Skip to main content

Advertisement

SpringerLink
Log in

Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Today, there exists a growing demand for Internet of Things (IoT) services in the form of vehicle networks, smart cities, augmented reality, virtual reality, positioning systems, and so on. Due to the considerable distance between the IoT devices and the central cloud, using this option may no longer be a suitable solution for delay-constraint tasks. To overcome these drawbacks, a complementary solution called fog computing, also known as the cloud at the edge is used. In this solution, nodes at the edge of the network provide resources for IoT applications. Although offloading tasks on the fog nodes save energy on IoT devices, it increases task response time. Therefore, making a trade-off between energy consumption and latency is crucial for IoT devices. Because offloading falls into the category of NP-hard knapsack problems, metaheuristic methods have been widely used in recent years. In this paper, we formulate the problem of joint optimization of energy consumption and latency in the form of a multi-objective problem and solve it using the non-dominant sorting genetic algorithm (NSGA-II) and Bees algorithm (BA). Also, to improve the quality of solutions, we combine each of these methods with a robust type of differential evolution approach called minimax differential evolution (MMDE). This combination moves the solutions to better areas and increases the convergence speed. The simulation results show that NSGA-based methods have remarkable robustness compared to BA-based methods in terms of significant criteria such as energy consumption, time delay, and so on. Our statistical analysis shows that both NSGA-based and BA-based metaheuristic methods not only do not significantly increase energy consumption but also drastically reduce response time.

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

Similar content being viewed by others

Data availability

The datasets generated during and analyzed during the current study are available in the [MENDELEY] repository, http://data.mendeley.com/datasets/m6v5kjp9fd/1.

References

  • Abbasi M, Pasand EM, Khosravi MR (2020) Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm. J Grid Comput 18:43–56

    Google Scholar 

  • Aboutorabi SJS, Rezvani MH (2020) An optimized meta-heuristic bees algorithm for players’ frame rate allocation problem in cloud gaming environments. Comput Games J 9(3):281–304

    Google Scholar 

  • Adhikari M, Gianey H (2019) Energy efficient offloading strategy in fog-cloud environment for IoT applications. Internet Things 6:100053

    Google Scholar 

  • Adhikari M, Srirama SN, Amgoth T (2019) Application offloading strategy for hierarchical fog environment through swarm optimization. IEEE Internet Things J 7(5):4317–4328

    Google Scholar 

  • Besharati R, Rezvani MH (2019) A prototype auction-based mechanism for computation offloading in fog-cloud environments. In: 2019 5th conference on knowledge based engineering and innovation (KBEI). IEEE, Tehran, Iran, pp 542–547

    Google Scholar 

  • Buyya R, Srirama SN (eds) (2019) Fog and edge computing: principles and paradigms. Wiley, Hoboken

    Google Scholar 

  • Caiza G, Saeteros M, Oñate W et al (2020) Fog computing at industrial level, architecture, latency, energy, and security: a review. Heliyon 6(4):e03706

    Google Scholar 

  • Chiti F, Fantacci R, Picano B (2018) A matching theory framework for tasks offloading in fog computing for IoT systems. IEEE Internet Things J 5(6):5089–5096

    Google Scholar 

  • De Maio V, Kimovski D (2020) Multi-objective scheduling of extreme data scientific workflows in Fog. Future Gener Comput Syst 106:171–184

    Google Scholar 

  • Deb K, Agrawal S, Pratap A et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  • Dinh THL, Kaneko M, Fukuda EH et al (2021) Energy efficient resource allocation optimization in fog radio access networks with outdated channel knowledge. IEEE Trans Green Commun Netw 5(1):146–159

    Google Scholar 

  • Djemai T, Stolf P, Monteil T et al (2019) A discrete particle swarm optimization approach for energy-efficient IoT services placement over fog infrastructures. In: 2019 18th international symposium on parallel and distributed computing (ISPDC). IEEE, Amsterdam, Netherlands, pp 32–40

    Google Scholar 

  • Elashri S, Azim A (2020) Energy-efficient offloading of real-time tasks using cloud computing. Cluster Comput 23:3273–3288

    Google Scholar 

  • Esfandiari S, Rezvani MH (2021) An optimized content delivery approach based on demand–supply theory in disruption-tolerant networks. Telecommun Syst 76:265–289

