SpringerLink
Log in

QoS-Aware Service Placement for Fog Integrated Cloud Using Modified Neuro-Fuzzy Approach

  • Conference paper
  • First Online:
Soft Computing and Its Engineering Applications (icSoftComp 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1788))

  • 426 Accesses

Abstract

Fog computing provides an infrastructure for enhancing quality of services (QoS), especially for time-critical applications. The reach of the Internet of things (IoT) has extended to another dimensions, expanding from acquisition of data to device interconnections and to data-processing. This acceleration assimilate fog and cloud compuhting into a single system for improving QoS and resource utilization. Due to the heterogeneity of IoT devices, selecting suitable computation devices and allocating resources are substantial issues that need to be addressed for effective resource utilization. This work proposes a smart decision-making system for service placement based on the various parameters. The proposed work utilizes machine learning based techniques: clustering for the labelling of the services followed by neuro-fuzzy based ANFIS model for offloading the services. A 5-layered neuro-fuzzy inference model is implemented to represent as an intelligent decision-making system. This work provides a solution for the learning phase of ANFIS by employing a meta-heuristic-based algorithm. Three metaheuristic algorithms, i.e. GA-ANFIS, JAYA-ANFIS and PSO-ANFIS are implemented for the training of the ANFIS model. The effectiveness of the model has been examined for the prediction of computing layer for offloading of the services. The results are compared with each other as well as with the conventional gradient-based ANFIS model. Experiment shows that the evolutionary-based neuro-fuzzy models yield imperative results against gradient-based neuro-fuzzy.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alizadeh, M.R., Khajehvand, V., Rahmani, A.M., Akbari, E.: Task scheduling approaches in fog computing: a systematic review. Int. J. Commun. Syst. (IJCS) 33(16), e4583 (2020)

    Article  Google Scholar 

  2. Asemi, A., Baba, M., Haji Abdullah, R., Idris, N.: Fuzzy multi criteria decision making applications: a review study. In: Proceedings of International Conference, Computer Engineering and Mathematical Sciences (ICCEMS) (2014)

    Google Scholar 

  3. Aslinezhad, M., Malekijavan, A., Abbasi, P.: Adaptive neuro-fuzzy modeling of a soft finger-like actuator for cyber-physical industrial systems. J. Supercomput. 77(3), 2624–2644 (2021)

    Article  Google Scholar 

  4. Benmouiza, K., Cheknane, A.: Clustered anfis network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theor. Appl. Climatol. 137(1), 31–43 (2019)

    Article  Google Scholar 

  5. Chauhan, N., Banka, H., Agrawal, R.: Delay-aware application offloading in fog environment using multi-class Brownian model. Wirel. Netw. 27(7), 4479–4495 (2021)

    Article  Google Scholar 

  6. Garg, K., Chauhan, N., Agrawal, R.: Optimized resource allocation for fog network using neuro-fuzzy offloading approach. Arab. J. Sci. Eng. (AJSE) 47, 1–14 (2022)

    Google Scholar 

  7. 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 

  8. Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Tran. Mob. Comput. 20(4), 1298–1311 (2020)

    Article  Google Scholar 

  9. Guevara, J.C., Torres, R.D.S., da Fonseca, N.L.: On the classification of fog computing applications: a machine learning perspective. J. Netw. Comput. Appl. (JNCA) 159, 102596 (2020)

    Google Scholar 

  10. Gupta, S., Dileep, A.D.: Long range dependence in cloud servers: a statistical analysis based on google workload trace. Computing 102(4), 1031–1049 (2020)

    Article  MathSciNet  Google Scholar 

  11. Haznedar, B., Kalinli, A.: Training anfis using genetic algorithm for dynamic systems identification. Int. J. Intell. Syst. Appl. Eng. (IJISAE) 4(Special Issue–1), 44–47 (2016)

    Article  Google Scholar 

  12. Jang, J.S.: Anfis: adaptive-network-based fuzzy inference system. IEEE Tran. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  13. Khandelwal, M., et al.: Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng. Comput. 34(2), 307–317 (2018)

    Article  MathSciNet  Google Scholar 

  14. Liu, L., Chang, Z., Guo, X., Mao, S., Ristaniemi, T.: Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J. (IoT-J) 5(1), 283–294 (2017)

    Google Scholar 

  15. Maala, H.H., Yousif, S.A.: Cluster trace analysis for performance enhancement in cloud computing environments. J. Theor. Appl. Inf. Technol. (JTAIT) 97(7), 2019 (2019)

