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https://issues.apache.org/jira/browse/AIRAVATA-357?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Suresh Marru updated AIRAVATA-357:
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Summary: [GSoC] Provide cloud bursting like capabilities to Airavata
computational workflows integrating with Apache Whirr (was: Provide cloud
bursting like capabilities to Airavata computational workflows integrating with
Apache Whirr)
> [GSoC] Provide cloud bursting like capabilities to Airavata computational
> workflows integrating with Apache Whirr
> -----------------------------------------------------------------------------------------------------------------
>
> Key: AIRAVATA-357
> URL: https://issues.apache.org/jira/browse/AIRAVATA-357
> Project: Airavata
> Issue Type: New Feature
> Components: GFac, Workflow Interpreter, XBaya
> Reporter: Suresh Marru
> Labels: gsoc2012, mentor
> Fix For: WISHLIST
>
>
> Apache Airavata provides capabilities to construct execute and monitor
> computational workflows with built in providers to execute applications on
> compute intensive resources. The users of Airavata constantly have the need
> to execute workflows which have tasks with a hybrid combination of
> computational grids and computational clouds. More over, some applications
> can be executed on multiple types of resources. The selection will depend on
> the distribution of data to be processed, the tolerable latency on shared
> batch queued grid clusters, and on the nature and size of the problem to be
> solved and data analysis tasks. Especially for applications which reduce the
> data as they proceed in the graph, they better fit for local map-reduce
> executions residing on hadoop based file systems.
> This idea has two implementation paths:
> Firstly extend airavata workflow providers to integrate with cloud based run
> times. Integrating with higher level API's like Apache Whirr provides a great
> value addition to this project to encompass multiple cloud services.
> Secondly, extending Airavata enactment called Workflow Interpreter so a
> component in the workflow is capable of initially running on local clusters
> deploying hadoop file systems and if the execution is saturating the local
> resources, the executions can scale out to commercial on-demand clouds or
> leverage high speed low latency interconnects on computational grids. This
> scaling technique is often referred to as Cloud Bursting. This project will
> not only enable this capability to Airavata workflow.
> User community & Impact of the software: Airavata is primarily targeted to
> build science gateways using computational resources from various
> disciplines. The initial targeted set of gateways include projects supporting
> research and education in chemistry, life sciences, biophysics, environmental
> sciences, geosciences astronomy and nuclear physics. The goal of airavata is
> to enhance productivity of these gateways to utilize cyberinfrastructure of
> resources (e.g., local lab resources, the Extreme Science and Engineering
> Discovery Environment (XSEDE), the Open Science Grid (OSG), University
> Clusters, Academic and Commercial Computational Clouds like FutureGrid &
> Amazon EC2). By using open community based software components and services
> like Airavata, gateways will be able to focus on providing additional
> scientific capabilities and to expanding the number of supported users. The
> capabilities of these gateways will offer clear benefits to society.
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