IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v340y2024i1d10.1007_s10479-022-04931-w.html
   My bibliography  Save this article

A reliability prediction model for a multistate cloud/edge-based network based on a deep neural network

Author

Listed:
  • Ding-Hsiang Huang

    (Tunghai University)

  • Cheng-Fu Huang

    (Feng Chia University)

  • Yi-Kuei Lin

    (National Yang Ming Chiao Tung University
    Chaoyang University of Technology
    China Medical University
    Asia University)

Abstract

Network reliability, named multistate stochastic cloud/edge-based network (MCEN) reliability afterwards, is defined as the probability that demands can be satisfied for an MCEN. It can be regarded as a performance indicator of the MCEN to measure the service capability. The concept of existing algorithms is to produce all of minimal system-state vectors for calculating MCEN reliability. However, such concept cannot response MCEN reliability in time when the MCEN scale becomes complicated in the Industry 4.0 environment. For providing MCEN reliability for decision making immediately, an architecture of a deep neural network (DNN) is developed to propose a prediction model for MCEN reliability such that MCEN capability with varied data can be learned promptly. To train the reliability prediction model, MCEN information is transformed to the suitable format, and the related information for DNN setting, including the determination of related functions, are defined with appropriate hyperparameters by using Bayesian Optimization. An illustrative case and a practical case of Amazon Web Service are provided to demonstrate the prediction model for MCEN reliability to show the availability and the efficiency.

Suggested Citation

  • Ding-Hsiang Huang & Cheng-Fu Huang & Yi-Kuei Lin, 2024. "A reliability prediction model for a multistate cloud/edge-based network based on a deep neural network," Annals of Operations Research, Springer, vol. 340(1), pages 271-287, September.
  • Handle: RePEc:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-022-04931-w
    DOI: 10.1007/s10479-022-04931-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04931-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-022-04931-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yi-Kuei Lin & Cheng-Fu Huang, 2016. "Reliability evaluation according to a routing scheme for multi-state computer networks under assured accuracy rate," Annals of Operations Research, Springer, vol. 244(1), pages 221-240, September.
    2. Joseph C. Hudson & Kailash C. Kapur, 1985. "Reliability Bounds for Multistate Systems with Multistate Components," Operations Research, INFORMS, vol. 33(1), pages 153-160, February.
    3. Cheng-Fu Huang & Ding-Hsiang Huang & Yi-Kuei Lin, 2022. "System reliability analysis for a cloud-based network under edge server capacity and budget constraints," Annals of Operations Research, Springer, vol. 312(1), pages 217-234, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao, 2019. "Reliability of a stochastic intermodal logistics network under spoilage and time considerations," Annals of Operations Research, Springer, vol. 277(1), pages 95-118, June.
    2. Yeh, Wei-Chang & Bae, Changseok & Huang, Chia-Ling, 2015. "A new cut-based algorithm for the multi-state flow network reliability problem," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 1-7.
    3. Lin, Yi-Kuei, 2010. "Calculation of minimal capacity vectors through k minimal paths under budget and time constraints," European Journal of Operational Research, Elsevier, vol. 200(1), pages 160-169, January.
    4. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2011. "Maximal network reliability for a stochastic power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1332-1339.
    5. Kuo-Ching Chiou, 2023. "Building Up of Fuzzy Evaluation Model of Life Performance Based on Type-II Censored Data," Mathematics, MDPI, vol. 11(17), pages 1-12, August.
    6. Yi-Kuei Lin & Lance Fiondella & Ping-Chen Chang, 2022. "Reliability of time-constrained multi-state network susceptible to correlated component faults," Annals of Operations Research, Springer, vol. 311(1), pages 239-254, April.
    7. K Kolowrocki & J Soszynska, 2011. "On safety analysis of complex technical maritime transportation systems," Journal of Risk and Reliability, , vol. 225(3), pages 345-354, September.
    8. Xu, Bei & Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-an & Fang, Yining, 2022. "A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Lin, Yi-Kuei & Fiondella, Lance & Chang, Ping-Chen, 2013. "Quantifying the impact of correlated failures on system reliability by a simulation approach," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 32-40.
    10. Kołowrocki, K. & Kwiatuszewska-Sarnecka, B., 2008. "Reliability and risk analysis of large systems with ageing components," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1821-1829.
    11. Lin, Yi-Kuei, 2010. "Reliability evaluation of a revised stochastic flow network with uncertain minimum time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1253-1258.
    12. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2012. "Determining the optimal double-component assignment for a stochastic computer network," Omega, Elsevier, vol. 40(1), pages 120-130, January.
    13. Lin, Yi-Kuei & Huang, Ding-Hsiang, 2020. "Reliability analysis for a hybrid flow shop with due date consideration," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    14. Yi-Kuei Lin & Ping-Chen Chang, 2015. "A novel model for a manufacturing system with joint production lines in terms of prior-set," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(2), pages 340-354, January.
    15. Bita Tadayon & J. Cole Smith, 2014. "Algorithms for an Integer Multicommodity Network Flow Problem with Node Reliability Considerations," Journal of Optimization Theory and Applications, Springer, vol. 161(2), pages 506-532, May.
    16. W-C Yeh, 2005. "A novel method for the network reliability in terms of capacitated-minimum-paths without knowing minimum-paths in advance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1235-1240, October.
    17. Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao & Chih-Ching Yeh, 2017. "System reliability for a multistate intermodal logistics network with time windows," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1957-1969, April.
    18. Niu, Yi-Feng & Zhao, Xia & Xu, Xiu-Zhen & Zhang, Shi-Yun, 2023. "Reliability assessment of a stochastic-flow distribution network with carbon emission constraint," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    19. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2010. "Optimal carrier selection based on network reliability criterion for stochastic logistics networks," International Journal of Production Economics, Elsevier, vol. 128(2), pages 510-517, December.
    20. Yi-Kuei Lin & Hsien-Chang Chou & Ping-Chen Chang, 2017. "Reliability and sensitivity analysis for a banking company transmission system," Journal of Risk and Reliability, , vol. 231(2), pages 146-154, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-022-04931-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.