Name
Title | ||
|---|---|---|
Automatic Detecting Performance Bugs in Cloud Computing Systems via Learning Latency Specification Model |
Abstract | ||
|---|---|---|
Performance bugs that don't cause fail-stop errors but degradation of system performance have been one of the most fundamental issues in the production platform. How to effectively online detect bugs becomes more and more urgent for engineers. Performance bugs usually manifest themselves as the anomalous call structures of request traces or anomalous latencies of invoked methods. In this paper, we propose an automatic performance bug online detecting approach, CloudDoc. CloudDoc maintains a performance model mined from execution traces that are collected in the normal period. The performance model captures the characteristics of call structures of request traces together with corresponding latencies. With the performance model, CloudDoc periodically detects whether performance bugs occur or not. All suspicious call structures or latency-abnormal invoked methods are presented to engineers. We report two case studies to demonstrate the effectiveness of CloudDoc in helping engineers identify performance bugs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SOSE.2014.43 | SoSE |
Keywords | Field | DocType |
production platform,clouddoc,anomalous call structures,learning (artificial intelligence),anomalous latencies,program debugging,software maintenance,automatic performance bug online detecting approach,software fault tolerance,learning latency specification model,execution traces,cloud computing,system performance degradation,request traces,formal specification,cloud computing systems,servers,system performance,learning artificial intelligence,merging,computer bugs,computational modeling | Cloud computing systems,Computer science,Latency (engineering),Software bug,Server,Real-time computing,Performance model,Merge (version control),Cloud computing,Distributed computing | Conference |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibo Mi | 1 | 134 | 12.60 |
| Wang Huaimin | 2 | 1025 | 121.31 |
| Zhenbang Chen | 3 | 199 | 23.60 |
| Yangfan Zhou | 4 | 232 | 29.72 |