COSTREAM provides a learned cost model for Distributed Stream Processing Systems that can accurately predict the execution costs of a streaming query in an edge-cloud environment. The model can be used to find an initial placement of operators across …
ZERoTuNE introduces a novel cost model for parallel and distributed stream processing that can be used to effectively set initial parallelism degrees of streaming queries. Unlike existing models, which rely majorly on online learning statistics that …
This paper presents zero-shot cost models for parallel stream processing, enabling accurate cost predictions for parallel streaming queries without having observed any query deployment. The approach leverages data-efficient zero-shot learning …
Distributed Stream Processing (DSP) systems highly rely on parallelism mechanisms to deliver high performance in terms of latency and throughput. Yet the development of such parallel systems altogether comes with numerous challenges. In this paper, …
Recently, machine learning has successfully been applied to many database problems such as query optimization, physical design tuning, or cardinality estimation. However, the predominant paradigm to design such learned database components is …
This paper proposes a learned cost estimation model for Distributed Stream Processing Systems (DSPS) with an aim to provide accurate cost predictions of executing queries. A major premise of this work is that the proposed learned model can generalize …