Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuxing Xiang
dblp:262/4802
· DBLP profile ↗
4ranked-venue papers
3as first author
4since 2021 · last 2026
0009-0001-1942-9902ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 first-author · 2 since 2021Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Cloud and datacenter computing · 45% Performance modeling and evaluation · 30% GPUs and heterogeneous computing · 26% | |
| Computer networks
2 papers |
Network management and operations · 60% Internet architecture and protocols · 40% | |
| Network and information security
1 paper |
Network security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Program verification · 100% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › protocol security
DNS security |
1.0 | 1 | 2026 | Iceberg: Automated Verification of DNS Authoritative Engines via Just-in-Time Summarization · NSDI 2026 |
Cloud and datacenter computing
inference serving |
1.0 | 1 | 2026 | ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production · NSDI 2026 |
Performance modeling and evaluation
workload characterization |
1.0 | 1 | 2026 | ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production · NSDI 2026 |
GPUs and heterogeneous computing › GPU sharing
GPU pooling |
0.9 | 1 | 2025 | Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market · SOSP 2025 |
Internet architecture and protocols
domain name system |
0.7 | 1 | 2023 | Automated Verification of an In-Production DNS Authoritative Engine · SOSP 2023 |
Program verification
automated verification |
0.7 | 1 | 2023 | Automated Verification of an In-Production DNS Authoritative Engine · SOSP 2023 |
Cloud and datacenter computing
cluster resource management and scheduling |
0.3 | 1 | 2025 | Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market · SOSP 2025 |
Cloud and datacenter computing › resource allocation › hardware resource assignment
GPU resource allocation |
0.3 | 1 | 2025 | Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market · SOSP 2025 |
Methods — techniques the papers use, named apart from their topics
just-in-time summarization · 2.0automated verification · 2.0symbolic execution · 1.3abstraction · 1.3workload generation · 1.0GPU pooling · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Iceberg: Automated Verification of DNS Authoritative Engines via Just-in-Time Summarization
Yuxing Xiang, Rilin Huang, Naiqian Zheng, Xin Jin 0008 |
NSDI | 1 |
| 2026 | ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
Yuxing Xiang, Xue Li 0024, Kun Qian 0004, Yan Zhang 0117, Wenyuan Yu, Ennan Zhai, Xin Jin 0008, Jingren Zhou 0001 |
NSDI | 1 |
| 2025 | Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market
Yuxing Xiang, Xue Li 0024, Kun Qian 0021, Diwen Zhu, Wenyuan Yu, Ennan Zhai, Xuanzhe Liu, Xin Jin 0008, Jingren Zhou 0001 |
SOSP | 1 |
| 2023 | Automated Verification of an In-Production DNS Authoritative EngineabstractThis paper presents DNS-V, a verification framework for our in-production DNS authoritative engine, which is the core of our DNS service. The key idea for automated verification in general is based on the layered verification principle. However, we face the challenge that our in-production DNS authoritative engine lacks modularity, more specifically, as can be seen with unclean interfaces and poor data structure encapsulation. This makes the layered verification hard to apply. To address this challenge, we propose a summarization approach that performs full-path symbolic execution to accumulate all path conditions and computation effects, and then represents a module's behavior in an abstract form as a set of input-effect pairs. In addition, for portability to future iterated versions of our DNS authoritative engine, we identify common dependency library modules that remain stable across different versions, and carefully design their abstractions to make them amenable to automated reasoning. Our framework has been successful in identifying and preventing tens of critical bugs in different versions of our DNS authoritative engine from reaching production, with a porting effort of less than one person-week. Naiqian Zheng, Mengqi Liu 0001, Yuxing Xiang, Linjian Song, Nan Wang 0041, Zhuo Liang, Dennis Cai, Ennan Zhai, Xuanzhe Liu, Xin Jin 0008 |
SOSP | 3 |