Mohamed Hussein
ABSTRACT
The PerCo performance control framework is capable of managing the distributed execution of scientific coupled models using migration, for example, in response to changes in an execution environment. PerCo monitors execution times and reacts according to an adaptive performance control strategy whenever serious changes of behaviour occur. A computationally cheap technique is used per model to smooth the series of monitored execution times and to provide a short-term forecast for future execution times on currently assigned resources. Where this short-term forecast fails to be achieved, the system analyses whether migration would improve matters. For models that are candidates for migration, more accurate but computationally expensive techniques are used to form a longer-term prediction of future execution times on various candidate resources. Based on the predicted gain, a migration decision is made taking account of the expected cost of migration. Experimental results for small real scientific coupled models show that the performance control strategy behaves effectively in scenarios in which the ambient load is varied during execution.
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Index Terms
- Adaptive performance control for distributed scientific coupled models
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