PoSeiDon: Platform for Explainable Distributed Infrastructure

PoSeiDon aims to advance the knowledge of how simulation and machine learning (ML) methodologies can be harnessed and amplified to improve DOE’s computational and data science. PosEiDon will provide an integrated platform that helps facility operators and scientists improve the overall end-to-end science workflow by (1) predicting the performance of complex workflows; (2) detecting and classifying infrastructure and workflow anomalies and "explaining" the sources of these anomalies; and (3) suggesting performance optimizations.

RENCI is responsible for data collection from testbeds (FABRIC, Chameleon etc.) and research on anomaly detection and guided performance optimization.

• Argonne National Labs
• Lawrence Berkeley National Laboratory
• Oak Ridge National Laboratory
• University of Southern California

• U.S. Department of Energy