most citedReliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.SE2026

Agentic Services Computing

Shuiguang Deng, Hailiang Zhao, Ziqi Wang +6

Services computing has evolved from Web services and microservices to cloud-native and serverless paradigms. These approaches established mature principles for describing, composin…

cs.MA2026

When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling

Ziqi Wang, Yuhao Yang, Zhiwei Ling +2

Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benchmarks mostly evaluate task co…

cs.LG20261 cited

Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation

Wenzhuo Qian, Hailiang Zhao, Jiayi Chen +7

Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the maintenance of strict Service Level Objective…

cs.LG2026

ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality

Wenzhuo Qian, Hailiang Zhao, Ziqi Wang +4

Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimization, they unrealistically assume…

cs.LG2026

Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection

Zhiwei Ling, Hailiang Zhao, Chao Zhang +8

Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes while preserving data privacy, making it a cornerstone of intelligent serv…

cs.CV2025

CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging

Zhiwei Ling, Yachen Chang, Hailiang Zhao +3

Deep neural networks (DNNs) have been widely criticized for their overconfidence when dealing with out-of-distribution (OOD) samples, highlighting the critical need for effective O…