Yue Cheng

About

Yue Cheng is an Assistant Professor at the University of Virginia, holding a dual appointment in the School of Data Science and the Department of Computer Science. Prior to joining UVA in 2022, he served as an Assistant Professor of Computer Science at George Mason University. His research interests include distributed systems, cloud and serverless computing, high-performance computing, and operating systems. Cheng's work is driven by the complexities of modern data-intensive computer systems and aims to develop more efficient and user-friendly approaches to manage these complexities. His current research focuses on designing efficient data systems for data science, including the development of efficient stateful serverless computing systems through a full-stack approach that spans applications, platforms, and hardware, as well as building improved computing and storage systems for distributed machine learning.

Research topics

• Computer Science
• Computer network
• Distributed computing
• Artificial Intelligence
• Machine Learning
• Computer Security
• Embedded system

Selected publications

• A Closer Look into IPFS: Accessibility, Content, and Performance

  Proceedings of the ACM on Measurement and Analysis of Computing Systems · 2024-05-21 · 26 citations

  articleOpen access

  The InterPlanetary File System (IPFS) has recently gained considerable attention. While prior research has focused on understanding its performance characterization and application support, it remains unclear: (1) what kind of files/content are stored in IPFS, (2) who are providing these files, (3) are these files always accessible, and (4) what affects the file access performance. To answer these questions, in this paper, we perform measurement and analysis on over 4 million files associated wi…

  PublisherOA PDFDOI

• Centralization in the Decentralized Web: Challenges and Opportunities in IPFS Data Management

  2025-04-22 · 6 citations

  articleOpen access

  The InterPlanetary File System (IPFS) is a pioneering effort for Web 3.0, well-known for its decentralized infrastructure. However, some recent studies have shown that IPFS exhibits a high degree of centralization and has integrated centralized components for improved performance. While this change contradicts the core decentralized ethos of IPFS and introduces risks of hurting the data replication level and thus availability, it also opens some opportunities for better data management and cost…

  PublisherOA PDFDOI

• Concurrency-Informed Orchestration for Serverless Functions

  2025-03-27 · 2 citations

  articleOpen access

  Cold start delays are a main pain point for today's FaaS (Function-as-a-Service) platforms. A widely used mitigation strategy is keeping recently invoked function containers alive in memory to enable warm starts with minimal overhead. This paper identifies new challenges that state-of-the-art FaaS keep-alive policies neglect. These challenges are caused by concurrent function invocations, a common FaaS workload behavior. First, concurrent requests present a tradeoff between reusing busy containe…

  PublisherDOI

• Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction

  Society for Industrial and Applied Mathematics eBooks · 2025-01-01 · 1 citations

  book-chapter

  Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computing becomes a promising solution by leveraging abundant computing resources (e.g., GPU). However, the node dependency of graph data increases the difficulty of achieving high concurrency in distributed GNN training, which suffers from the massive communication overhead. To address it, Historical value approximation is de…

  PublisherDOI

• Everything You Always Wanted to Know About Storage Compressibility of Pre-Trained ML Models but Were Afraid to Ask

  Proceedings of the VLDB Endowment · 2024-04-01 · 1 citations

  articleSenior author

  As the number of pre-trained machine learning (ML) models is growing exponentially, data reduction tools are not catching up. Existing data reduction techniques are not specifically designed for pre-trained model (PTM) dataset files. This is largely due to a lack of understanding of the patterns and characteristics of these datasets, especially those relevant to data reduction and compressibility. This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibili…

  PublisherDOI

Recent grants

• SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications

  NSF · $121k · 2022–2024

• CAREER: Harnessing Serverless Functions to Build Highly Elastic Cloud Storage Infrastructure

  NSF · $349k · 2021–2023

• SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications

  NSF · $321k · 2019–2023

Frequent coauthors

• Ali Anwar

  37 shared

• Ali R. Butt

  Virginia Tech

  32 shared

• Gaoyan Zhang

  Tianjin University

  16 shared

• Lixiang Huang

  Fujian Women and Children Hospital

  16 shared

• Xiaodong Zhang

  University of Electronic Science and Technology of China

  16 shared

• Jia-Min Zhou

  Tianjin Medical University

  16 shared

• Shen Wen

  Tianjin First Center Hospital

  16 shared

• Yuexuan Li

  University of Minnesota Medical Center

  16 shared

Similar researchers at University of Virginia

• Kevin Skadronh-63
• David Evansh-61
• Qizhe Caih-57