    Google Scholar 

  • Fisher GG (2002) Work/personal life balance: a construct development study. Diss Abstr Int Sect B: Sci Eng 63(1-B):575

    Google Scholar 

  • Ghobaei-Arani M, Souri A, Safara F et al (2020) An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans Emerg Telecommun Technol 31(2):e3770

    Google Scholar 

  • Gupta H, Vahid Dastjerdi A, Ghosh SK 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 47:1275–1296

    Google Scholar 

  • Huang X, Yang Y, Wu X (2019) A meta-heuristic computation offloading strategy for IoT applications in an edge-cloud framework. In: Proceedings of the 2019 3rd international symposium on computer science and intelligent control, Amsterdam, Netherlands, pp 1–6

  • Hussein MK, Mousa MH (2020) Efficient task offloading for IoT-based applications in fog computing using ant colony optimization. IEEE Access 8:37191–37201

    Google Scholar 

  • Jazayeri F, Shahidinejad A, Ghobaei-Arani M (2021) Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach. J Ambient Intell Human Comput 12:8265–8284

    Google Scholar 

  • Jiang YL, Chen YS, Yang SW, Wu CH (2019) Energy-efficient task offloading for time-sensitive applications in fog computing. IEEE Syst J 13(3):2930–2941

    Google Scholar 

  • Kaur M, Kumar V (2018) Parallel non-dominated sorting genetic algorithm-II-based image encryption technique. Imaging Sci J 66(8):453–462

    Google Scholar 

  • Kaur M, Singh D (2021) Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. J Ambient Intell Humaniz Comput 12(2):2483–2493

    Google Scholar 

  • Kaur M, Singh D, Kumar V (2020a) Color image encryption using minimax differential evolution-based 7D hyper-chaotic map. Appl Phys B 126(9):1–19

    Google Scholar 

  • Kaur M, Singh D, Sun K, Rawat U (2020b) Color image encryption using non-dominated sorting genetic algorithm with local chaotic search based 5D chaotic map. Future Gener Comput Syst 107:333–350

    Google Scholar 

  • Keshavarznejad M, Rezvani MH, Adabi S (2021) Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms. Cluster Comput. https://doi.org/10.1007/s10586-020-03230-y

    Article  Google Scholar 

  • Lahmar IB, Boukadi K (2020) Resource allocation in fog computing: a systematic mapping study. In: 2020 fifth international conference on fog and mobile edge computing (FMEC). IEEE, Paris, France, pp 86–93. https://doi.org/10.1109/FMEC49853.2020.9144705

    Chapter  Google Scholar 

  • Liu L, Chang Z, Guo X et al (2018) Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J 5(1):283–294

    Google Scholar 

  • Maity S, Mistry S (2020) Partial offloading for fog computing using P2P based file-sharing protocol.  In: Das H, Pattnaik P, Rautaray S, Li KC (eds) Progress in computing, analytics and networking. Advances in intelligent systems and computing, vol 1119. Springer, Singapore, pp 293–302

    Google Scholar 

  • Manasrah AM, Gupta BB (2019) An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Clust Comput 22(1):1639–1653

    Google Scholar 

  • Mebrek A, Merghem-Boulahia L, Esseghir M (2017) Efficient green solution for a balanced energy consumption and delay in the IoT-Fog-Cloud computing. In: 2017 IEEE 16th international symposium on network computing and applications (NCA). IEEE, pp 1–4. https://doi.org/10.1109/NCA.2017.8171359

    Chapter  Google Scholar 

  • Millham R, Agbehadji IE, Frimpong SO (2021) The paradigm of fog computing with bio-inspired search methods and the “5Vs” of big data. In: Fong S, Millham R (eds) Bio-inspired algorithms for data streaming and visualization, big data management, and fog computing. Springer tracts in nature-inspired computing. Springer, Singapore, pp 145–167

    Google Scholar 

  • Mohammadi A, Rezvani MH (2019) A novel optimized approach for resource reservation in cloud computing using producer–consumer theory of microeconomics. J Supercomput 75(11):7391–7425

