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Toward an End-to-End Framework for Modeling, Monitoring and Anomaly Detection for Scientific Workflows.

, , , , , , , , , , , , , , , , and . IPDPS Workshops, page 1370-1379. IEEE Computer Society, (2016)

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Toward Prioritization of Data Flows for Scientific Workflows Using Virtual Software Defined Exchanges., , , , and . eScience, page 566-575. IEEE Computer Society, (2017)Fair sharing of network resources among workflow ensembles., , , , , , , , , and . Clust. Comput., 25 (4): 2873-2891 (2022)A Performance Characterization of Scientific Machine Learning Workflows., , , , , , , , , and 1 other author(s). WORKS, page 58-65. IEEE, (2021)Domain Science Applications on GENI: Presentation and Demo., and . ICNP, page 540-543. IEEE Computer Society, (2014)VGrADS: enabling e-Science workflows on grids and clouds with fault tolerance., , , , , , , , , and 2 other author(s). SC, ACM, (2009)Towards an Experimental LegoLand: Slice Modification and Recovery in ExoGENI Testbed., , , , and . TRIDENTCOM, volume 177 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 35-45. Springer, (2016)Enabling workflow repeatability with virtualization support., , , , , and . WORKS@SC, page 8:1-8:10. ACM, (2015)Capacity of inter-cloud layer-2 virtual networking., , , , and . DCC@SIGCOMM, page 31-36. ACM, (2014)SMART-HexBot: a Simulation, Modeling, Analysis and Research Tool for Hexapod Robot in Virtual Reality and Simulink., , and . AIR, page 75:1-75:7. ACM, (2013)The role of machine learning in scientific workflows., , , and . Int. J. High Perform. Comput. Appl., (2019)