Skip to main content

Advertisement

SpringerLink
Log in

An Autonomous Evolutionary Approach to Planning the IoT Services Placement in the Cloud-Fog-IoT Ecosystem

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

The growth of network-based computing devices and the development of massive services are both results of the Internet of Things (IoT) by deploying Internet-connected devices at the edge. These devices are limited in terms of storage and computing, where the resources they need are provided by the cloud computing paradigm. Unfortunately, the two-tier cloud-IoT architecture is not efficient enough to provide the resources needed for latency-sensitive applications. Consequently, Fog Computing Paradigm (FCP) has been proposed to complement cloud computing and support such IoT-generated applications at the network edge. Heterogeneity, geographical distribution, and large-scale of fog nodes need the development of new methodologies for deploying and running IoT applications on fog nodes. A collection of IoT services, which have varying Quality of Service (QoS) needs, make up applications for the IoT, so finding an autonomous IoT Fog Service Placement (IoT-FSP) scheme in such an infrastructure can be challenging. The current study presents a distributed conceptual computing framework to address this problem. It is based on an autonomous approach, which improves resource management in three-tier cloud-fog-IoT architecture. Besides, we use the Cuckoo Optimization Algorithm (COA) as a meta-heuristic approach to efficiently solve the FSP. We refer to the proposed strategy as the FSP-COA. The FSP-COA formulates the problem as a multi-objective problem to reconcile various objectives, including SLA violations, service latency, response time, fog utilization, service cost, and energy consumption. Finally, the performance of FSP-COA is evaluated compared to state-of-the-art algorithms using the iFogSim simulator. According to experiments, FSP-COA is more efficient than other algorithms regarding various metrics, including latency, energy and cost, and on average, outperforms the better of existing algorithms ranging from 4% to 16%.

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

Similar content being viewed by others

Data Availability

Data sharing not applicable to this manuscript as no datasets were generated or analyzed during the current study.

References

  1. Salaht, F. A., Desprez, F., Lebre, A., Prud’Homme, C., Abderrahim, M.: Service placement in fog computing using constraint programming. In: 2019 IEEE International Conference on Services Computing (SCC), pp. 19–27, IEEE, Milan, Italy, 8–13 July 2019

  2. Khezri, S., Faez, K., Osmani, A.: An intelligent sensor placement method to reach a high coverage in wireless sensor networks. Int. J. Grid High-Perform. Comput. (IJGHPC). 3(3), 54–68 (2011)

    Article  Google Scholar 

  3. Raghavendra, M. S., Chawla, P., & Rana, A.: A survey of optimization algorithms for fog computing service placement. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), pp. 259–262, IEEE, Noida, India, 4–5 June 2020

  4. Canali, C., Lancellotti, R.: Gasp: genetic algorithms for service placement in fog computing systems. Algorithms. 12(10), 201 (2019)

    Article  MathSciNet  Google Scholar 

  5. Osmani, A., Mohasefi, J.B., Gharehchopogh, F.S.: Enriched latent Dirichlet allocation for sentiment analysis. Expert. Syst. 37(4), e12527 (2020)

    Article  Google Scholar 

  6. Guerrero, C., Lera, I., Juiz, C.: Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures. Futur. Gener. Comput. Syst. 97, 131–144 (2019)

    Article  Google Scholar 

  7. Nasiri, E., Berahmand, K., Li, Y.: A new link prediction in multiplex networks using topologically biased random walks. Chaos, Solitons Fractals. 151, 111230 (2021)

    Article  MATH  Google Scholar 

  8. Wang, N., Osmani, A., Mirzaei, S.: Dynamic placement of virtual machines using an improved multi-objective teaching-learning based optimization algorithm in cloud. Trans. Emerg. Telecommun. Technol. e4529 (2022). https://doi.org/10.1002/ett.4529

  9. Hassan, H.O., Azizi, S., Shojafar, M.: Priority, network and energy-aware placement of IoT-based application services in fog-cloud environments. IET Commun. 14(13), 2117–2129 (2020)

