Adaptive Online Discrete Degradation State Distribution Prediction Methodology for Cloud Computing Optical Fiber Ports

With the deep penetration of optical fiber communication technology in cloud computing environments, optical fiber switches and other devices have been widely deployed. A gradual decline in power of optical fiber ports will lead to signal transmission attenuation and even complete communication failure at a critical degradation point. If the failure time of optical fiber ports can be predicted in advance, preventive maintenance can be performed. Therefore, accurate remaining useful life (RUL) prediction of optical fiber ports becomes valuable. Based on the unique discrete reciprocal hopping characteristics of optical fiber port power data, this article reports an adaptive online discrete degradation state distribution prediction methodology, which fills the gaps in modeling discrete degradation processes and RUL predictions. First, optical fiber port degradation process with state discretization is probabilistically modeled. Then, the intractable problem of solving a discrete degradation state first hitting time distribution is transformed to the problem of solving a cumulative degradation data distribution through the first hitting time theorem. Following this, analytical expressions for the discrete degradation state first hitting time distribution are, respectively, derived by the sum of multinomial distributions and central limit theorem. To realize RUL prediction of an online individual optical fiber port, a dynamic update mechanism is proposed to adaptively update distribution parameters by online individual optical fiber port degradation data. Finally, our proposed optical fiber RUL prediction methodology is applied to analyze industrial optical fiber port degradation data from Lenovo data centers. Industrial experiments show that our proposed methodology achieves a 95% prediction accuracy, which is significantly better than currently popular RUL prediction methods.