Publication: ScoPredâScalable User-Directed Performance Prediction Using Complexity Modeling and Historical Data
All || By Area || By Year| Title | ScoPredâScalable User-Directed Performance Prediction Using Complexity Modeling and Historical Data | Authors/Editors* | Benjamin Lafreniere, Angela C. Sodan |
|---|---|
| Where published* | Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP), Cambridge |
| How published* | Proceedings |
| Year* | 2005 |
| Volume | 3834 |
| Number | -1 |
| Pages | 62-90 |
| Publisher | Springer |
| Keywords | scalability, performance prediction, historical data, prediction, linear regression |
| Link | |
| Abstract |
Using historical information to predict future runs of parallel jobs has shown to be valuable in job scheduling. Trends toward more flexible job-scheduling techniques such as adaptive resource allocation, and toward the expansion of scheduling to grids, make runtime predictions even more important. We present a technique of employing both a user |
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