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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
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DTSTART:20101118T200600Z
DTEND:20101118T202400Z
LOCATION:399
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Scientific experiments carried out in collaboration by researchers results in replication of large amounts of data globally. Unlike traditional approaches, where one best data source would be selected for retrieving data, we could leverage the presence of these distributed data sources when scheduling application workflows in distributed systems. Moreover, when the scheduling system has cost and time constraints, the problem of selection of data sources, distributed compute resources, tasks and data files to minimize total cost and time, are challenging.=0A=0AI propose probe-based multi-source parallel-data-retrieval technique to produce online and offline-schedules to minimize both time and cost of execution of application workflows when using distributed resources.  I have implemented a workflow management system that uses these heuristics to execute several real-world applications: image registration, intrusion detection, distributed evolutionary algorithms and ECG analysis. From live demonstrations at prestigious conferences, I have demonstrated the impact of my approach to the scientific community.
SUMMARY:Scheduling Data Intensive Applications Based on Multi-Source Parallel Data Retrievals
PRIORITY:3
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