Name
Title | ||
|---|---|---|
Bayesian Probabilistic Monitor: A New and Efficient Probabilistic Monitoring Approach Based on Bayesian Statistics |
Abstract | ||
|---|---|---|
Modern software systems deal with increasing dependability requirements which specify non-functional aspect of a system correct operation. Usually, probabilistic properties are used to formulate dependability requirements like performance, reliability, safety, and availability. Probabilistic monitoring techniques, as an important assurance measure, has drawn more and more interest. Despite currently several approaches has been proposed to monitor probabilistic properties, it still lacks of a general and efficient monitoring approach for monitoring probabilistic properties. This paper puts forward a novel probabilistic monitoring approach based on Bayesian statistics, called Bayesian Probabilistic Monitor (BaProMon). By calculating Bayesian Factor, the approach can check whether the runtime information can provide sufficient evidences to support the null or alternative hypothesis. We give the corresponding algorithms and validate them via simulated-based experiments. The experimental results show that BaProMon can effectively monitor QoS properties. The results also indicate that our approach is superior to other approaches. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/QSIC.2013.55 | QSIC |
Keywords | Field | DocType |
probabilistic monitoring technique,runtime information,runtime monitoring,bayesian statistics,bayes methods,quality of service,qos properties,bapromon,qos property,efficient monitoring approach,bayesian factor,web services,bayesian statistic,bayesian probabilistic monitor,alternative hypothesis,dependability requirement,probabilistic property,novel probabilistic monitoring approach,probability properties,efficient probabilistic monitoring,system monitoring,probabilistic monitoring,probabilistic logic,semantics | Data mining,Dependability,Computer science,Recursive Bayesian estimation,System monitoring,Bayesian programming,Graphical model,Probabilistic logic,Bayesian statistics,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1550-6002 | 5 | 0.42 |
References | Authors | |
15 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuelong Zhu | 1 | 41 | 8.39 |
| Meijun Xu | 2 | 5 | 0.42 |
| Pengcheng Zhang | 3 | 40 | 10.97 |
| Wenrui Li | 4 | 58 | 8.98 |
| Hareton Leung | 5 | 982 | 65.63 |