  High-performance computing (HPC) systems frequently experience congestion
leading to significant application performance variation. However, the impact
of congestion on application runtime differs from application to application
depending on their network characteristics (such as bandwidth and latency
requirements). We leverage this insight to develop Netscope, an automated
ML-driven framework that considers those network characteristics to dynamically
mitigate congestion. We evaluate Netscope on four Cray Aries systems, including
a production supercomputer on real scientific applications. Netscope has a
lower training cost and accurately estimates the impact of congestion on
application runtime with a correlation between 0.7and 0.9 for common scientific
applications. Moreover, we find that Netscope reduces tail runtime variability
by up to 14.9 times while improving median system utility by 12%.
