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From "John Weachock (JIRA)" <>
Subject [jira] [Commented] (AIRAVATA-1636) [GSoC] Benchmark framework to facilitate Airavata Scheduling
Date Sun, 22 Mar 2015 17:18:11 GMT


John Weachock commented on AIRAVATA-1636:


I've got several questions about implementing a benchmark framework.

First, as Airavata is available for installation on private machines / clusters rather than
being centralized to one global installation, does it have a mechanism built in to record
data from all experiments that it has executed? For example, if an organization is using Airavata
to manage and run experiments, will any future benchmark framework be able to access all previous
records (including things like experiment type, input size, computation resource, and run
time) to prime its decision engine? Or do users have to manually record this data until the
benchmark framework is installed and able to record it?

Are all the computation resources running a uniform scheduler? Or is there some common API
to access their scheduling queue? If the benchmark framework predicts that resource A will
execute an experiment in 3 hours while resource B will compute it in 6 hours, but resource
A is allocated for the next 6 hours, it would be ideal to schedule the experiment for resource
B instead. I'm wondering if this can be used to help inform the decision. 

All execution on the resources is *not* performed through Airavata, correct? For example,
other users may choose to run their experiments through a terminal or other applications?
If all scheduling is done through Airavata, information relevant to the previous question
could be provided by the benchmark framework itself.

My first idea for such a framework is to provide a set of known parameters and historic execution
data to a machine learning algorithm or framework and provide extra information to the user
 as they schedule an experiment. If this has been discussed as a possibility already, is there
a link to the discussion?



> [GSoC] Benchmark framework to facilitate Airavata Scheduling
> ------------------------------------------------------------
>                 Key: AIRAVATA-1636
>                 URL:
>             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
> 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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