Deep learning (DL) inference has become an essential building block in modern intelligent applications. Due to the high computational intensity of DL, it is critical to scale DL inference serving systems in response to fluctuating workloads to …
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 …