Name
Abstract | ||
|---|---|---|
Understanding and predicting disk failures are essential for both disk vendors and users to manufacture more reliable disk drives and build more reliable storage systems, in order to avoid service downtime and possible data loss. Predicting disk failure from observable disk attributes, such as those provided by the Self-Monitoring and Reporting Technology (SMART) system, has been shown to be effective. In the paper, we treat SMART data as time series, and explore the prediction power by using HMM- and HSMM-based approaches. Our experimental results show that our prediction models outperform other models that do not capture the temporal relationship among attribute values over time. Using the best single attribute, our approach can achieve a detection rate of 46% at 0% false alarm. Combining the two best attributes, our approach can achieve a detection rate of 52% at 0% false alarm. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14400-4_30 | ICDM |
Keywords | Field | DocType |
observable disk attribute,attribute value,hsmm-based approach,smart data,detection rate,false alarm,disk vendor,reliable disk drive,disk failure,predicting disk failure,hidden semi markov model,prediction model,storage system,hidden markov model,time series | Data mining,False alarm,Observable,Data loss,Computer science,Artificial intelligence,Predictive modelling,Hidden Markov model,Smart data,Downtime,Machine learning,Hidden semi-Markov model | Conference |
Volume | ISSN | ISBN |
6171 | 0302-9743 | 3-642-14399-7 |
Citations | PageRank | References |
16 | 0.91 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Zhao | 1 | 902 | 49.19 |
| Xiang Liu | 2 | 85 | 10.11 |
| Siqing Gan | 3 | 121 | 15.75 |
| Weimin Zheng | 4 | 1889 | 182.48 |