TY - GEN
T1 - Experiences of On-demand execution for large scale parameter sweep applications on OSG by swift
AU - Hou, Zhengxiong
AU - Wilde, Mike
AU - Hategan, Mihael
AU - Zhou, Xingshe
AU - Foster, Ian
AU - Clifford, Ben
PY - 2009
Y1 - 2009
N2 - Large scale parameter sweep application (PSA) is one of the main grid applications, which may have different characteristics and demands. In this paper, we describe how to use swift to enable the on-demand execution of large scale PSA on open science grid (OSG). The basic on-demand concept means providing appropriate grid resources for the application, which is decided by the characteristics and demands of the application. So we can get high reliability, efficiency, and scalability for large scale independent PSA jobs on OSG. The main on-demand policies include: trust based site selection and pre-selection; scheduling policy on-demand configuration; clustering for small jobs; adaptive execution and automatic data staging; divide and conquer for the scalability. Some usage examples of swift for executing large scale PSA are presented, such as dock, blast. The experimental results for the performance of different policies are presented, with a benchmarking workload size of 10,000 jobs.
AB - Large scale parameter sweep application (PSA) is one of the main grid applications, which may have different characteristics and demands. In this paper, we describe how to use swift to enable the on-demand execution of large scale PSA on open science grid (OSG). The basic on-demand concept means providing appropriate grid resources for the application, which is decided by the characteristics and demands of the application. So we can get high reliability, efficiency, and scalability for large scale independent PSA jobs on OSG. The main on-demand policies include: trust based site selection and pre-selection; scheduling policy on-demand configuration; clustering for small jobs; adaptive execution and automatic data staging; divide and conquer for the scalability. Some usage examples of swift for executing large scale PSA are presented, such as dock, blast. The experimental results for the performance of different policies are presented, with a benchmarking workload size of 10,000 jobs.
UR - http://www.scopus.com/inward/record.url?scp=70449591259&partnerID=8YFLogxK
U2 - 10.1109/HPCC.2009.43
DO - 10.1109/HPCC.2009.43
M3 - 会议稿件
AN - SCOPUS:70449591259
SN - 9780769537382
T3 - 2009 11th IEEE International Conference on High Performance Computing and Communications, HPCC 2009
SP - 527
EP - 532
BT - 2009 11th IEEE International Conference on High Performance Computing and Communications, HPCC 2009
T2 - 11th IEEE International Conference on High Performance Computing and Communications, HPCC 2009
Y2 - 25 June 2009 through 27 June 2009
ER -