  Massive upgrades to science infrastructure are driving data velocities
upwards while stimulating adoption of increasingly data-intensive analytics.
While next-generation exascale supercomputers promise strong support for
I/O-intensive workflows, HPC remains largely untapped by live experiments,
because data transfers and disparate batch-queueing policies are prohibitive
when faced with scarce instrument time. To bridge this divide, we introduce
Balsam: a distributed orchestration platform enabling workflows at the edge to
securely and efficiently trigger analytics tasks across a user-managed
federation of HPC execution sites. We describe the architecture of the Balsam
service, which provides a workflow management API, and distributed sites that
provision resources and schedule scalable, fault-tolerant execution. We
demonstrate Balsam in efficiently scaling real-time analytics from two DOE
light sources simultaneously onto three supercomputers (Theta, Summit, and
Cori), while maintaining low overheads for on-demand computing, and providing a
Python library for seamless integration with existing ecosystems of data
analysis tools.

Distributed, Parallel, and Cluster Computing