Admission Control for Complex Responsive Systems

Modern software systems have become too complex to make accurate predictions about their performance under different configurations. Real-time or even responsiveness requirements cannot be met because it is not possible to perform admission control for new or changing tasks if we cannot tell how their execution affects the other tasks already running. Previously, we proposed a resource-allocation middleware that manages the execution of tasks in a complex software system with real-time requirements. The middleware behavior can be modeled depending on the configuration of the tasks running, so that the performance of any given configuration can be calculated. This makes it possible to have admission control in such a system, but the model requires knowledge of run-time parameters. We propose the utilization of machine-learning algorithms to obtain the model parameters, and to be able to predict the system performance under any configuration, so that we can provide a full admission control mechanism for complex software systems. In this paper, we present such an admission control mechanism, measure its accuracy in estimating the parameters of the model, and evaluate its performance to determine its suitability for a real-time or responsive system.

A Condensed version of this report (title: Admission Control for Distributed Complex Responsive Systems) has appeared in: Eighth International Parallel and Distributed Computing, 2009 "ISPDC '09," (IEEE July 2009) 226-233.