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https://issues.apache.org/jira/browse/AIRAVATA-357?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13256052#comment-13256052
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Milinda Lakmal Pathirage commented on AIRAVATA-357:
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I'm planning to focus more on the scenario where we execute map tasks on the
cloud and reduce tasks on the local cluster like scenario assuming that the
computing resource requirements are low for reduce task when compared with the
map task. But this involves data movement overhead and additional setup cost
for local Hadoop cluster. And also we may need to tweak Hadoop to support this.
This has additional infrastructure setup complexity when compared with cloud
bursting setup where we do whole computation on a Hadoop cluster running on
cloud.
Initial plan is to do simple resource selection based on the location of data
and availability of computing resources. Plan is to improve this based on the
experience gained during the implementation of simple version of the algorithm.
> [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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