Name
Abstract | ||
|---|---|---|
Data analytics are key applications running in the cloud computing environment. To improve performance and cost-effectiveness of a data analytics cluster in the cloud, the data analytics system should account for heterogeneity of the environment and workloads. In addition, it also needs to provide fairness among jobs when multiple jobs share the cluster. In this paper, we rethink resource allocation and job scheduling on a data analytics system in the cloud to embrace the heterogeneity of the underlying platforms and workloads. To that end, we suggest an architecture to allocate resources to a data analytics cluster in the cloud, and propose a metric of share in a heterogeneous cluster to realize a scheduling scheme that achieves high performance and fairness. |
Year | Venue | Keywords |
|---|---|---|
2011 | HotCloud | key application,scheduling scheme,job scheduling,multiple job,data analytics system,data analytics cluster,heterogeneity-aware resource allocation,data analytics,high performance,heterogeneous cluster,cloud computing environment |
Field | DocType | Citations |
Architecture,Data analysis,Scheduling (computing),Computer science,Real-time computing,Heterogeneous cluster,Resource allocation,Job scheduler,Cloud computing,Distributed computing | Conference | 93 |
PageRank | References | Authors |
3.22 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gunho Lee | 1 | 1917 | 84.03 |
| Byung-Gon Chun | 2 | 3832 | 234.37 |
| Randy H. Katz | 3 | 16819 | 3018.89 |