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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T223000Z
DTEND:20121115T000000Z
LOCATION:155-F
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Computational behavior of large-scale data driven applications are complex functions of their input, various configuration settings, and underlying system architecture. The resulting difficulty in predicting their behavior complicates optimization of their performance and scheduling them onto compute resources. Manually diagnosing performance problems and reconfiguring resource settings to improve performance is cumbersome and inefficient.  We thus need autonomic optimization techniques that observe the application, learn from the observations, and subsequently successfully predict its behavior across different systems and load scenarios.  This work presents a modular modeling approach for complex data-driven applications that uses statistical techniques to capture pertinent characteristics of input data, dynamic application behaviors and system properties to predict application behavior with minimum human intervention. The work demonstrates how to adaptively structure and configure the model based on the observed complexity of application behavior in different input and execution contexts.
SUMMARY:Autonomic Modeling of Data-Driven Application Behavior
PRIORITY:3
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END:VCALENDAR