Name
Abstract | ||
|---|---|---|
The time spent by an application can broadly be classified into two main categories - user mode and kernel mode. In order to optimize applications from a performance perspective, it is critical to know the code regions where they spend the bulk of their time. With datacenter applications becoming more I/O intensive and storage devices attaining higher performance with each generation, the contribution of the Linux kernel stack to overall performance is increasing to an all time high. These trends make it imperative to observe and visualize kernel behavior and performance in order to effectively optimize it for specific use cases. To that end, in this paper, we present KOVA: Kernel Overhead Visualization and Analysis framework that builds on top of existing kernel tracers to provide comprehensive insights into Linux kernel behavior. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/PCCC.2016.7820601 | 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC) |
Keywords | Field | DocType |
KOVA,kernel overhead visualization and analysis,user mode,kernel mode,data center application,Linux kernel stack | Kernel (linear algebra),epoll,sysfs,Use case,Visualization,Computer science,Hybrid kernel,Real-time computing,Kernel preemption,Linux kernel | Conference |
ISSN | ISBN | Citations |
1097-2641 | 978-1-5090-5253-0 | 1 |
PageRank | References | Authors |
0.38 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manu Awasthi | 1 | 215 | 9.57 |
| Krishna T. Malladi | 2 | 249 | 18.37 |