Skip to main content

Advertisement

Springer Nature Link
Log in

Optimized fog community framework with advanced genetic algorithm for enhanced performance dynamics

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Accretion of performance dynamics of fog computing for IoT applications is still in its early stages. IoT applications are transitioning to fog computing for hastier edge-based computational need. Managing the fog-cloud continuum infrastructure on a large scale in a complicated network is a challenging task. Fog communities, that is a collection of fog devices, can facilitate the management of large fog domains. There is a notable deficiency within the approaches for organizing fog devices into an optimized fog communities. A fog community can operate independently from other fog communities; thus, fewer resources are sufficient for fog community management. An effective fog community framework can improve the efficiency of the system. In this context, we make use of cumulative hierarchical clustering as the foundation for defining the fog community framework, which generates a hierarchical arrangement of inter-related elements that represents all feasible community candidates for this framework. We advance the genetic algorithms (GA) and apply them to choose a subset of community candidates by means of correctness and fitness functions. The adaptive fuzzy algorithm efficiently places the services within each community. The primary objective of the proposed framework is to reduce the network’s response time and execution time for the adaptive fuzzy algorithm. Subsequently, reducing the migration cost of service placement among fog networks is the secondary objective of this work. To evaluate how well the proposed framework performs, we have implemented a merge sort-based multi-objective GA (MNSGA). The obtained result shows that the proposed framework outperforms the various available baseline frameworks by reducing response time, placement time, and migration cost for each experimental configuration.

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
Algorithm 1
Algorithm 2
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Data availability

The authors affirm that all data supporting the findings of this study are contained within the paper.

References

  1. Mayer R, et al (2017) Fogstore: toward a distributed data store for fog computing. In: 2017 IEEE Fog World Congress (FWC). IEEE

  2. Guerrero C, Isaac L, Carlos J (2022) Genetic-based optimization in fog computing: current trends and research opportunities. Swarm Evolut Comput 72:101094

    Article  Google Scholar 

  3. Bonomi F, et al (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing

  4. Skarlat O et al (2017) Optimized IoT service placement in the fog. Serv Oriented Comput Appl 11(4):427–443

    Article  Google Scholar 

  5. Skarlat O et al (2017) Towards QoS-aware fog service placement. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC). IEEE

  6. Talavera F, et al (2022) Genetic-based fog colony optimization hybridized with hierarchical clustering and its influence in the placement of fog services. arXiv preprint arXiv:2209.05794

  7. Ogundoyin SO, Kamil IA (2021) A lightweight authentication and key agreement protocol for secure fog-to-fog collaboration. In: 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). IEEE

  8. Murtagh F, Contreras P (2012) Algorithms for hierarchical clustering: an overview. Wiley Interdiscip Rev Data Min Knowl Discov 2(1):86–97

    Article  Google Scholar 

  9. Guerrero C, Isaac L, Carlos J (2018) On the influence of fog communities partitioning in fog application makespan. In: 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE

  10. Moreno J et al (2020) Merge nondominated sorting algorithm for many-objective optimization. IEEE Trans Cybern 51(12):6154–6164

    Article  Google Scholar 

  11. Margariti SV, Dimakopoulos VV, Tsoumanis G (2020) Modeling and simulation tools for fog computing—a comprehensive survey from a cost perspective. Future Internet 12(5):89

    Article  Google Scholar 

  12. Brogi A et al (2020) How to place your apps in the fog: state of the art and open challenges. Softw Pract Exp 50(5):719–740

    Article  Google Scholar 

  13. Baburao D, Pavankumar T, Prabhu CSR (2019) Survey on service migration, load optimization and load balancing in fog computing environment. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE

  14. Minh QT, et al (2017) Toward service placement on fog computing landscape. In: 2017 4th NAFOSTED Conference on Information and Computer Science. IEEE

  15. Shurman MM, Aljarah MK (2017) Collaborative execution of distributed mobile and IoT applications running at the edge. In: 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). IEEE

