1.2k citations · 1.2k across the 26 of their papers we have counts for
27 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…
LipCache: A Local Inference Proxy with Certified Caching for Edge Image Classification Service
Zhengzhe Xiang, Yinlin Chen, Fuli Ying +3
As edge-side vision services continue to expand toward low-latency, high-throughput scenarios, reducing the inference cost of vision models without sacrificing reliability has beco…
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…
Frequency Matching in Spiking Neural Networks for mmWave Sensing
Di Yu, Zhenyu Liao, Changze Lv +7
Millimeter-wave (mmWave) sensing enables privacy-preserving, always-on edge perception, but its measurements are often sparse, temporally irregular, and corrupted by high-frequency…
Scene-Aware Latency Estimation for Microservices via Multi-Scale Graph Fusion
Zhichao Sun, Hailiang Zhao, Kingsum Chow
Cloud-Native microservice architectures have become prevalent owing to their inherent flexibility and scalability properties. To satisfy service quality guarantees, cloud providers…
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…
SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
Yin Wang, Zixuan Wang, Hao Lu +7
Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great…
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…
A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network
Feiyi Chen, Naijin Liu, Zhen Qin +3
Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional workload bursts, which makes workl…
Cloud-Native Computing: A Survey from the Perspective of Services
Shuiguang Deng, Hailiang Zhao, Binbin Huang +6
The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web a…
Scheduling Multi-Server Jobs with Sublinear Regrets via Online Learning
Hailiang Zhao, Shuiguang Deng, Zhengzhe Xiang +4
Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources t…
Learning to Schedule Multi-Server Jobs with Fluctuated Processing Speeds
Hailiang Zhao, Shuiguang Deng, Feiyi Chen +3
Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneousl…
Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs
Hailiang Zhao, Shuiguang Deng, Jianwei Yin +2
Multi-server jobs are imperative in modern computing clusters. A multi-server job has multiple task components and each of the task components is responsible for processing a speci…
Placement is not Enough: Embedding with Proactive Stream Mapping on the Heterogenous Edge
Hailiang Zhao, Shuiguang Deng, Zijie Liu +2
Edge computing is naturally suited to the applications generated by Internet of Things (IoT) nodes. The IoT applications generally take the form of directed acyclic graphs (DAGs),…
DPoS: Decentralized, Privacy-Preserving, and Low-Complexity Online Slicing for Multi-Tenant Networks
Hailiang Zhao, Shuiguang Deng, Zijie Liu +4
Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to…