Name
Abstract | ||
|---|---|---|
In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on Reinforcement Learning are a good choice to improve the overall performance of grid applications and resource utilization. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISPA.2008.119 | ISPA |
Keywords | Field | DocType |
job orchestration,different resource scheduling strategy,rl-based scheduling strategies,actual grid environments,scheduling strategy,resource utilization,grid application,overall performance,reinforcement learning,grid environment,good choice,scheduling,grid computing,learning artificial intelligence,algorithm design and analysis,torque,dynamic scheduling,orchestration | Algorithm design,Grid computing,Fair-share scheduling,Scheduling (computing),Computer science,Real-time computing,Dynamic priority scheduling,Orchestration (computing),Grid,Distributed computing,Reinforcement learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernardo Costa | 1 | 0 | 0.68 |
| Inês Dutra | 2 | 61 | 10.35 |
| Marta Mattoso | 3 | 1287 | 109.83 |