| Automating Edge-to-cloud Workflows for Science: Traversing the Edge-to-cloud Continuum with Pegasus | 0 | 0.34 | 2022 |
| Accelerating Scientific Workflows on HPC Platforms with In Situ Processing | 0 | 0.34 | 2022 |
| Fair sharing of network resources among workflow ensembles | 0 | 0.34 | 2022 |
| Building the Research Innovation Workforce: Challenges and Recommendations from a Virtual Workshop to Advance the Research Computing Community | 0 | 0.34 | 2022 |
| Broadening Student Participation in Cyberinfrastructure Research and Development | 0 | 0.34 | 2022 |
| Anomaly Detection in Scientific Workflows using End-to-End Execution Gantt Charts and Convolutional Neural Networks | 0 | 0.34 | 2021 |
| A lightweight method for evaluating in situ workflow efficiency | 1 | 0.36 | 2021 |
| Pushing the Cloud Limits in Support of IceCube Science | 0 | 0.34 | 2021 |
| Assessing Resource Provisioning and Allocation of Ensembles of In Situ Workflows | 0 | 0.34 | 2021 |
| Modeling the Performance of Scientific Workflow Executions on HPC Platforms with Burst Buffers | 1 | 0.36 | 2020 |
| Workflow Submit Nodes as a Service on Leadership Class Systems | 0 | 0.34 | 2020 |
| WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development | 0 | 0.34 | 2020 |
| Identifying Execution Anomalies for Data Intensive Workflows Using Lightweight ML Techniques | 0 | 0.34 | 2020 |
| An On-Demand Weather Avoidance System for Small Aircraft Flight Path Routing. | 0 | 0.34 | 2020 |
| Toward a Dynamic Network-Centric Distributed Cloud Platform for Scientific Workflows: A Case Study for Adaptive Weather Sensing | 2 | 0.41 | 2019 |
| Custom Execution Environments with Containers in Pegasus-Enabled Scientific Workflows | 0 | 0.34 | 2019 |
| Integrity Protection for Scientific Workflow Data: Motivation and Initial Experiences | 0 | 0.34 | 2019 |
| Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows | 2 | 0.36 | 2019 |
| Applicability study of the PRIMAD model to LIGO gravitational wave search workflows. | 0 | 0.34 | 2019 |
| Measuring the impact of burst buffers on data-intensive scientific workflows | 4 | 0.41 | 2019 |
| FABRIC: A National-Scale Programmable Experimental Network Infrastructure. | 4 | 0.57 | 2019 |
| The role of machine learning in scientific workflows | 2 | 0.40 | 2019 |
| Cyberinfrastructure Center of Excellence Pilot: Connecting Large Facilities Cyberinfrastructure | 0 | 0.34 | 2019 |
| Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis. | 0 | 0.34 | 2018 |
| PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows. | 9 | 0.58 | 2017 |
| A characterization of workflow management systems for extreme-scale applications. | 22 | 0.82 | 2017 |
| Distributed workflows for modeling experimental data | 0 | 0.34 | 2017 |
| Workflow Performance Profiles: Development and Analysis. | 1 | 0.35 | 2016 |
| Asterism: Pegasus and Dispel4py Hybrid Workflows for Data-Intensive Science. | 0 | 0.34 | 2016 |
| Understanding User Behavior: From HPC to HTC. | 0 | 0.34 | 2016 |
| Pegasus in the Cloud: Science Automation through Workflow Technologies | 16 | 0.68 | 2016 |
| Performance Analysis of an I/O-Intensive Workflow Executing on Google Cloud and Amazon Web Services | 4 | 0.50 | 2016 |
| Challenges of Running Scientific Workflows in Cloud Environments | 0 | 0.34 | 2015 |
| Adapting Scientific Workflows on Networked Clouds Using Proactive Introspection | 4 | 0.44 | 2015 |
| HUBzero and Pegasus: integrating scientific workflows into science gateways. | 0 | 0.34 | 2015 |
| Pegasus, a workflow management system for science automation | 154 | 5.18 | 2015 |
| Scheduling multilevel deadline-constrained scientific workflows on clouds based on cost optimization | 23 | 0.78 | 2015 |
| Algorithms and Scheduling Techniques to Manage Resilience and Power Consumption in Distributed Systems (Dagstuhl Seminar 15281). | 0 | 0.34 | 2015 |
| A Semantic-Based Approach to Attain Reproducibility of Computational Environments in Scientific Workflows: A Case Study. | 2 | 0.38 | 2014 |
| Comparing FutureGrid, Amazon EC2, and Open Science Grid for Scientific Workflows | 1 | 0.35 | 2013 |
| Imbalance optimization in scientific workflows | 3 | 0.41 | 2013 |
| Introducing PRECIP: An API for Managing Repeatable Experiments in the Cloud | 13 | 0.80 | 2013 |
| Creating A Galactic Plane Atlas With Amazon Web Services. | 0 | 0.34 | 2013 |
| Energy-Constrained Provisioning for Scientific Workflow Ensembles. | 13 | 0.60 | 2013 |
| Cost Optimization Of Execution Of Multi-Level Deadline-Constrained Scientific Workflows On Clouds | 5 | 0.44 | 2013 |
| Peer-to-Peer Data Sharing for Scientific Workflows on Amazon EC2 | 8 | 0.48 | 2012 |
| Integrating Policy with Scientific Workflow Management for Data-Intensive Applications | 4 | 0.41 | 2012 |
| Enabling large-scale scientific workflows on petascale resources using MPI master/worker | 7 | 0.57 | 2012 |
| Integration of Workflow Partitioning and Resource Provisioning | 21 | 0.84 | 2012 |
| Failure prediction and localization in large scientific workflows. | 12 | 0.55 | 2011 |
