Demo Data from EDBT 2026 / reviewers in the wild / expert
Cuong Pham 0003
dblp:20/6376-3
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author
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
1 paper |
Distributed systems · 61% Hardware reliability and fault tolerance · 30% Cloud and datacenter computing · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems
distributed management |
0.3 | 1 | 2017 | Failure Diagnosis for Distributed Systems Using Targeted Fault Injection · IEEE Trans. Parallel Distributed Syst. 2017 |
Distributed systems › fault tolerance
failure diagnosis |
0.3 | 1 | 2017 | Failure Diagnosis for Distributed Systems Using Targeted Fault Injection · IEEE Trans. Parallel Distributed Syst. 2017 |
Hardware reliability and fault tolerance
fault injection |
0.3 | 1 | 2017 | Failure Diagnosis for Distributed Systems Using Targeted Fault Injection · IEEE Trans. Parallel Distributed Syst. 2017 |
Cloud and datacenter computing › cloud service management
cloud infrastructure management |
0.1 | 1 | 2017 | Failure Diagnosis for Distributed Systems Using Targeted Fault Injection · IEEE Trans. Parallel Distributed Syst. 2017 |
Methods — techniques the papers use, named apart from their topics
flow similarity · 0.3execution flow reconstruction · 0.3data analytics · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Failure Diagnosis for Distributed Systems Using Targeted Fault InjectionabstractThis paper introduces a novel approach to automating failure diagnostics in distributed systems by combining fault injection and data analytics. We use fault injection to populate the database of failures for a target distributed system. When a failure is reported from production environment, the database is queried to find “matched” failures generated by fault injections. Relying on the assumption that similar faults generate similar failures, we use information from the matched failures as hints to locate the actual root cause of the reported failures. In order to implement this approach, we introduce techniques for (i) reconstructing end-to-end execution flows of distributed software components, (ii) computing the similarity of the reconstructed flows, and (iii) performing precise fault injection at pre-specified executing points in distributed systems. We have evaluated our approach using an OpenStack cloud platform, a popular cloud infrastructure management system. Our experimental results showed that this approach is effective in determining the root causes, e.g., fault types and affected components, for 71-100 percent of tested failures. Furthermore, it can provide fault locations close to actual ones and can easily be used to find and fix actual root causes. We have also validated this technique by localizing real bugs that occurred in OpenStack. Cuong Pham 0003, Long Wang 0003, Byung-Chul Tak, Salman Baset, Chunqiang Tang, Zbigniew T. Kalbarczyk, Ravishankar K. Iyer |
IEEE Trans. Parallel Distributed Syst. | 1 |