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

Ding-Hsiang Huang, Cheng-Fu Huang and Yi-Kuei Lin ()
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Ding-Hsiang Huang: Tunghai University
Cheng-Fu Huang: Feng Chia University
Yi-Kuei Lin: National Yang Ming Chiao Tung University

Annals of Operations Research, 2024, vol. 340, issue 1, No 13, 287 pages

Abstract: 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.

Keywords: MCEN reliability; Cloud computing; Edge computing; Deep neural network; Prediction model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04931-w

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