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Experiment-in-the-Loop Computing (EILC) requires support for numerous typesof processing and the management of heterogeneous infrastructure over a dynamicrange of scales: from the edge to the cloud and HPC, and intermediateresources. Serverless is an emerging service that combines high-levelmiddleware services, such as distributed execution engines for managing tasks,with low-level infrastructure. It offers the potential of usability andscalability, but adds to the complexity of managing heterogeneous and dynamicresources. In response, we extend Pilot-Streaming to support serverlessplatforms. Pilot-Streaming provides a unified abstraction for resourcemanagement for HPC, cloud, and serverless, and allocates resource containersindependent of the application workload removing the need to writeresource-specific code. Understanding of the performance and scalingcharacteristics of streaming applications and infrastructure presents anotherchallenge for EILC. StreamInsight provides insight into the performance ofstreaming applications and infrastructure, their selection, configuration andscaling behavior. Underlying StreamInsight is the universal scalability law,which permits the accurate quantification of scalability properties ofstreaming applications. Using experiments on HPC and AWS Lambda, we demonstratethat StreamInsight provides an accurate model for a variety of applicationcharacteristics, e.g., machine learning model sizes and resourceconfigurations.

Performance Characterization and Modeling of Serverless and HPC Streaming Applications