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