As distributed teams and complex workflows now span resources from telescopes and light sources to fast networks and smart Internet of Things sensor systems, a single, centralized administrative team and software stack cannot coordinate and manage all the resources. Instead, resources must begin to respond autonomically, repairing and tuning their behavior in response to scientific workflows.
This project explores the architecture, methods, and algorithms needed to support future scientific computing systems that self-tune and self-manage. Our aim is threefold:
- Design a scalable architecture for smart science ecosystems
- Embed intelligence in relevant subsystems via lightweight machine learning
- Explore methods for distributed and autonomous management of the systems.