Elastic scheduling for flexible workload management

An increasing number of real-time applications, related to multimedia and adaptive control systems, require greater flexibility than classical real-time theory usually permits. In this paper, we present a novel scheduling framework in which tasks are treated as springs with given elastic coefficients to better conform to the actual load conditions. Under this model, periodic tasks can intentionally change their execution rate to provide different quality of service and the other tasks can automatically adapt their periods to keep the system underloaded. The proposed model can also be used to handle overload conditions in a more flexible way and to provide a simple and efficient mechanism for controlling a system's performance as a function of the current load.