期刊
JOURNAL OF SUPERCOMPUTING
卷 59, 期 3, 页码 1431-1454出版社
SPRINGER
DOI: 10.1007/s11227-011-0555-y
关键词
Distributed architectures; Load balancing and task assignment; Scheduling and task partitioning; User interfaces
类别
资金
- Department of Computer and Information Sciences at the University of Alabama at Birmingham
- CIS at UAB
- NSF [CNS-0420614]
- NIH/NIAID [HHSN266200400036C-Viral Bioinformatics Resource Center]
Growth in availability of data collection devices has allowed individual researchers to gain access to large quantities of data that needs to be analyzed. As a result, many labs and departments have acquired considerable compute resources. However, effective and efficient utilization of those resources remains a barrier for the individual researchers because the distributed computing environments are difficult to understand and control. We introduce a methodology and a tool that automatically manipulates and understands job submission parameters to realize a range of job execution alternatives across a distributed compute infrastructure. Generated alternatives are presented to a user at the time of job submission in the form of tradeoffs mapped onto two conflicting objectives, namely job cost and runtime. Such presentation of job execution alternatives allows a user to immediately and quantitatively observe viable options regarding their job execution, and thus allows the user to interact with the environment at a true service level. Generated job execution alternatives have been tested through simulation and on real-world resources and, in both cases, the average accuracy of the runtime of the generated and perceived job alternatives is within 5%.
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