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From "Scott McCaulay (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (AIRAVATA-1084) [GSoC] Prototype Airavata Support for Application Scheduling using Ultrscan usecase
Date Thu, 20 Mar 2014 17:20:48 GMT

    [ https://issues.apache.org/jira/browse/AIRAVATA-1084?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13941990#comment-13941990
] 

Scott McCaulay commented on AIRAVATA-1084:
------------------------------------------

I am interested in working on this.  I think that having one (or a small number) of good use
cases like this could help visualize the more generally applicable solution that could be
incorporated into Airavata Orchestrator. 

Bringing together already available information from the resource side about availability
and queue wait predictions, and the kind of detailed application performance information described
here, it should be possible to build additional intelligence into the assignment of jobs to
resources.  

My understanding is that this project would be to extend the Orchestrator in such a way as
to support the Ultrascan project, with an intention of keeping that functionality general
enough to apply to additional projects.

> [GSoC] Prototype Airavata Support for Application Scheduling using Ultrscan usecase
> -----------------------------------------------------------------------------------
>
>                 Key: AIRAVATA-1084
>                 URL: https://issues.apache.org/jira/browse/AIRAVATA-1084
>             Project: Airavata
>          Issue Type: New Feature
>            Reporter: Suresh Marru
>              Labels: gsoc2014, mentor
>
> Before a general purpose application specific monitoring could be built into Airavata
Orchestrator, it will be good to prototype a one application specific monitoring and then
think about how to generalize it. 
> As an example, Ultrascan application team Borries Demeler and Gary Gorbet were willing
to provide a use case and access to information in their database to quert for the parameters
that influence calculation time, such as number of datapoints, grid resolution, cluster hardware,
noise fits, Monte Carlo iterations, etc. Based on these data, this prototype would do a multi-variate
performance analysis and create a prediction model for the length of time it would take for
a dataset to complete on a given resource. This will have to profile the 2 dimensional spectrum
analysis (2DSA) and possibly Genetic Algorithm. 



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