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DABS-Storm: A Data-Aware Approach for Elastic Stream Processing

Abstract : 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.
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Submitted on : Tuesday, December 11, 2018 - 3:41:14 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Tuesday, March 12, 2019 - 3:08:08 PM


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Roland Kotto-Kombi, Nicolas Lumineau, Philippe Lamarre, Nicoló Rivetti, Yann Busnel. DABS-Storm: A Data-Aware Approach for Elastic Stream Processing. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Springer Berlin / Heidelberg, 2019, 40, pp.58--93. ⟨10.1007/978-3-662-58664-8_3⟩. ⟨hal-01951682⟩



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