    Google Scholar 

  • Ning Z, Dong P, Wang X, Hu X, Liu J, Guo L, Hu B, Kwok R, Leung VC (2020) Partial computation offloading and adaptive task scheduling for 5G-enabled vehicular networks. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2020.3025116

    Article  Google Scholar 

  • Parvizi E, Rezvani MH (2020) Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach. Cluster Comput 23:2945–2967

    Google Scholar 

  • Qiu X, Xu JX, Xu Y, Tan KC (2017) A new differential evolution algorithm for minimax optimization in robust design. IEEE Trans Cybern 48(5):1355–1368

    Google Scholar 

  • Salaht FA, Desprez F, Lebre A (2020) An overview of service placement problem in fog and edge computing. ACM Comput Surv (CSUR) 53(3):1–35

    Google Scholar 

  • Seada H, Deb K (2015) U-NSGA-III: a unified evolutionary algorithm for single, multiple, and many-objective optimization. In: Gaspar-Cunha A, Henggeler Antunes C, Coello C (eds) Evolutionary multi-criterion optimization. EMO 2015. Lecture notes in computer science, vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_3

    Chapter  Google Scholar 

  • Shahidinejad A, Ghobaei‐Arani M (2020) Joint computation offloading and resource provisioning for edge‐cloud computing environment: a machine learning‐based approach. Softw Pract Exp 50:2212–2230

    Google Scholar 

  • Shah-Mansouri H, Wong VW (2018) Hierarchical fog-cloud computing for IoT systems: a computation offloading game. IEEE Internet Things J 5(4):3246–3257

    Google Scholar 

  • Shakarami A, Ghobaei-Arani M, Masdari M, Hosseinzadeh M (2020) A survey on the computation offloading approaches in mobile edge/cloud computing environment: a stochastic-based perspective. J Grid Comput 18:639–671

    Google Scholar 

  • Subramaniam EVD, Krishnasamy V (2021) Energy aware smartphone tasks offloading to the cloud using gray wolf optimization. J Ambient Intell Human Comput 12:3979–3987

    Google Scholar 

  • Sun M, Xu X, Tao X et al (2020) Large-scale user-assisted multi-task online offloading for latency reduction in D2D-enabled heterogeneous networks. IEEE Trans Netw Sci Eng 7(4):2456–2467

    MathSciNet  Google Scholar 

  • Tavakoli-Someh S, Rezvani MH (2019) Multi-objective virtual network function placement using NSGA-II meta-heuristic approach. J Supercomput 75(10):6451–6487

    Google Scholar 

  • Tušar T, Filipič B (2007) Differential evolution versus genetic algorithms in multiobjective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization. EMO 2007. Lecture notes in computer science, vol 4403. Springer, Berlin, Heidelberg, pp 257–271

    Google Scholar 

  • Wang J, Lv T, Huang P, Mathiopoulos PT (2020) Mobility-aware partial computation offloading in vehicular networks: a deep reinforcement learning based scheme. China Commun 17(10):31–49

    Google Scholar 

  • Yang T, Feng H, Yang C et al (2018) Multivessel computation offloading in maritime mobile edge computing network. IEEE Internet Things J 6(3):4063–4073

    Google Scholar 

  • Yassine A, Singh S, Hossain MS et al (2019) IoT big data analytics for smart homes with fog and cloud computing. Future Gener Comput Syst 91:563–573

    Google Scholar 

  • Yu Y, Bu X, Yang K, Wu Z, Han Z (2018) Green large-scale fog computing resource allocation using joint benders decomposition, Dinkelbach algorithm, ADMM, and branch-and-bound. IEEE Internet Things J 6(3):4106–4117

    Google Scholar 

  • Zhang G, Shen F, Liu Z et al (2019) FEMTO: fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Internet Things J 6(3):4388–4400. https://doi.org/10.1109/JIOT.2018.2887229

    Article  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Vahid Jafari & Mohammad Hossein Rezvani

Authors
  1. Vahid Jafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hossein Rezvani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hossein Rezvani.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states 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'' (in PDF at the end of the article below the references.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jafari, V., Rezvani, M.H. Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm. J Ambient Intell Human Comput 14, 1675–1698 (2023). https://doi.org/10.1007/s12652-021-03388-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03388-2

Keywords

Search

Navigation