Using stochastic intervals to predict application behavior on contended resources

Current distributed parallel platforms can provide the resources required to execute a scientific application efficiently. However when these platforms are shared by multiple users, performance prediction becomes increasingly difficult due to the dynamic behavior of the system. This paper addresses the use of stochastic values, represented by intervals, to parameterize performance models. We describe a method for using upper and lower bound information to parameterize application prediction models in order to make better predictions about the application's behavior in a contentious environment. We demonstrate this technique for a set of 3 applications under different workloads on a production network of workstations.