As microservices architectures become increasingly prevalent, they introduce significant operational challenges due to the complexities in service interactions and fault propagation. These architectures often conceal the origins of faults due to intricate inter-service communications, making fault localization both critical and challenging. Addressing these difficulties, this paper introduces a novel fault localization method that leverages synergies between domain prior knowledge, ensemble learning, and graph-based modeling. Our approach models microservices as a graph, with services as nodes and their interactions as edges, illuminating complex dependencies and enhancing the depth of data analysis. The method integrates expert knowledge with a unique blend of multi-class decision trees and strategy models derived from a knowledge base, enabling effective de-tection of diverse patterns and anomalies. Additionally, a meta-learner refines the outputs from base models using a weighted decision-making process, significantly improving the accuracy and robustness of fault detection. Compared to traditional models, including graph neural networks, our approach sub-stantially reduces model complexity and enhances adaptability to evolving service patterns. It demonstrates superior scalability and real-time processing capabilities, offering a robust solution to the challenges of fault localization in dynamic microservice environments.

Knowledge tracing models based on deep learning can achieve impressive predictive performance by leveraging attention mechanisms. However, there still exist two challenges in attentive knowledge tracing (AKT): First, the mechanism of classical models of AKT demonstrates relatively low attention when processing exercise sequences with shifting knowledge concepts (KC), making it difficult to capture the comprehensive state of knowledge across sequences. Second, classical models do not consider stochastic behaviors, which negatively affects models of AKT in terms of capturing anomalous knowledge states. This article proposes a model of AKT, called Enhancing Locality for Attentive Knowledge Tracing (ELAKT), that is a variant of the deep KT model. The proposed model leverages the encoder module of the transformer to aggregate knowledge embedding generated by both exercises and responses over all timesteps. In addition, it uses causal convolutions to aggregate and smooth the states of local knowledge. The ELAKT model uses the states of comprehensive KCs to introduce a prediction correction module to forecast the future responses of students to deal with noise caused by stochastic behaviors. The results of experiments demonstrated that the ELAKT model consistently outperforms state-of-the-art baseline KT models.