mlsanityData to 7 Oct 2026

AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP

arXiv 2501.14745 ↗First posted 16 Dec 2024cs.DC cs.LGcited by 7

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Abstract (arXiv)

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack of accuracy and interpretability of traditional methods in node health status judgment, this paper proposes a health status judgment method based on XGBoost and combines the SHAP method to analyze the interpretability of the model. Through experiments, it is verified that XGBoost has superior performance in processing complex features and nonlinear data of edge computing nodes, especially in capturing the impact of key features (such as response time and power consumption) on node status. SHAP value analysis further reveals the global and local importance of features, so that the model not only has high precision discrimination ability but also can provide intuitive explanations, providing data support for system optimization. Research shows that the combination of AI technology and computer system optimization can not only realize the intelligent monitoring of the health status of edge computing nodes but also provide a scientific basis for dynamic optimization scheduling, resource management and anomaly detection. In the future, with the in-depth development of AI technology, model dynamics, cross-node collaborative optimization and multimodal data fusion will become the focus of research, providing important support for the intelligent evolution of edge computing systems.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

15 of 22 references.

References · 15

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  2. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
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3 more
  1. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  2. 2024Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Modelscited by 6
  3. 2024Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothingcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 1.

7 papers.

  1. 2025Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaborationcited by 9
  2. 2025Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attentioncited by 8
  3. 2025Context-Aware Rule Mining Using a Dynamic Transformer-Based Frameworkcited by 7
  4. 2025Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Miningcited by 6
  5. 2025A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarizationcited by 5
  6. 2025Improving Harmful Text Detection with Joint Retrieval and External Knowledgecited by 2
  7. 2026Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationshipscited by 0