Name
Abstract | ||
|---|---|---|
In the past, data management research has concentrated in separate data processing issues: heavy database like query processing, and throughput of stream data processing over high-rate data CEP. However, in many practical contexts, high-rate stream and heavy data processing work together, for correlation, lookup, aggregation, merging or comparison with large amounts of previous data. We refer to these as stream-DB workloads. One way to provide scalability with any off-the-shelf engine is to have multiple machines and/or processor cores, and to parallelise the load external scheduler, but nodes can still overload. We propose automated control for balancing and scalability over stream-DB workloads. The approach, called DynLW, offers scalability with an integrated mechanism that manages overload rescheduling, automated elasticity, shedding, admission control and overload alerts when resources are insufficient. As a result, the approach provides continuous and totally balanced operation, and avoids overload-related problems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1504/IJBIDM.2014.062879 | International Journal of Business Intelligence and Data Mining |
Field | DocType | Volume |
Data processing,Admission control,Computer science,Load balancing (computing),Complex event processing,Throughput,Data management,Multi-core processor,Scalability,Distributed computing | Journal | 9 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Martins | 1 | 85 | 24.21 |
| Pedro Furtado | 2 | 204 | 55.67 |