4 papers
Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services
Yue Xing, Yingnan Deng, Heyao Liu +3
This paper addresses the challenges of complex dependencies and diverse anomaly patterns in cloud service environments by proposing a dependency modeling and anomaly detection meth…
Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models
Ming Gong, Yingnan Deng, Nia Qi +3
This paper addresses the issues of parameter redundancy, rigid structure, and limited task adaptability in the fine-tuning of large language models. It proposes an adapter-based fi…
Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery
Yun Zi, Ming Gong, Zhihao Xue +3
This study proposes an unsupervised anomaly detection method for distributed backend service systems, addressing practical challenges such as complex structural dependencies, diver…
Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
Zhihao Xue, Yun Zi, Nia Qi +2
This paper proposes a spatiotemporal graph neural network-based performance prediction algorithm to address the challenge of forecasting performance fluctuations in distributed bac…