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DAPHNE is a new open-source software infrastructure designed to address theincreasing demands of integrated data analysis (IDA) pipelines, comprising datamanagement (DM), high performance computing (HPC), and machine learning (ML)systems. Efficiently executing IDA pipelines is challenging due to theirdiverse computing characteristics and demands. Therefore, IDA pipelinesexecuted with the DAPHNE infrastructure require an efficient and versatilescheduler to support these demands. This work introduces DaphneSched, thetask-based scheduler at the core of DAPHNE. DaphneSched is versatile byincorporating eleven task partitioning and three task assignment techniques,bringing the state-of-the-art closer to the state-of-the-practice taskscheduling. To showcase DaphneSched's effectiveness in scheduling IDApipelines, we evaluate its performance on two applications: a productrecommendation system and a linear regression model training. We conductperformance experiments on multicore platforms with 20 and 56 cores. Theresults show that the versatility of DaphneSched enabled combinations ofscheduling strategies that outperform commonly used scheduling techniques by upto 13%. This work confirms the benefits of employing DaphneSched for theefficient execution of applications with IDA pipelines.

DaphneSched: A Scheduler for Integrated Data Analysis Pipelines