W. (2020). PPO-CPQ: A privacy-preserving optimization of clinical pathway query for e-healthcare systems. IEEE Internet of Things Journal, 7(10), 10660–10672.

    Article  Google Scholar 

  5. Quy, V. K., Nam, V. H., Linh, D. M., Ban, N. T., & Han, N. D. (2021). A survey of QoS-aware routing protocols for the MANET-WSN convergence scenarios in IoT networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08433-z

    Article  Google Scholar 

  6. Zenggang, X., Zhiwen, T., Xiaowen, C., Xue-min, Z., Kaibin, Z., & Conghuan, Y. (2019). “Research on image retrieval algorithm based on combination of color and shape features,” Journal of Signal Processing System, pp. 1–8.

  7. Sui, T., Marelli, D., Sun, X., & Fu, M. (2020). Multi-sensor state estimation over lossy channels using coded measurements. Automatica, 111, 108561.

    Article  MathSciNet  Google Scholar 

  8. Manshahia, M. S. (2019). Grey wolf algorithm based energy-efficient data transmission in internet of things. Procedia Comput. Sci., 160, 604–609.

    Article  Google Scholar 

  9. Hosseinzadeh, M., et al. (2020). A hybrid service selection and composition model for cloud-edge computing in the internet of things. IEEE Access, 8, 85939–85949. https://doi.org/10.1109/ACCESS.2020.2992262

    Article  Google Scholar 

  10. Souri, A., & Norouzi, M. (2015). A new probable decision making approach for verification of probabilistic real-time systems,” in Proceedings of the IEEE international conference on software engineering and service sciences, ICSESS, https://doi.org/10.1109/ICSESS.2015.7339003.

  11. Souri, A., Rahmani, A. M., Navimipour, N. J., & Rezaei, R. (2019). A symbolic model checking approach in formal verification of distributed systems. Human-centric Computing and Information Sciences. https://doi.org/10.1186/s13673-019-0165-x

    Article  Google Scholar 

  12. Pingale, R. P., & Shinde, S. N. (2021). Multi-objective Sunflower Based Grey Wolf Optimization Algorithm for Multipath Routing in IoT Network. Wireless Personal Communications, 117(3), 1909–1930. https://doi.org/10.1007/s11277-020-07951-6

    Article  Google Scholar 

  13. Al-Tashi, Q., Kadir, S. J. A., Rais, H. M., Mirjalili, S., & Alhussian, H. (2019). Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access, 7, 39496–39508.

    Article  Google Scholar 

  14. Ramollari, E., Kourtesis, D., Dranidis, D., & Simons, A. J. (2008). Towards reliable web service discovery through behavioural verification and validation.

  15. Li, B., Xiao, G., Lu, R., Deng, R., & Bao, H. (2019). On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices. IEEE Transactions on Industrial Informatics, 16(2), 854–864.

    Article  Google Scholar 

  16. Coti, C., Evangelista, S., & Klai, K. (2015). Queue-less, uncentralized resource discovery: formal specification and verification, in PNSE@ Petri Nets, pp. 315–316.

  17. Kifer, M. et al. (2004). A logical framework for web service discovery.

  18. Perera, C., & Vasilakos, A. V. (2016). A knowledge-based resource discovery for Internet of Things. Knowledge-Based System, 109, 122–136.

    Article  Google Scholar 

  19. Asghari, S., & Navimipour, N. J. (2019). Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm. Peer-to-Peer Networking and Applications, 12(1), 129–142.

    Article  Google Scholar 

  20. AlZubi, A., Alarifi, A., Al-Maitah, M., & Albasheer, O. A. (2020). Location assisted delay-less service discovery method for IoT environments. Computer Communications, 150, 405–412.

    Article  Google Scholar 

  21. Sikri, M. (2019). An adaptive and scalable framework for automated service discovery. Serv. Oriented Comput. Appl., 13(1), 67–79.

    Article  Google Scholar 

  22. Sim, S., & Choi, H. (2020). A study on the service discovery support method in the IoT environments. International Journal of Electrical Engineering Education, 57(1), 85–96.

    Article  Google Scholar 

  23. Pahl, M.-O., & Liebald, S. (2019). “A modular distributed iot service discovery”, in. IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2019, 448–454.

    Google Scholar 

  24. Liu, W., Nishio, T., Shinkuma, R., & Takahashi, T. (2014). Adaptive resource discovery in mobile cloud computing. Computer Communications, 50, 119–129.

    Article  Google Scholar 

  25. Wang, J., Zhu, P., He, B., Deng, G., Zhang, C., & Huang, X. (2021). An adaptive neural sliding mode control with ESO for uncertain nonlinear systems. International Journal of Control, Automation and Systems, 19(2), 687–697.

    Article  Google Scholar 

  26. Li, B., Liang, R., Zhou, W., Yin, H., Gao, H., & Cai, K. (2021). LBS Meets Blockchain: an Efficient Method with Security Preserving Trust in SAGIN,” IEEE Internet Things Journal.

