Modeling and Optimizing Transport-Support Workflows in High-Performance Networks
High-performance networking technologies and services are being rapidly developed and deployed across the nation and around the globe to support the transfer of large data sets generated by next-generation scientific applications for collaborative data processing, analysis, and storage. However, these networking technologies and services have not been fully utilized mainly because their use often requires considerable domain knowledge and many application users are even not aware of their existence. The main goal of our work is to provide end users an integrated solution to discovering system and network resources and composing end-to-end paths for large data transfer. By leveraging the resource discovery capability previously developed in Network-Aware Data Movement Advisor (NADMA), we propose novel profiling and modeling approaches to characterize various types of resources that are available in end systems, edge segments, and backbone networks, taking into consideration a comprehensive set of performance metrics and network parameters in different phases including device deployment, circuit setup, and data transfer. Based on these profiles and models, we formulate a class of transport-support workflow optimization problems where an appropriate set of technologies and services are selected to compose the best transport-support workflow to meet the user's data transfer request in terms of various performance requirements. We conduct wide-area network experiments to validate the cost models and illustrate the efficacy of the proposed workflow-based transport solution.
MSU Digital Commons Citation
Yun, Daqing; Wu, Qishi; Brown, Patrick; and Zhu, Michelle, "Modeling and Optimizing Transport-Support Workflows in High-Performance Networks" (2012). Department of Computer Science Faculty Scholarship and Creative Works. 409.