Serving ML prediction pipelines spanning multiple models and hardware accelerators is a
key challenge in production machine learning. Optimally configuring these pipelines to meet …

Container-based operation system (OS) level virtualization has been adopted by many edge-
computing platforms. However, for an edge server, inter-container communications, and …

This work introduces Mashup, a novel strategy to leverage serverless computing model for
executing scientific workflows in a hybrid fashion by taking advantage of both the traditional …

HPC applications are increasingly being designed as dynamic workflows for the ease of
development and scaling. This work demonstrates how the serverless computing model can …

Scientific workflow management systems (SWMSs) and resource managers together ensure
that tasks are scheduled on provisioned resources so that all dependencies are obeyed …

The dominant cost in production machine learning workloads is not training individual
models but serving predictions from increasingly complex prediction pipelines spanning …

Scientific workflows consist of thousands of highly parallelized tasks executed in a
distributed environment involving many components. Automatic tracing and investigation of …

High-throughput and data-intensive applications are increasingly present, often composed
as workflows, in the workloads of current HPC systems. At the same time, trends for future …

Next-generation data-intensive scientific workflows need to support streaming and real-time
applications with dynamic resource needs on high performance computing (HPC) platforms …

This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows
face in serverless environments-an issue that has been largely overlooked by prior works …