  27. Feng, J., Liu, Z., & Feng, L. (2021). Identifying opportunities for sustainable business models in manufacturing: Application of patent analysis and generative topographic mapping. Sustainable production and consumption, 27, 509–522.

    Article  Google Scholar 

  28. Gong, C., Hu, Y., Gao, J., Wang, Y., & Yan, L. (2019). An improved delay-suppressed sliding-mode observer for sensorless vector-controlled PMSM. IEEE Transactions on Industrial Electronics, 67(7), 5913–5923.

    Article  Google Scholar 

  29. Zhang, L., Zheng, H., Wan, T., Shi, D., Lyu, L., & Cai, G. (2021). An integrated control algorithm of power distribution for islanded microgrid based on improved virtual synchronous generator, IET Renewable Power Generation.

  30. Kordestani, H., Zhang, C., Masri, S. F., & Shadabfar, M. (2021). An empirical time-domain trend line-based bridge signal decomposing algorithm using Savitzky-Golay filter. Structural Control and Health Monitoring., 28(7), e2750.

    Article  Google Scholar 

  31. Zhang, X., Wang, Y., Wang, C., Su, C.-Y., Li, Z., & Chen, X. (2018). Adaptive estimated inverse output-feedback quantized control for piezoelectric positioning stage. IEEE Transactions on Cybernetics, 49(6), 2106–2118.

    Article  Google Scholar 

  32. Weng, L., He, Y., Peng, J., Zheng, J., & Li, X. (2021). Deep cascading network architecture for robust automatic modulation classification. Neurocomputing, 455, 308–324.

    Article  Google Scholar 

  33. He, Y., Dai, L., & Zhang, H. (2020). Multi-branch deep residual learning for clustering and beamforming in user-centric network. IEEE Communications Letters, 24(10), 2221–2225.

    Article  Google Scholar 

  34. Cai, K., Chen, H., Ai, W., Miao, X., Lin, Q., & Feng, Q. (2021). Feedback convolutional network for intelligent data fusion based on near-infrared collaborative IoT technology, IEEE Transactions on Industrial Informatics.

  35. Li, B., Wu, Y., Song, J., Lu, R., Li, T., & Zhao, L. (2020). DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics, 17(8), 5615–5624.

    Article  Google Scholar 

  36. Wu, Z., Li, C., Cao, J., & Ge, Y. (2020). On Scalability of Association-rule-based recommendation: A unified distributed-computing framework. ACM Transactions on the Web, 14(3), 1–21.

    Google Scholar 

  37. Wang, D., Zhong, D., & Souri, A. (2021). Energy management solutions in the internet of things applications: Technical analysis and new research directions. Cognitive Systems Research, 67, 33–49. https://doi.org/10.1016/j.cogsys.2020.12.009

    Article  Google Scholar 

  38. Ni, T., Liu, D., Xu, Q., Huang, Z., Liang, H., & Yan, A. (2020). Architecture of cobweb-based redundant TSV for clustered faults. IEEE Transactions on Very Large Scale Integration (VLSI) System, 28(7), 1736–1739.

    Article  Google Scholar 

  39. Wu, Z., Song, A., Cao, J., Luo, J., & Zhang, L. (2017). Efficiently Translating Complex SQL Query to MapReduce Jobflow on Cloud. IEEE Trans. Cloud Comput., 8(2), 508–517.

    Article  Google Scholar 

  40. Lv, Z., Qiao, L., & Song, H. (2020). Analysis of the security of internet of multimedia things. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(3s), 1–16.

    Article  Google Scholar 

  41. Lv, Z., Lou, R., Li, J., Singh, A. K., & Song, H. (2021). Big data analytics for 6G-enabled massive internet of things. IEEE Internet of Things Journal, 8(7), 5350–5359.

    Article  Google Scholar 

  42. Xiao, N., et al. (2021). A diversity-based selfish node detection algorithm for socially aware networking. Journal of Signal Processing System, 93(7), 811–825.

    Article  Google Scholar 

  43. Lv, Z., Qiao, L., Li, J., & Song, H. (2020). Deep-learning-enabled security issues in the internet of things. IEEE Internet of Things Journal, 8(12), 9531–9538.

    Article  Google Scholar 

Download references

Funding

There is no funding on this manuscript.

Author information

Authors and Affiliations

  1. Science and Technology Information Center of Hunan Institute of Science and Technology Information, Changsha, 410000, Hunan, China

    Ronghan Wang

  2. Education Information Platform of Orient Science and Technology College of Hunan Agricultural University, Changsha, 410128, Hunan, China

    Junwei Lu

Authors
  1. Ronghan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junwei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junwei Lu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

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

Wang, R., Lu, J. QoS-Aware Service Discovery and Selection Management for Cloud-Edge Computing Using a Hybrid Meta-Heuristic Algorithm in IoT. Wireless Pers Commun 126, 2269–2282 (2022). https://doi.org/10.1007/s11277-021-09052-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09052-4

Keywords

Search

Navigation