Research on End-To-End Reliability Modeling and Optimization of Service Grid

This study focuses on the challenges of end-to-end reliability modeling and optimization in service grids.In response to the shortcomings of traditional methods in adapting to heterogeneous link characteristics, data collection limitations, and dynamic environment adaptability, an innovative solution based on machine learning is proposed.The research background points out that latency and jitter, as key performance indicators, directly affect service quality, while traditional models have significant deficiencies in capturing nonlinear relationships, data collection costs, and dynamic optimization capabilities.The research method adopts machine learning techniques such as deep neural networks, random forests, and LSTM, combined with high-precision time synchronization (accuracy ≤ 300 nanoseconds) and large capacity data collection (single link data volume ≥ 50GB, lasting for 3 months), to construct a flexible and adjustable real dataset; Propose a heterogeneous neural network latency model, which independently models a single link through multiple models and introduces a weight learning mechanism to intelligently integrate the results of each link to adapt to heterogeneous characteristics; Introducing transfer learning to supplement target domain data, reducing annotation dependencies, and expanding jitter modeling application scenarios.Research has found that LSTM performs the best in modeling single link temporal sequences, but the entire path modeling requires heterogeneous neural networks to adapt to the differences in each link; When using transfer learning combined with feedforward neural networks and LSTM to model jitter, the R ² index exceeded 0.99, verifying the efficient utilization of data and the model's generalization ability; Heterogeneous neural network has better loss and fitting degree than single model in path delay prediction, which can effectively capture complex network behavior characteristics.Research contributions include the construction of high-quality datasets, new sample approximation methods based on path and link relationships, innovation in heterogeneous neural network models, and the application of transfer learning in supplementing lost traffic data.In the future, we will explore potential impact relationships between multiple links, consistency of transfer sample relationships, and better jitter modeling models to continuously optimize network performance and management support.