Name
Abstract | ||
|---|---|---|
Canary testing is an emerging technique that offers to minimize the risk of deploying a new version of software. It does so by slowly transferring load from the current to the new (\"canary\") version. As this ramp-up progresses, a human compares the performance and correctness of the two versions, and assesses whether to abort the canary version. For canary testing to be effective, a plethora of metrics must be analyzed, including CPU utilization and logged errors, across hundreds to thousands of machines. Performing this analysis manually is both time consuming and error prone. In this paper, we present CanaryAdvisor, a tool for automatic canary testing of cloud-based applications. CanaryAdvisor continuously monitors the deployed versions of an application and detects degradations in correctness, performance, and/or scalability. We describe our design and implementation of the CanaryAdvisor and outline open challenges. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2771783.2784770 | International Symposium on Software Testing and Analysis |
Field | DocType | Citations |
Abort,Computer science,CPU time,Correctness,Real-time computing,Software,Operating system,Cloud computing,Scalability | Conference | 10 |
PageRank | References | Authors |
0.60 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Tarvo | 1 | 10 | 0.60 |
| Peter F. Sweeney | 2 | 742 | 69.82 |
| Nick Mitchell | 3 | 353 | 14.92 |
| V. T. Rajan | 4 | 58 | 2.52 |
| Matthew Arnold | 5 | 362 | 20.57 |
| Ioana Baldini | 6 | 17 | 1.37 |