Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective QoS-aware optimization for deployment of IoT applications on cloud and fog computing infrastructure

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) technology serves many industries to improve their performance. As such, utilizing far distant cloud datacenters to run time-sensitive IoT applications has become a great challenge for the sake of real-time interaction and accurate service delivery time requests. Therefore, the fog computing as a deployment approach of IoT applications has been presented in the edge network. However, inefficient deployment of applications’ modules on the fog infrastructure leads to physical resource and bandwidth dissipations, and debilitation of quality of service (QoS), and also increases the power consumption. When all application’s modules are highly utilized on a single fog node owing to the reduction in the power consumption, the level of service reliability is decreased. To obviate the problem, this paper takes the concept of fault tolerance threshold into account as a criterion to guarantee applications’ running reliability. This paper formulates deployment of IoT applications’ modules on fog infrastructure as a multi-objective optimization problem with minimizing both bandwidth wastage and power consumption approach. To solve this combinatorial problem, a multi-objective optimization genetic algorithm (MOGA) is proposed which considers physical resource utilization and bandwidth wastage rate in their objective functions along with reliability and application’s QoS in their constraints. To validate the proposed method, extensive scenarios have been conducted. The result of simulations proves that the proposed MOGA model has 18, 38, 9, and 43 percent of improvement against MODCS, MOGWO-I, MOGWO-II, and MOPSO in terms of total power consumption (TPC) and it has 6.4, 15.99, 28.15, and 15.43 dominance percent against them in term of link wastage rate (LWR), respectively.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Azimi S, Pahl C, Shirvani MH (2020) Particle swarm optimization for performance management in multi-cluster IoT edge architectures. In: International cloud computing conference (CLOSER), pp 328–337. https://doi.org/10.5220/0009391203280337.

  2. Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: “a platform for internet of things and analytics”. In: Big data and internet of things: a roadmap for smart environments. Springer, Berlin, pp 169–186. https://doi.org/10.1007/978-3-319-05029-4_7

  3. Andriopoulou F, Dagiuklas T, Orphanoudakis T (2017) Integrating IoT and fog computing for healthcare service delivery. In: Components and services for IoT platforms. Springer, Berlin, pp 213–232. https://doi.org/10.1007/978-3-319-42304-3_11

  4. Shi Y, Ding G, Wang H, Roman HE (2015) The fog computing service for healthcare. In: International Symposium on future information and communication technologies for ubiquitous healthcare, pp 70–74. https://doi.org/10.1109/Ubi-HealthTech.2015.7203325

  5. An OpenFog Architecture Overview, OpenFog (2017) https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf. Accessed 2017

  6. Farzai S, Hosseini Shirvani M, Rabbani M (2020) Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters. Sustain Comput Inform Syst 28:100374. https://doi.org/10.1016/j.suscom.2020.100374

  7. Hosseini Shirvani M (2020) A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems. Eng Appl Artif Intell 90:103501. https://doi.org/10.1016/j.engappai.2020.103501

  8. Taneja M, Davy A (2017) Resource-aware placement of IoT application modules in fog-cloud computing paradigm. In: Proc. of the IFIP/IEEE symposium on integrated network and service management, IM’15. IEEE, pp 1222–1228. https://doi.org/10.23919/INM.2017.7987464

  9. Venticinque S, Amato A (2018) A methodology for deployment of IoT application in fog. J Ambient Intell Humaniz Comput 1–22, https://doi.org/10.1007/s12652-018-0785-4

  10. Hong HJ, Tsai PH, Hsu CH (2016) Dynamic module deployment in a fog computing platform. In: 18th Asia-Pacific network operations and management symposium (APNOMS), pp 1–6. https://doi.org/10.1109/APNOMS.2016.7737202

  11. Ramzanpoor Y, Hosseini Shirvani M, Golsorkhtabaramiri M (2022) Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure. Complex Intell Syst 8:361–392. https://doi.org/10.1007/s40747-021-00368-z

    Article  Google Scholar 

  12. Pallewatta S, Kostakos V, Buyya R (2022) QoS-aware placement of microservices-based IoT applications in Fog computing environments. Futur Gener Comput Syst 131:121–136. https://doi.org/10.1016/j.future.2022.01.012

    Article  Google Scholar 

  13. Chen L et al (2021) IoT microservice deployment in edge-cloud hybrid environment using reinforcement learning. IEEE Internet Things J 8(16):12610–12622. https://doi.org/10.1109/JIOT.2020.3014970

    Article  Google Scholar 

  14. Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol 1–21. https://doi.org/10.1145/3186592

  15. Brogi A, Forti A (2017) QoS-aware deployment of IoT applications through the fog. iEEE Internet Things J 4:1185–1192. https://doi.org/10.1109/JIOT.2017.2701408

    Article  Google Scholar 

  16. Yangui S, Ravindran P, Bibani O, Glitho RH, Hadj-Alouane NB, Morrow MJ, Polakos PA (2016) A platform as-a-service for hybrid cloud/fog environments. In: 2016 IEEE international symposium on local and metropolitan area networks (LANMAN), pp 1–7.https://doi.org/10.1109/LANMAN.2016.7548853

