Name
Abstract | ||
|---|---|---|
Automated detection and diagnosis of the performance faults in cloud and datacenter environments is a crucial task to maintain smooth operation of different services and minimize downtime. We demonstrate an effective machine learning approach based on detecting metric correlation stability violations (CSV) for automated localization of performance faults for datacenter services running under dynamic load conditions. |
Year | Venue | Field |
|---|---|---|
2017 | IM | Computer science,Dynamic load testing,Server,Computer network,Quality of service,Real-time computing,Systems architecture,Downtime,Cloud computing,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jawwad Ahmed | 1 | 85 | 7.97 |
| Andreas Johnsson | 2 | 46 | 10.68 |
| Farnaz Moradi | 3 | 33 | 6.22 |
| Rafael Pasquini | 4 | 43 | 12.82 |
| Christofer Flinta | 5 | 39 | 8.71 |
| Rolf Stadler | 6 | 706 | 70.88 |