Skip to main content

Advertisement

Springer Nature Link
Log in

An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment

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

Abstract

In recent years, the rapid growth of IoT devices has led to an increase significantly the amount of data generated. Transferring a huge amount of datasets from IoT devices to remote cloud servers will result in high latency and bandwidth usage. Fog computing has emerged as an Internet-based distributed computing model to store datasets generated by IoT devices near the user. Since IoT devices generate continuously massive amounts of datasets, placing them on the storage fog nodes with various capabilities to reduce latency and costs of data access and increase reliability and availability of data datasets while satisfying the QoS requirements as one of the challenging tasks to be considered. This paper proposes a metaheuristic-based data replica placement mechanism using biogeography-based optimization (BBO) for data-intensive IoT applications on the fog ecosystem. Besides, we design an autonomous framework to illustrate transferring data replicas between IoT devices and storage fog nodes for data replica placement problem in the fog ecosystem. The obtained simulation results by varying the number of data replicas and fog nodes demonstrate that the proposed mechanism is a cost-effective solution and it increases the average reliability and availability by up 13% and 15% and reduces the total cost and the latency 25% and 3%, respectively, compared with the other baseline mechanisms.

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
€32.70 /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 includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig.4
Fig.5
Fig.6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

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

References

  • Alvarez F, Breitgand D, Griffin D, Andriani P, Rizou S, Zioulis N, Moscatelli F, Serrano J, Keltsch M, Trakadas P, Phan TK (2019) An edge-to-cloud virtualized multimedia service platform for 5G networks. IEEE Trans Broadcast 65(2):369–380

    Article  Google Scholar 

  • Alweshah M (2019) Construction biogeography-based optimization algorithm for solving classification problems. Neural Comput Appl 31(10):5679–5688

    Article  Google Scholar 

  • Aral A, Ovatman T (2018) A decentralized replica placement algorithm for edge computing. IEEE Trans Netw Serv Manage 15(2):516–529

    Article  Google Scholar 

  • Breitbach M, Schäfer D, Edinger J, Becker C (2019) Context-aware data and task placement in edge computing environments. In: 2019 IEEE international conference on pervasive computing and communications (PerCom). IEEE, pp 1–10

  • Chen Y, Deng S, Ma H, Yin J (2019) Deploying data-intensive applications with multiple services components on edge. Mobile Netw Appl 25:1–16

    Google Scholar 

  • Confais B, Parrein B, Lebre A (2018) A tree-based approach to locate object replicas in a fog storage infrastructure. In: 2018 IEEE global communications conference (GLOBECOM). IEEE, pp 1–6

  • Costa Filho JS, Cavalcante DM, Moreira LO, Machado JC (2020) An adaptive replica placement approach for distributed key-value stores. Concurr Comput Pract Exp 32(11):e5675

    Article  Google Scholar 

  • Dadashi Gavaber M, Rajabzadeh A (2021) MFP: an approach to delay and energy-efficient module placement in IoT applications based on multi-fog. J Ambient Intell Human Comput 12:7965–7981. https://doi.org/10.1007/s12652-020-02525-7

    Article  Google Scholar 

  • Devadas TJ, Thayammal S, Ramprakash A (2020) IoT data management, data aggregation and dissemination. Principles of internet of things (IoT) ecosystem: insight paradigm. Springer, Cham, pp 385–411

    Chapter  Google Scholar 

  • Goudarzi S, Anisi MH, Abdullah AH, Lloret J, Soleymani SA, Hassan WH (2019) A hybrid intelligent model for network selection in the industrial Internet of Things. Appl Soft Comput 74:529–546

    Article  Google Scholar 

  • Guerrero C, Lera I, Juiz C (2019) Optimization policy for file replica placement in fog domains. Concurr Comput Pract Exp 32:e5343

    Google Scholar 

  • Habibi P, Farhoudi M, Kazemian S, Khorsandi S, Leon-Garcia A (2020) Fog computing: a comprehensive architectural survey. IEEE Access 8:69105–69133

    Article  Google Scholar 

  • Huang T, Lin W, Li Y, He L, Peng S (2019) A latency-aware multiple data replicas placement strategy for fog computing. J Signal Process Syst 91(10):1191–1204

    Article  Google Scholar 

  • Karatas F, Korpeoglu I (2019) Fog-based data distribution service (F-DAD) for internet of things (IoT) applications. Futur Gener Comput Syst 93:156–169

    Article  Google Scholar 

  • Khorsand R, Ghobaei-Arani M, Ramezanpour M (2018) FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments. Softw Pract Exp 48(12):2147–2173

    Article  Google Scholar 

  • Kumari A, Tanwar S, Tyagi S, Kumar N, Parizi RM, Choo KKR (2019) Fog data analytics: a taxonomy and process model. J Netw Comput Appl 128:90–104

    Article  Google Scholar 

  • Li C, Tang J, Luo Y (2019a) Scalable replica selection based on node service capability for improving data access performance in edge computing environment. J Supercomput 75(11):7209–7243

    Article  Google Scholar 

  • Li C, Wang Y, Chen Y, Luo Y (2019b) Energy-efficient fault-tolerant replica management policy with deadline and budget constraints in edge-cloud environment. J Netw Comput Appl 143:152–166

