DABS-Storm: A Data-Aware Approach for Elastic Stream Processing
| Autor: | Nicolo Rivetti, Yann Busnel, Nicolas Lumineau, Roland Kotto-Kombi, Philippe Lamarre |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
Computer science Data stream mining Real-time computing System stability Storm 02 engineering and technology Load balancing (computing) Computational resource Stream processing Data aware 020204 information systems 0202 electrical engineering electronic engineering information engineering Scaling |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783662586631 |
| DOI: | 10.1007/978-3-662-58664-8_3 |
| Popis: | In the last decade, stream processing has become a very active research domain motivated by the growing number of stream-based applications. These applications make use of continuous queries, which are processed by a stream processing engine (SPE) to generate timely results given the ephemeral input data. Variations of input data streams, in terms of both volume and distribution of values, have a large impact on computational resource requirements. Dynamic and Automatic Balanced Scaling for Storm (DABS-Storm) is an original solution for handling dynamic adaptation of continuous queries processing according to evolution of input stream properties, while controlling the system stability. Both fluctuations in data volume and distribution of values within data streams are handled by DABS-Storm to adjust the resources usage that best meets processing needs. To achieve this goal, the DABS-Storm holistic approach combines a proactive auto-parallelization algorithm with a latency-aware load balancing strategy. |
| Databáze: | OpenAIRE |
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