5 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…
Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs
Xin Hu, Yue Kang, Guanzi Yao +3
This study addresses the generalization limitations commonly observed in large language models under multi-task and cross-domain settings. Unlike prior methods such as SPoT, which…
Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks
Yuxi Wang, Heyao Liu, Guanzi Yao +2
This paper proposes a topology-aware graph reinforcement learning approach to address the routing policy optimization problem in cloud server environments. The method builds a unif…
Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling
Yuxi Wang, Heyao Liu, Nyutian Long +1
This paper proposes an anomaly detection method based on federated learning to address key challenges in multi-tenant cloud environments, including data privacy leakage, heterogene…
Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters
Guanzi Yao, Heyao Liu, Linyan Dai
This paper addresses the challenges of high resource dynamism and scheduling complexity in cloud-native database systems. It proposes an adaptive resource orchestration method base…