Modeling the impact of run-time uncertainty on optimal computation scheduling using feedback

Abstract

Increasingly, feedback of measured run-time information is being used in the optimization of computation execution. This paper introduces a model relating the static view of a computation to its run-time variance that is useful in this context. A notion of uncertainty is then used to provide bounds on key scheduling parameters of the run-time computation. To illustrate the relationship between fidelity in measured information and minimum schedulable, grain size, we apply the bounds to three existing parallel architectures for the case of run-time variance caused by monitoring intrusion. We also outline a hybrid static-dynamic scheduling paradigm-SEDIA-that uses the model of uncertainty to optimize computation for execution in the presence of run-time variance from sources other than monitoring intrusion.