Networking Research & Infrastructure
CICI: SSC: Integrity Introspection for Scientific Workflows (IRIS)
Description
The Integrity Introspection for Scientific Workflows (IRIS) project aims to automatically detect, diagnose, and pinpoint the source of unintentional integrity anomalies in scientific workflows on distributed cyberinfrastructure. The approach is to develop an appropriate threat model and incorporate it in an integrity introspection, correlation and analysis framework that collects application and infrastructure data and uses statistical and machine learning (ML) algorithms to perform the needed analysis. The framework is powered by novel ML-based methods developed through experimentation in a controlled testbed and validated in and made broadly available on NSF production CI. The solutions are being integrated into the Pegasus workflow management system, which is already used by a wide variety of scientific domains. An important part of the project is the engagement with selected science application partners in gravitational-wave physics, earthquake science, and bioinformatics to deploy the analysis framework for their workflows, and iteratively fine tune the threat models, the testbed, ML model training, and ML model validation in a feedback loop.
RENCI's Role
RENCI is leading the project and is responsible for development of ML models using testbed experimentation on ExoGENI and for development of a framework to analyze integrity-relevant data using offline and online ML-based approaches.