MorphStream: Adaptive Scheduling for Scalable Transactional Stream Processing on Multicores

Transactional stream processing engines (TSPEs) differ significantly in their designs, but all rely on non- adaptive scheduling strategies for processing concurrent state transactions. Subsequently, none exploit multicore parallelism to its full potential due to complex workload dependencies. This p...

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Veröffentlicht in:Proceedings of the ACM on management of data 2023-05, Vol.1 (1), p.1-26, Article 59
Hauptverfasser: Mao, Yancan, Zhao, Jianjun, Zhang, Shuhao, Liu, Haikun, Markl, Volker
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Sprache:eng
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Zusammenfassung:Transactional stream processing engines (TSPEs) differ significantly in their designs, but all rely on non- adaptive scheduling strategies for processing concurrent state transactions. Subsequently, none exploit multicore parallelism to its full potential due to complex workload dependencies. This paper introduces MorphStream, which adopts a novel approach by decomposing scheduling strategies into three dimensions and then strives to make the right decision along each dimension, based on analyzing the decision trade-offs under varying workload characteristics. Compared to the state-of-the-art, MorphStream achieves up to 3.4 times higher throughput and 69.1% lower processing latency for handling real-world use cases with complex and dynamically changing workload dependencies.
ISSN:2836-6573
2836-6573
DOI:10.1145/3588913