Name
Abstract | ||
|---|---|---|
The dominant model for computing with large-scale data in cloud environments has been founded on batch processing including the Map-Reduce model. Important use-cases such as monitoring and alerting in the cloud require instead the incremental and continual handling of new data. Thus recent systems such as Storm, Samza and S4 have adopted ideas from stream processing to the cloud environment. We describe a novel system, Curracurrong Cloud, that, for the first time, allows the computation and data origins to share a cloud-hosted cluster, offers a lightweight algebraic-style description of the processing pipeline, and supports automated placement of computation among compute resources. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDEW.2014.6818328 | Data Engineering Workshops |
Keywords | Field | DocType |
batch processing (computers),cloud computing,data handling,algebraic style description,automated placement,batch processing,cloud environments,cloud stream processing,curracurrong cloud,dominant model,map reduce model,pipeline processing | Computer science,Storm,Real-time computing,Batch processing,Stream processing,Database,Cloud testing,Computation,Distributed computing,Cloud computing,Single-chip Cloud Computer | Conference |
Citations | PageRank | References |
1 | 0.37 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vasvi Kakkad | 1 | 4 | 1.44 |
| Akon Dey | 2 | 41 | 4.20 |
| Alan Fekete | 3 | 7 | 2.15 |
| Bernhard Scholz | 4 | 104 | 10.59 |