Name
Abstract | ||
|---|---|---|
As the complexity of large-scale enterprise applications increases, providing performance verification through staging becomes an important part of reducing business risks associated with violating sophisticated service-level agreement (SLA). Currently, performance verification during the staging process is accomplished through either an expensive, cumbersome manual approach or ad hoc automation. This paper describes an automation approach as part of the Elba project supporting monitoring and performance analysis of distributed multi-tiered applications that helps in bottleneck detection. We use machine-learning to determine service-level objectives (SLOs) satisfaction and locate bottlenecks in candidate deployment scenarios. We evaluate our tools with TPC-W, an on-line bookstore, and RUBiS, an on-line auction site. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11907466_13 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
bottleneck detection,n-tier application,decision tree,SLOs,Elba | Conference | 4269 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.57 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gueyoung Jung | 1 | 477 | 30.21 |
| Galen S. Swint | 2 | 40 | 5.20 |
| Jason Parekh | 3 | 27 | 2.59 |
| Calton Pu | 4 | 5377 | 877.83 |
| Akhil Sahai | 5 | 567 | 58.03 |