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

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.

Automated summary

This paper introduces a deep neural network (DNN) model for predicting the reliability of a multistate stochastic cloud/edge-based network (MCEN). By transforming MCEN information into a suitable format and using Bayesian optimization to determine relevant functions and hyperparameters, the model can learn the capability of MCEN with different data promptly. Illustrative and practical cases of Amazon Web Service demonstrate the availability and efficiency of the prediction model for MCEN reliability.