Name
Abstract | ||
|---|---|---|
Log-based Field Failure Data Analysis (FFDA) is a widely-adopted methodology to assess dependability properties of an operational system. A key step in FFDA is filtering out entries that are not useful and redundant error entries from the log. The latter is challenging: a fault, once triggered, can generate multiple errors that propagate within the system. Grouping the error entries related to the same fault manifestation is crucial to obtain realistic measurements. This paper deals with the issues of the tuple heuristic, used to group the error entries in the log, in multi-node computing systems. We demonstrate that the tuple heuristic can group entries incorrectly; thus, an improved heuristic that adopts statistical indicators is proposed. We assess the impact of inaccurate grouping on dependability measurements by comparing the results obtained with both the heuristics. The analysis encompasses the log of the Mercury cluster at the National Center for Supercomputing Applications. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/DSN.2011.5958210 | Dependable Systems & Networks |
Keywords | Field | DocType |
data analysis,statistical analysis,National Center for Supercomputing Application,data analysis,log-based field failure data,multinode computing system,statistical indicator,tuple heuristic issue,Field Failure Data Analysis,collision,dependability measurements,supercomputer,tuple heuristic | Data mining,Dependability,Heuristic,Supercomputer,Tuple,Computer science,Filter (signal processing),Operational system,Collision,Real-time computing,Heuristics,Distributed computing | Conference |
ISSN | ISBN | Citations |
1530-0889 E-ISBN : 978-1-4244-9231-2 | 978-1-4244-9231-2 | 28 |
PageRank | References | Authors |
1.27 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Pecchia | 1 | 143 | 18.60 |
| Domenico Cotroneo | 2 | 974 | 79.93 |
| Zbigniew Kalbarczyk | 3 | 1896 | 159.48 |
| Ravishankar K. Iyer | 4 | 3489 | 504.32 |