  16. Nikolopoulos V et al (2022) Context diffusion in fog communities: exploring autonomous fog node operation using ECTORAS. IoT 3(1):91–108

    Article  Google Scholar 

  17. Lordan F, Lezzi D, Badia RM (2021) community: parallel functions as a service on the cloud-edge continuum. In: Euro-Par 2021: Parallel Processing: 27th International Conference on Parallel and Distributed Computing, Lisbon, Portugal, September 1–3, 2021, Proceedings 27. Springer

  18. Tran QM et al (2020) Designed features for improving openness, scalability and programmability in the fog computing-based IoT systems. SN Comput Sci 1(4):1–12

    Article  Google Scholar 

  19. Hatti DI, Sutagundar AV (2021) Swarm intelligence based MSMOPSO for optimization of resource provisioning in Internet of Things. In: Recent Trends in Computational Intelligence Enabled Research. Academic Press, pp 61–82

  20. Guerrero C, Lera I, Juiz C (2019) A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Humaniz Comput 10:2435–2452

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Jafari V, Hossein RM (2021) Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm’’. J Ambient Intell Humaniz Comput 14:1–24

    Google Scholar 

  23. Tavousi F, Azizi S, Ghaderzadeh A (2022) A fuzzy approach for optimal placement of IoT applications in fog-cloud computing. Cluster Comput 25:1–18

    Article  Google Scholar 

  24. Patel KD, Bhalodia TM (2019) An efficient dynamic load balancing algorithm for virtual machine in cloud computing. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE

  25. Varghese B, Wang N (2022) Context-aware distribution of fog applications using deep reinforcement. J Netw Comput Appl 203:103354

    Article  Google Scholar 

  26. Mahmud R, Ramamohanarao K, Buyya R (2020) Application management in fog computing environments: a taxonomy, review and future directions. ACM Comput Surv (CSUR) 53(4):1–43

    Article  Google Scholar 

  27. Ogundoyin SO, Kamil IA (2021) Optimization techniques and applications in fog computing: an exhaustive survey. Swarm Evolut Comput 66:100937

    Article  Google Scholar 

  28. Zhang J, Luo Y (2017) Degree centrality, betweenness centrality, and closeness centrality in social network. In: 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017). Atlantis Press

  29. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197

    Article  Google Scholar 

  30. Sun Y, Lin F, Haitao X (2018) Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wirel Pers Commun 102(2):1369–1385

    Article  Google Scholar 

  31. Ali IM et al (2020) An automated task scheduling model using non-dominated sorting genetic algorithm II for fog-cloud systems. IEEE Trans Cloud Comput 10(4):2294

    Article  MathSciNet  Google Scholar 

  32. Gupta H et al (2017) iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments. Softw Pract Exp 47(9):1275–1296

    Article  Google Scholar 

Download references

Funding

The study was conducted without any financial assistance, grants.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, India

    Nilesh Kumar Verma & K. Jairam Naik

Authors
  1. Nilesh Kumar Verma
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. K. Jairam Naik
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the study’s conceptualization and design. Responsibility of NKV is conceptualization, methodology design, formal analysis, experimentation, result analysis, and writing of the original draft. Responsibility of KJN is supervision, substantial editing, and critical review of the manuscript. The final manuscript received unanimous approval from all authors.

Corresponding author

Correspondence to Nilesh Kumar Verma.

Ethics declarations

Conflict of interest

The authors possess no financial or proprietary affiliations with any of the materials discussed in this article.

Ethical approval

All authors have collectively examined and agreed upon the substance of the manuscript, expressing their enthusiasm for its publication in this scholarly journal.

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 (e.g. a society or other partner) 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

Verma, N.K., Naik, K.J. Optimized fog community framework with advanced genetic algorithm for enhanced performance dynamics. J Supercomput 80, 8202–8235 (2024). https://doi.org/10.1007/s11227-023-05769-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05769-0

Keywords