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https://issues.apache.org/jira/browse/AIRAVATA-1636?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14377309#comment-14377309
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John Weachock commented on AIRAVATA-1636:
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My idea is that as the user is filling out the scheduling form for an
experiment or job, the application will be able to asynchronously pass several
of the parameters to the decision engine and display feedback. For example, if
a user selects application X, the benchmark system should inform them *before
they submit* that application X generally performs the fastest when run on
resource Z. Additional information from each resource's scheduler, wait list,
and hardware availability can be combined with the decision engine's prediction
to enhance the information presented to the user. If a user does not care about
the execution time frame, it should be scheduled automatically using the best
prediction.
If this seems correct, I will start writing my proposal as soon as possible.
> [GSoC] Benchmark framework to facilitate Airavata Scheduling
> ------------------------------------------------------------
>
> Key: AIRAVATA-1636
> URL: https://issues.apache.org/jira/browse/AIRAVATA-1636
> Project: Airavata
> Issue Type: New Feature
> Reporter: Suresh Marru
> Labels: gsoc, gsoc2015, mentor
>
> Airavata assists science gateways to execute on multiple computational
> resources. To efficiently schedule applications on resources, Airavata needs
> to understand application performance. Applications are typically complex in
> terms of the models and algorithms they support and internally implemented
> optimization of resources available. The hardware provides additional
> variables in this optimization in terms of memory and computing units that
> can be allocated and time restrictions in the form of queue limits.
> Scheduling adds to this complexity by implementing policies toward enabling a
> particular Science domain and/or maximizing the usage of the resources
> itself.
> Airavata can feed data from historical executions and a framework can be
> built to systematically feed to new experiments (based on existing or totally
> newly devised models) executed. The run and timing data then can be codified
> such that the information can be presented to the user if an intelligent
> choice can be made by the user or can be used programmatically by Airavata in
> cases where the user does not or cannot provide such a choice.
> The end goal of this benchmark exercise will be to provide fastest execution
> time possible accounting for constraints available in the gateway to optimize
> its own allocations for all the users in the communities the gateway
> supports.
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