Details
Description
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.
Attachments
Issue Links
- supercedes
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AIRAVATA-1084 [GSoC] Prototype Airavata Support for Application Scheduling using Ultrscan usecase
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- Closed
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