Name
Title | ||
|---|---|---|
Workload characterization in a high-energy data grid and impact on resource management |
Abstract | ||
|---|---|---|
The analysis of data usage in a large set of real traces from a high-energy physics collaboration revealed the existence of an emergent grouping of files that we coined “filecules”. This paper presents the benefits of using this file grouping for prestaging data and compares it with previously proposed file grouping techniques along a range of performance metrics. Our experiments with real workloads demonstrate that filecule grouping is a reliable and useful abstraction for data management in science Grids; that preserving time locality for data prestaging is highly recommended; that job reordering with respect to data availability has significant impact on throughput; and finally, that a relatively short history of traces is a good predictor for filecule grouping. Our experimental results provide lessons for workload modeling and suggest design guidelines for data management in data-intensive resource-sharing environments. |
Year | DOI | Venue |
|---|---|---|
2009 | https://doi.org/10.1007/s10586-009-0081-3 | Cluster Computing |
Keywords | Field | DocType |
Grid computing,Workload characterization,Data management | Resource management,Data mining,Locality,Grid computing,Data analysis,Workload,Computer science,Data grid,Real-time computing,Throughput,Data management,Database | Journal |
Volume | Issue | ISSN |
12 | 2 | 1386-7857 |
Citations | PageRank | References |
10 | 0.61 | 41 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adriana Iamnitchi | 1 | 2547 | 222.35 |
| Shyamala Doraimani | 2 | 33 | 1.86 |
| Gabriele Garzoglio | 3 | 56 | 6.60 |