Name
Abstract | ||
|---|---|---|
Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforward. Performance models trained on historical executions of a concrete job are helpful in such situations, yet they are usually bound to a specific job execution context (e.g. node type, software versions, job parameters... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/Cluster48925.2021.00052 | 2021 IEEE International Conference on Cluster Computing (CLUSTER) |
Keywords | DocType | ISSN |
Analytical models,Runtime,Computational modeling,Clustering algorithms,Predictive models,Prediction algorithms,Data models | Conference | 1552-5244 |
ISBN | Citations | PageRank |
978-1-7281-9666-4 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominik Scheinert | 1 | 2 | 1.74 |
| Lauritz Thamsen | 2 | 43 | 9.26 |
| Houkun Zhu | 3 | 2 | 0.73 |
| Jonathan Will | 4 | 2 | 3.09 |
| Alexander Acker | 5 | 11 | 4.04 |
| Thorsten Wittkopp | 6 | 0 | 1.35 |
| Odej Kao | 7 | 1066 | 96.19 |