7 citations · 8 across the 19 of their papers we have counts for
22 papers
Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications
Hailiang Zhao, Xueyan Tang, Peng Chen +4
Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This survey synthesizes prediction interfaces, error measures, consistency--ro…
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…
Towards Optimal Robustness in Learning-Augmented Paging
Peng Chen, Hailiang Zhao, Xueyan Tang +2
Learning-augmented paging has been extensively studied in recent years. A key advantage over naive ML-based approaches is \emph{bounded robustness}, which guarantees worst-case per…
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…
TADS: Task-Aware Data Selection for Multi-Task Multimodal Pre-Training
Guanjie Cheng, Boyi Li, Lingyu Sun +4
Large-scale multimodal pre-trained models like CLIP rely heavily on high-quality training data, yet raw web-crawled datasets are often noisy, misaligned, and redundant, leading to…
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…
Shiva-DiT: Residual-Based Differentiable Top- Selection for Efficient Diffusion Transformers
Jiaji Zhang, Hailiang Zhao, Jiaju Wu +3
Diffusion Transformers (DiTs) are costly at high resolution because self-attention scales quadratically with token sequence length. Existing pruning methods do not jointly provide…
Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence
Hailiang Zhao, Ziqi Wang, Daojiang Hu +9
The convergence of artificial intelligence, cyber-physical systems, and distributed networking has accelerated the evolution of industrial intelligence across edge, cloud, and cros…
TrackTeller: Temporal Multimodal 3D Grounding for Behavior-Dependent Object References
Jiahong Yu, Ziqi Wang, Hailiang Zhao +3
Understanding natural-language references to objects in dynamic 3D driving scenes is essential for interactive autonomous systems. In practice, many referring expressions describe…
OmniFuser: Adaptive Multimodal Fusion for Service-Oriented Predictive Maintenance
Ziqi Wang, Hailiang Zhao, Yuhao Yang +7
Accurate and timely prediction of tool conditions is critical for intelligent manufacturing systems, where unplanned tool failures can lead to quality degradation and production do…
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…
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-…
Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting
Ziqi Wang, Hailiang Zhao, Cheng Bao +5
Rainfall is an important environmental driver of water-quality variations through processes such as runoff, pollutant transport, dilution, and resuspension. Traditional mechanistic…
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…
PeerSync: Accelerating Containerized Service Delivery at the Network Edge
Yinuo Deng, Hailiang Zhao, Dongjing Wang +5
Efficient container image distribution is crucial for enabling machine learning inference at the network edge, where resource limitations and dynamic network conditions create sign…
Robustifying Learning-Augmented Caching Efficiently without Compromising 1-Consistency
Peng Chen, Hailiang Zhao, Jiaji Zhang +3
The online caching problem aims to minimize cache misses when serving a sequence of requests under a limited cache size. While naive learning-augmented caching algorithms achieve i…
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…
Learning-Augmented Algorithms for the Bahncard Problem
Hailiang Zhao, Xueyan Tang, Peng Chen +1
In this paper, we study learning-augmented algorithms for the Bahncard problem. The Bahncard problem is a generalization of the ski-rental problem, where a traveler needs to irrevo…
Data-Locality-Aware Task Assignment and Scheduling for Distributed Job Executions
Hailiang Zhao, Xueyan Tang, Peng Chen +2
This paper addresses the data-locality-aware task assignment and scheduling problem for distributed job executions. Our goal is to minimize job completion times without prior knowl…
Decentralized Proactive Model Offloading and Resource Allocation for Split and Federated Learning
Binbin Huang, Hailiang Zhao, Lingbin Wang +3
In the resource-constrained IoT-edge computing environment, Split Federated (SplitFed) learning is implemented to enhance training efficiency. This method involves each terminal de…
Tail-Learning: Adaptive Learning Method for Mitigating Tail Latency in Autonomous Edge Systems
Cheng Zhang, Yinuo Deng, Hailiang Zhao +2
In the realm of edge computing, the increasing demand for high Quality of Service (QoS), particularly in dynamic multimedia streaming applications (e.g., Augmented Reality/Virtual…