Publications that have used WfCommons

1. Z. Zhang, Q-S. Hua, X. Zhang, H. Jin, X. Liao, DAG Scheduling with Communication Delays Based on Graph Convolutional Neural Network, Wireless Communications and Mobile Computing, 2022.
2. H. Casanova, Y.C. Wong, L. Pottier, R. Ferreira da Silva, On the Feasibility of Simulation-driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems, Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP), 2022.
3. L. Xing, M. Zhang, H. Li, M. Gong, J. Yang, K. Wang, Local search driven periodic scheduling for workflows with random task runtime in clouds, Computers & Industrial Engineering, 2022.
4. Z. Li, Y. Liu, L. Guo, Q. Chen, J. Cheng, W. Zheng, M. Guo, FaaSFlow: enable efficient workflow execution for function-as-a-service, 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2022.
5. S. Tuli, G. Casale, N. R. Jennings, MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems, IEEE Transactions on Parallel and Distributed Systems, 2021.
6. M. Orr, O. Sinnen, Optimal task scheduling for partially heterogeneous systems, Parallel Computing, 2021.
7. A. Benoit, L. Perotin, Y. Robert, H. Sun, Checkpointing Workflows à la Young/Daly Is Not Good Enough, , 2021.
8. E. Saeedizade, M. Ashtiani, DDBWS: a dynamic deadline and budget-aware workflow scheduling algorithm in workflow-as-a-service environments, The Journal of Supercomputing, 2021.
9. T. Coleman, H. Casanova, T. Gwartney, R. Ferreira da Silva, Evaluating Energy-Aware Scheduling Algorithms for I/O-Intensive Scientific Workflows, International Conference on Computational Science (ICCS), 2021.
10. W. Koch, An Approach for Automating the Calibration of Simulations of Parallel and Distributed Computing Systems, PhD Thesis, 2021.