    Google Scholar 

  16. 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. (JPDC) 132, 190–203 (2019)

    Article  Google Scholar 

  17. Mechouche, J., Touihri, R., Sellami, M., Gaaloul, W.: Conformance checking for autonomous multi-cloud SLA management and adaptation. J. Supercomput. 78, 1–36 (2022)

    Article  Google Scholar 

  18. Meng, X., Wang, W., Zhang, Z.: Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE Access 5, 21355–21367 (2017)

    Article  Google Scholar 

  19. Momeni, E., Nazir, R., Armaghani, D.J., Maizir, H.: Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57, 122–131 (2014)

    Article  Google Scholar 

  20. Nayeri, Z.M., Ghafarian, T., Javadi, B.: Application placement in fog computing with AI approach: taxonomy and a state of the art survey. J. Netw. Comput. Appl. (JNCA) 185, 103078 (2021)

    Google Scholar 

  21. Qasem, S.N., Ebtehaj, I., Riahi Madavar, H.: Optimizing anfis for sediment transport in open channels using different evolutionary algorithms. J. Appl. Res. Water Wastewater (JARWW) 4(1), 290–298 (2017)

    Google Scholar 

  22. Rao, R.V., Waghmare, G.: A new optimization algorithm for solving complex constrained design optimization problems. Eng. Optim. 49(1), 60–83 (2017)

    Article  Google Scholar 

  23. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: Vision and challenges. IEEE Internet Things J. (IoT-J) 3(5), 637–646 (2016)

    Google Scholar 

  24. Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. Serv. Oriented Comput. Appl. 11(4), 427–443 (2017)

    Article  Google Scholar 

  25. Sonmez, C., Ozgovde, A., Ersoy, C.: Fuzzy workload orchestration for edge computing. IEEE Tran. Netw. Serv. Manag. 16(2), 769–782 (2019)

    Article  Google Scholar 

  26. Tadakamalla, U., Menasce, D.A.: Autonomic resource management for fog computing. IEEE Trans. Cloud Comput. 10, 2334–2350 (2021)

    Article  Google Scholar 

  27. Tong, L., Li, Y., Gao, W.: A hierarchical edge cloud architecture for mobile computing. In: 35th Annual IEEE International Conference on Computer Communications (INFOCOM), pp. 1–9. IEEE (2016)

    Google Scholar 

  28. Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., Wilkes, J.: Large-scale cluster management at google with borg. In: Proceedings of Tenth European Conference on Computer Systems (ECCS), pp. 1–17 (2015)

    Google Scholar 

  29. Vlamou, E., Papadopoulos, B.: Fuzzy logic systems and medical applications. AIMS Neurosci. 6(4), 266 (2019)

    Article  Google Scholar 

  30. Walia, N., Singh, H., Sharma, A.: Anfis: adaptive neuro-fuzzy inference system-a survey. Int. J. Comput. Appl. (IJCA) 123(13), 1–7 (2015)

    Google Scholar 

  31. Yi, S., Hao, Z., Qin, Z., Li, Q.: Fog computing: platform and applications. In: Third IEEE workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp. 73–78. IEEE (2015)

    Google Scholar 

  32. Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of workshop on Mobile Big Data (MBD), pp. 37–42 (2015)

    Google Scholar 

  33. Yousif, S., Al-Dulaimy, A.: Clustering cloud workload traces to improve the performance of cloud data centers. In: Proceedings of The World Congress on Engineering (WCE), vol. 1, pp. 7–10 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110 067, India

    Supriya Singh & D. P. Vidyarthi

Authors
  1. Supriya Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. P. Vidyarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Supriya Singh .

Editor information

Editors and Affiliations

  1. Charotar University of Science and Technology, Changa, Anand, Gujarat, India

    Kanubhai K. Patel

  2. University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  3. Charotar University of Science and Technology, Changa, Anand, India

    Atul Patel

  4. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, S., Vidyarthi, D.P. (2023). QoS-Aware Service Placement for Fog Integrated Cloud Using Modified Neuro-Fuzzy Approach. In: Patel, K.K., Santosh, K.C., Patel, A., Ghosh, A. (eds) Soft Computing and Its Engineering Applications. icSoftComp 2022. Communications in Computer and Information Science, vol 1788. Springer, Cham. https://doi.org/10.1007/978-3-031-27609-5_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-27609-5_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27608-8

  • Online ISBN: 978-3-031-27609-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation