Workflows, especially data-driven workflows and workflow ensembles are becoming a centerpiece of modern computational science. However, scientists lack the tools that integrate the operation of workflow-driven science applications on top of dynamic infrastructures that link campus, institutional and national resources into connected arrangements targeted at solving a specific problem. These tools must (a) orchestrate the infrastructure in response to application demands, (b) manage application lifetime on top of the infrastructure by monitoring various workflow steps and modifying slices in response to application demands, and (c) integrate data movement with the workflows to optimize performance.
Project ADAMANT (Adaptive Data-Aware Multi-domain Application Network Topologies) brings together researchers from RENCI/UNC Chapel Hill, Duke University and USC/ISI and two successful software tools to solve these problems: Pegasus workflow management system and ORCA resource control framework, developed for NSF GENI. The integration of Pegasus and ORCA enables powerful application- and data-driven virtual topology embedding into multiple institutional and national substrates (providers of cyber-resources, like computation, storage and networks). ADAMANT leverages ExoGENI – an NSF-funded GENI testbed, as well as national providers of on-demand bandwidth services (NLR, I2, ESnet) and existing OSG computational resources to create elastic, isolated environments to execute complex distributed tasks. This approach improves the performance of these applications and, by explicitly including data movement planning into the application workflow, enables new unique capabilities for distributed data-driven ‘Big Science’ applications.
- Adapting Scientific Workflows on Networked Clouds Using Proactive Introspection
- Enabling Workflow Repeatability with Virtualization Support
- Evaluating I/O Aware Network Management for Scientific Workflows on Networked Clouds
- Leveraging and Adapting ExoGENI Infrastructure for Data-Driven Domain Science Workflows
- Towards an Experimental LegoLand: Slice Modification and Recovery in ExoGENI Testbed
- WIRE: Resource-efficient Scaling with Online Prediction for DAG-based Workflows