    Article  Google Scholar 

  • Martin JP, Kandasamy A, Chandrasekaran K (2020) Mobility aware autonomic approach for the migration of application modules in fog computing environment. J Ambient Intell Humaniz Comput 11:1–20

    Article  Google Scholar 

  • Mayer R, Gupta H, Saurez E, Ramachandran U (2017) Fogstore: toward a distributed data store for fog computing. In: 2017 IEEE Fog World Congress (FWC). IEEE, pp 1–6

  • Monga SK, Ramachandra SK, Simmhan Y (2019) ElfStore: a resilient data storage service for federated edge and fog resources. In: 2019 IEEE international conference on web services (icws). IEEE, pp 336–345

  • Mukherjee M, Shu L, Wang D (2018) Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun Surv Tutorials 20(3):1826–1857

    Article  Google Scholar 

  • Naas MI, Parvedy PR, Boukhobza J, Lemarchand L (2017) iFogStor: an IoT data placement strategy for fog infrastructure. In: 2017 IEEE 1st international conference on fog and edge computing (ICFEC). IEEE, pp 97–104

  • Naas MI, Lemarchand L, Boukhobza J, Raipin P (2018a) A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure. In: Proceedings of the 33rd annual ACM symposium on applied computing, pp 767–774

  • Naas MI, Boukhobza J, Parvedy PR, Lemarchand L (2018b) An extension to ifogsim to enable the design of data placement strategies. In: 2018 IEEE 2nd international conference on fog and edge computing (ICFEC). IEEE, pp 1–8

  • Nikoui TS, Rahmani AM, Tabarsaied H (2019) Data management in fog computing. In: Fog and edge computing: principles and paradigms, pp 171–190

  • Pal R, Saraswat M (2019) Histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization. Appl Intell 49(9):3406–3424

    Article  Google Scholar 

  • Paraskevopoulos A, Dallas PI, Siakavara K, Goudos SK (2017) Cognitive radio engine design for IoT using real-coded biogeography-based optimization and fuzzy decision making. Wirel Pers Commun 97(2):1813–1833

    Article  Google Scholar 

  • PunithaIlayarani P, Dominic MM (2019) Anatomization of fog computing and edge computing. In: 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT). IEEE, pp 1–6

  • Reihanian A, Feizi-Derakhshi MR, Aghdasi HS (2017) Community detection in social networks with node attributes based on multi-objective biogeography based optimization. Eng Appl Artif Intell 62:51–67

    Article  Google Scholar 

  • Sangaiah AK, Bian GB, Bozorgi SM, Suraki MY, Hosseinabadi AAR, Shareh MB (2019) A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm. Soft Comput 24:1–13

    Google Scholar 

  • Sengupta S, Bhunia SS (2020) Secure data management in cloudlet assisted IoT enabled e-health framework in Smart City. IEEE Sens J 20:9581–9588

    Article  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(12):2212–2230

    Article  Google Scholar 

  • Shahidinejad A, Ghobaei-Arani M, Masdari M (2021) Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust Comput 24(1):319–342

    Article  Google Scholar 

  • Shao Y, Li C, Tang H (2019) A data replica placement strategy for IoT workflows in collaborative edge and cloud environments. Comput Netw 148:46–59

    Article  Google Scholar 

  • Silva DMAD, Asaamoning G, Orrillo H, Sofia RC, Mendes PM (2019) An analysis of fog computing data placement algorithms. In: Proceedings of the 16th EAI international conference on mobile and ubiquitous systems: computing, networking and services, pp 527–534

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Trakadas P, Simoens P, Gkonis P, Sarakis L, Angelopoulos A, Ramallo-González AP, Skarmeta A, Trochoutsos C, Calvο D, Pariente T, Chintamani K (2020) An artificial intelligence-based collaboration approach in industrial IoT manufacturing: key concepts. Archit Ext Potential Appl Sens 20(19):5480

    Google Scholar 

  • Zhang M, Jiang W, Zhou X, Xue Y, Chen S (2019) A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation. Soft Comput 23(6):2033–2046

    Article  Google Scholar 

  • Zheng Q, Li R, Li X, Shah N, Zhang J, Tian F, Chao KM, Li J (2016) Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Futur Gener Comput Syst 54:95–122

    Article  Google Scholar 

  • Zhou X, Liu Y, Li B, Sun G (2015) Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks. Phys A 436:430–442

    Article  MATH  Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

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

    Jaber Taghizadeh, Mostafa Ghobaei-Arani & Ali Shahidinejad

Authors
  1. Jaber Taghizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mostafa Ghobaei-Arani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Shahidinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MT, AS, MG-A conducted this research. MT: methodology, software, validation, writing original draft. AS: conceptualization, supervision, writing review and editing, formal analysis, project administration. MG-A: investigation, resources, data curation, visualization.

Corresponding author

Correspondence to Mostafa Ghobaei-Arani.

Ethics declarations

Conflict of interest

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

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taghizadeh, J., Ghobaei-Arani, M. & Shahidinejad, A. An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment. J Ambient Intell Human Comput 14, 3691–3711 (2023). https://doi.org/10.1007/s12652-021-03495-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03495-0

Keywords

Search

Navigation