Exploring the optimal strategy for large-scale data movement in high-performance networks

Patrick Brown, Mengxia Zhu, Qishi Wu, Daqing Yun, Jason Zurawski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advanced networking technologies and services have been rapidly developed and deployed to facilitate bulk data transfer so as to support next-generation eScience applications. However, these technologies and services have not been fully utilized due to the knowledge lack of scientific domain experts. By leveraging the functionalities of an existing data movement advising utility, we propose a new Workflow-based Intelligent Network Data Movement Advisor (WINDMA) with end-to-end performance optimization. WINDMA provides a web interface and interacts with existing data/space management and discovery services such as Storage Resource Management, transport methods such as GridFTP and GlobusOnline, and network resource provisioning brokers such as ION and OSCARS. Efficacy of WINDMA has been demonstrated in several use cases based on its implementation and deployment in wide-area networks.

Original languageEnglish (US)
Title of host publication2012 IEEE 31st International Performance Computing and Communications Conference, IPCCC 2012
Pages181-182
Number of pages2
DOIs
StatePublished - Dec 1 2012
Externally publishedYes
Event2012 IEEE 31st International Performance Computing and Communications Conference, IPCCC 2012 - Austin, TX, United States
Duration: Dec 1 2012Dec 3 2012

Publication series

Name2012 IEEE 31st International Performance Computing and Communications Conference, IPCCC 2012

Other

Other2012 IEEE 31st International Performance Computing and Communications Conference, IPCCC 2012
CountryUnited States
CityAustin, TX
Period12/1/1212/3/12

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications

Keywords

  • High-performance networks
  • eScience applications
  • large data transfer

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