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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
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    References listed on IDEAS

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    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. Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-An & Fang, Yining, 2021. "An improved bounding algorithm for approximating multistate network reliability based on state-space decomposition method," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. 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.
    4. Yeh, Cheng-Ta & Fiondella, Lance, 2017. "Optimal redundancy allocation to maximize multi-state computer network reliability subject to correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 138-150.
    5. Yeh, Cheng-Ta, 2019. "An improved NSGA2 to solve a bi-objective optimization problem of multi-state electronic transaction network," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    6. 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.
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