Name
Abstract | ||
|---|---|---|
As computer systems become increasingly complex, system anomalies have become major concerns in system management. In this paper, we present a comprehensive measurement study to quantify the predictability of different system anomalies. Online anomaly prediction allows the system to foresee impending anomalies so as to take proper actions to mitigate anomaly impact. Our anomaly prediction approach combines feature value prediction with statistical classification methods. We conduct extensive measurement study to investigate anomalous behavior of three systems in the real world: PlanetLab, SMART hard drive data, and IBM System S. We observe that real world system anomalies do exhibit predictability, which can be predicted with high accuracy and significant lead time. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MASCOTS.2010.22 | MASCOTS |
Keywords | Field | DocType |
system anomalies,feature value prediction,ibm system s,system anomaly,real world system anomaly,planetlab,statistical analysis,pattern classification,online anomaly prediction,system management,computer system,smart hard drive data,impending anomaly,internet,systems analysis,different system anomaly,statistical classification method,real world,anomaly prediction approach,anomaly impact,measurement,complex system,accuracy,systems management,predictions,markov processes,mathematical model,bayesian methods,predictive models | Data mining,IBM,Predictability,PlanetLab,Markov process,Computer science,Systems analysis,Lead time,Artificial intelligence,Statistical classification,Systems management,Machine learning | Conference |
ISSN | ISBN | Citations |
1526-7539 | 978-1-4244-8181-1 | 16 |
PageRank | References | Authors |
0.82 | 30 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongmin Tan | 1 | 169 | 7.58 |
| Xiaohui Gu | 2 | 1975 | 103.57 |