    Article  Google Scholar 

  10. Yang, X. S., Deb, S.: Cuckoo Search Via Lévy Flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), pp. 210–214, IEEE, Coimbatore, India, 9–11 December 2009

  11. Nasiri, E., Berahmand, K., Samei, Z., Li, Y.: Impact of centrality measures on the common neighbors in link prediction for multiplex networks. Big Data. 10(2), 138–150 (2022)

    Article  Google Scholar 

  12. Lera, I., Guerrero, C., Juiz, C.: YAFS: a simulator for IoT scenarios in fog computing. IEEE Access. 7, 91745–91758 (2019)

    Article  Google Scholar 

  13. Santos, F., Immich, R., Madeira, E.R.: Multimedia services placement algorithm for cloud–fog hierarchical environments. Comput. Commun. 191, 78–91 (2022)

    Article  Google Scholar 

  14. Aslanpour, M.S., Dashti, S.E., Ghobaei-Arani, M., Rahmanian, A.A.: Resource provisioning for cloud applications: a 3-D, provident and flexible approach. J. Supercomput. 74(12), 6470–6501 (2018)

    Article  Google Scholar 

  15. Shakarami, A., Shahidinejad, A., Ghobaei-Arani, M.: An autonomous computation offloading strategy in Mobile edge computing: a deep learning-based hybrid approach. J. Netw. Comput. Appl. 178, 102974 (2021)

    Article  Google Scholar 

  16. Sharma, S., Saini, H.: Minimizing energy consumption and SLA violation in fog computing using artificial neural network. Int. J. Sensors Wirel. Commun. Control. 10(5), 640–648 (2020)

    Article  Google Scholar 

  17. Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. SOCA. 11(4), 427–443 (2017)

    Article  Google Scholar 

  18. Saeedi, S., Khorsand, R., Bidgoli, S.G., Ramezanpour, M.: Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Comput. Ind. Eng. 147, 106649 (2020)

    Article  Google Scholar 

  19. Gasmi, K., Dilek, S., Tosun, S., Ozdemir, S.: A survey on computation offloading and service placement in fog computing-based IoT. J. Supercomput. 78(2), 1983–2014 (2022)

    Article  Google Scholar 

  20. Rezaeipanah, A., Amiri, P., Nazari, H., Mojarad, M., Parvin, H.: An energy-aware hybrid approach for wireless sensor networks using re-clustering-based multi-hop routing. Wirel. Pers. Commun. 120(4), 3293–3314 (2021)

    Article  Google Scholar 

  21. Abualigah, L., Diabat, A.: A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications. Neural Comput. & Applic. 32(19), 15533–15556 (2020)

    Article  Google Scholar 

  22. Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft. Comput. 22(2), 387–408 (2018)

    Article  Google Scholar 

  23. Rezaeipanah, A., Nazari, H., Ahmadi, G.: A hybrid approach for prolonging lifetime of wireless sensor networks using genetic algorithm and online clustering. J. Comput. Sci. Eng. 13(4), 163–174 (2019)

    Article  Google Scholar 

  24. Holland, J.: Outline of control parameters for genetic algorithms. J. Assoc. Comput. Mach. 3, 297–314 (1962)

    Article  Google Scholar 

  25. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  26. Choi, J., Ahn, S.: Scalable service placement in the fog computing environment for the IoT-based smart city. J. Inf. Process. Syst. 15(2), 440–448 (2019)

    Google Scholar 

  27. Mahmud, R., Srirama, S.N., Ramamohanarao, K., Buyya, R.: Quality of experience (QoE)-aware placement of applications in fog computing environments. J. Parallel Distrib. Comput. 132, 190–203 (2019)

    Article  Google Scholar 

  28. Wen, Z., Yang, R., Garraghan, P., Lin, T., Xu, J., Rovatsos, M.: Fog orchestration for internet of things services. IEEE Internet Comput. 21(2), 16–24 (2017)

