1 citations · 1 across the 9 of their papers we have counts for
9 papers
Prediction-Robust Service Deployment with Capacity-Aware Edge Admission
Hailiang Zhao, Ziqi Wang, Yifei Zhang +4
Edge platforms instantiate executable services close to users to reduce request-serving cost, but each instance incurs a one-time deployment cost and remains useful only for a fini…
Morphis: SLO-Aware Resource Scheduling for Microservices with Time-Varying Call Graphs
Yu Tang, Hailiang Zhao, Chuansheng Lu +4
Modern microservice systems exhibit continuous structural evolution in their runtime call graphs due to workload fluctuations, fault responses, and deployment activities. Despite t…
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…
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
Boyu Zhang, Ping He, Tianyu Du +4
With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern. While current waterm…
Toward Robust and Efficient ML-Based GPU Caching for Modern Inference
Peng Chen, Jiaji Zhang, Hailiang Zhao +11
In modern GPU inference, cache efficiency remains a major bottleneck, and heuristic policies such as \textsc{LRU} can perform far worse than the offline optimum. Existing learning-…
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…
SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models
Jiaji Zhang, Ruichao Sun, Hailiang Zhao +7
Diffusion models have demonstrated exceptional generative capabilities but are computationally intensive, posing significant challenges for deployment in resource-constrained or la…
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…
SecCoder: Towards Generalizable and Robust Secure Code Generation
Boyu Zhang, Tianyu Du, Junkai Tong +5
After large models (LMs) have gained widespread acceptance in code-related tasks, their superior generative capacity has greatly promoted the application of the code LM. Neverthele…