9 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…
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…
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-…
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…
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…