    Article  Google Scholar 

  29. Aburukba, R.O., AliKarrar, M., Landolsi, T., El-Fakih, K.: Scheduling Internet of Things requests to minimize delay in hybrid Fog–Cloud computing. Futur. Gener. Comput. Syst. 111, 539–551 (2020)

    Article  Google Scholar 

  30. Eyckerman, R., Mercelis, S., Marquez-Barja, J., Hellinckx, P.: Requirements for distributed task placement in the fog. Int. Things. 12, 100237 (2020)

    Article  Google Scholar 

  31. Salimian, M., Ghobaei-Arani, M., Shahidinejad, A.: Toward an autonomic approach for internet of things service placement using gray wolf optimization in the fog computing environment. Softw. Pract. Experience. 51(8), 1745–1772 (2021)

    Article  Google Scholar 

  32. Emami Khansari, M., Sharifian, S.: A modified water cycle evolutionary game theory algorithm to utilize QoS for IoT services in cloud-assisted fog computing environments. J. Supercomput. 76(7), 5578–5608 (2020)

    Article  Google Scholar 

  33. Faraji Mehmandar, M., Jabbehdari, S., Javadi, H.S., H.: A dynamic fog service provisioning approach for IoT applications. Int. J. Commun. Syst. 33(14), e4541 (2020)

    Article  Google Scholar 

  34. Salimian, M., Ghobaei-Arani, M., Shahidinejad, A.: An evolutionary multi-objective optimization technique to deploy the IoT services in fog-enabled networks: an autonomous approach. Appl. Artif. Intell. 36(1), e2008149 (2022)

    Article  Google Scholar 

  35. Zhao, D., Zou, Q., Boshkani Zadeh, M.: A QoS-aware IoT service placement mechanism in fog computing based on open-source development model. J. Grid Comput. 20(2), 1–29 (2022)

    Article  Google Scholar 

  36. Ghobaei-Arani, M., Shahidinejad, A.: A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Syst. Appl. 200, 117012 (2022)

    Article  Google Scholar 

  37. Ayoubi, M., Ramezanpour, M., Khorsand, R.: An autonomous IoT service placement methodology in fog computing. Softw. Pract. Experience. 51(5), 1097–1120 (2021)

    Article  Google Scholar 

  38. Etemadi, M., Ghobaei-Arani, M., Shahidinejad, A.: Resource provisioning for IoT services in the fog computing environment: an autonomic approach. Comput. Commun. 161, 109–131 (2020)

    Article  Google Scholar 

  39. Natesha, B.V., Guddeti, R.M.R.: Adopting elitism-based genetic algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment. J. Netw. Comput. Appl. 178, 102972 (2021)

    Article  Google Scholar 

  40. Rezaeipanah, A., Matoori, S.S., Ahmadi, G.: A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search. Appl. Intell. 51(1), 467–492 (2021)

    Article  Google Scholar 

  41. Kaliszewski, I., Podkopaev, D.: Simple additive weighting—a metamodel for multiple criteria decision analysis methods. Expert Syst. Appl. 54, 155–161 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

2022 Guangdong Educational Science Planning Project (Higher Education Special). 2022 Party Building Research Project of Guangdong University Party Building Research Association.

Author information

Authors and Affiliations

  1. Guangzhou College of Technology and Business, Guangzhou, 510800, Guangdong, China

    Xiaobin Hong, Jiali Zhang & Yerong Shao

  2. Department of Computer Engineering, Robat Karim Branch, Islamic Azad University, Robat Karim, Iran

    Yeganeh Alizadeh

Authors
  1. Xiaobin Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiali Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yerong Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yeganeh Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

Corresponding authors

Correspondence to Jiali Zhang or Yeganeh Alizadeh.

Ethics declarations

Conflict of Interest

We certify that there is no actual or potential conflict of interest in relation to this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, X., Zhang, J., Shao, Y. et al. An Autonomous Evolutionary Approach to Planning the IoT Services Placement in the Cloud-Fog-IoT Ecosystem. J Grid Computing 20, 32 (2022). https://doi.org/10.1007/s10723-022-09622-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-022-09622-1

Keywords

Search

Navigation