  17. Ahmadighohandizi F, Systä K (2016) Application development and deployment for IoT devices. In: Proc. 4th Int’l workshop cloud for IoT (CL IoT 16). https://doi.org/10.1007/978-3-319-72125-5_6

  18. Chen BL, Huang SC, Luo YC, Chung YC, Chou J (2017) A dynamic module deployment framework for M2M platforms. In: IEEE 7th international symposium on cloud and service computing (SC2). IEEE, pp 194–200. https://doi.org/10.1109/SC2.2017.37

  19. Li F, Vögler M, Claeßens M, Dustdar S (2013) Towards automated iot application deployment by a cloud-based approach. In: 6th international conference on service-oriented computing and applications. IEEE, pp 61–68. https://doi.org/10.1109/SOCA.2013.12

  20. Saurez E, Hong K, Lillethun D, Ramachandran U, Ottenwalder B (2016) Incremental deployment and migration of geo-distributed situation awareness applications in the fog. In: DEBS 2016, pp 258–269. https://doi.org/10.1145/2933267.2933317

  21. Vögler M, Schleicher JM, Inzinger C, Dustdar S (2015) DIANE—dynamic IoT application deployment. In: IEEE International conference on mobile services, pp 298–305. https://doi.org/10.1109/MobServ.2015.49

  22. Akyildiz IF, Wang X, Wang W (2005) Wireless mesh networks: a survey. Comput Netw 47(4):445–487. https://doi.org/10.1016/j.comnet.2004.12.001

    Article  MATH  Google Scholar 

  23. Blaglazov A, Buyya R (2011) Optimal online deterministic and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machine in cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420. https://doi.org/10.1002/cpe.1867

    Article  Google Scholar 

  24. Arcangeli JP, Boujbel R, Leriche S (2015) Automatic deployment of distributed software systems: definitions and state of the art. J Syst Softw 3:198–218. https://doi.org/10.1016/j.jss.2015.01.040

    Article  Google Scholar 

  25. Luo J, Song W, Yin L (2018) Reliable virtual machine placement based on multi-objective optimization with traffic-aware algorithm in industrial cloud. IEEE Access 6:23043–23052. https://doi.org/10.1109/ACCESS.2018.2816983

    Article  Google Scholar 

  26. Hosseini Shirvani M, Gorji AB (2020) Optimization of automatic web services composition using genetic algorithm. Int J Cloud Comput 9(4):397–411. https://doi.org/10.1109/IC4.2015.7375538

    Article  Google Scholar 

  27. Li H, Zhu G, Zhao Y, Dai Y, Tian W (2017) Energy-efficient and QoS-aware model based resource consolidation in cloud data centers. Clust Comput 20:2793–2803. https://doi.org/10.1007/s10586-017-0893-5

    Article  Google Scholar 

  28. Saeedi P, Hosseini Shirvani M (2021) An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power efficient virtual machine consolidation in cloud data centers. Soft Comput 25:5233–5260. https://doi.org/10.1007/s00500-020-05523-1

    Article  Google Scholar 

  29. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi objective genetic algorithm: Nsga-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  30. Hosseini Shirvani M (2021) Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm. J Exp Theor Artif Intell 33(2):179–202. https://doi.org/10.1080/0952813X.2020.1725652

    Article  Google Scholar 

  31. Hosseini Shirvani M (2018) Web service composition in multi-cloud environment: a bi-objective genetic optimization algorithm. In: 2018 IEEE (SMC) International conference on innovations in intelligent systems and applications (INISTA). https://doi.org/10.1109/INISTA.2018.8466267

  32. Hosseini Shirvani M, Rahmani AM, Sahafi A (2018) An iterative mathematical decision model for cloud migration: a cost and security risk approach. Softw Pract Exp 48(3):449–485. https://doi.org/10.1002/spe.2528

    Article  Google Scholar 

  33. Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. J Expert Syst Appl. https://doi.org/10.1016/j.eswa.2015.10.039

    Article  Google Scholar 

  34. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. J Comput Oper Res. https://doi.org/10.1016/j.cor.2011.09.026

    Article  MathSciNet  MATH  Google Scholar 

  35. Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation (CEC'02). IEEE Publications, USA. https://doi.org/10.1109/CEC.2002.1004388

  36. Asghari Alaie Y, Hosseini Shirvani M, Rahmani AM (2023) A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach. J Supercomput 79:1451–1503. https://doi.org/10.1007/s11227-022-04703-0

    Article  Google Scholar 

Download references

Funding

There is no funding.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

    Mirsaeid Hosseini Shirvani

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

    Yaser Ramzanpoor

Authors
  1. Mirsaeid Hosseini Shirvani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaser Ramzanpoor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mirsaeid Hosseini Shirvani.

Ethics declarations

Conflict of interest

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

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

Hosseini Shirvani, M., Ramzanpoor, Y. Multi-objective QoS-aware optimization for deployment of IoT applications on cloud and fog computing infrastructure. Neural Comput & Applic 35, 19581–19626 (2023). https://doi.org/10.1007/s00521-023-08759-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08759-8

Keyword

Search

Navigation