Publications (3)0.64 Total impact
Conference Proceeding: Lessons learned from moving earth system grid data sets over a 20 Gbps wide-area network.Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, Chicago, Illinois, USA, June 21-25, 2010; 01/2010
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ABSTRACT: Many production Grid and e-Science infrastructures have begun to offer services to end-users during the past several years with an increasing number of scientific applications that require access to a wide variety of resources and services in multiple Grids. Therefore, the Grid Interoperation Now—Community Group of the Open Grid Forum—organizes and manages interoperation efforts among those production Grid infrastructures to reach the goal of a world-wide Grid vision on a technical level in the near future. This contribution highlights fundamental approaches of the group and discusses open standards in the context of production e-Science infrastructures. Copyright © 2009 John Wiley & Sons, Ltd.Concurrency and Computation Practice and Experience 01/2009; 21:961-990. · 0.64 Impact Factor
Article: ADAPTIVE TRANSFER ADJUSTMENT IN EFFICIENT BULK DATA TRANSFER MANAGEMENT FOR CLIMATE DATASET[show abstract] [hide abstract]
ABSTRACT: Many scientific applications and experiments, such as high energy and nuclear physics, astrophysics, climate observation and modeling, combustion, nano-scale material sciences, and computational biology, generate extreme volumes of data with a large number of files. These data sources are distributed among national and international data repositories, and are shared by large numbers of geographically distributed scientists. A large portion of the data is frequently accessed, and a large volume of data is moved from one place to another for analysis and storage. A challenging issue in such efforts is the limited network capacity for moving large datasets. A tool that addresses this challenge is the Bulk Data Mover (BDM), a data transfer management tool used in the Earth System Grid (ESG) community. It has been managing massive dataset transfers efficiently in the environment where the network bandwidth is limited. Adaptive transfer adjustment was studied to enhance the BDM to handle significant end-to-end performance changes in the dynamic network environments as well as to control the data transfers for the desired transfer performance. We describe the results from our hands-on data transfer management experience in the climate research community. We study a practical transfer estimation model and state our initial results from the adaptive transfer adjustment methodology.