Period and Computational Elasticity for Adaptive Real-Time Systems

A wide range range of real-world applications (including multimedia players, ad-hoc communication networks, online trading, radar tracking software, and other adaptive control algorithms) need adaptive adjustment to their resource utilizations at run-time, while still maintaining real-time guarantees. The elastic task model of soft real-time systems allows for the run-time manipulation of tasks’ processor utilizations in order to maintain a system-wide quality of service or accommodate needs of other tasks by assigning each task a period within a specified range. As originally presented, only sequential tasks executing on a single processor were considered. However, in the two decades since the elastic task model was first introduced, multiprocessor systems have become increasingly prevalent. This dissertation appropriately extends the elastic task model to include both multiprocessor scheduling of sequential adaptive tasks and scheduling of adaptive tasks with internal parallelism. It also introduces novel elastic concepts in which 1) tasks can vary their computational loads rather than their periods and 2) the more realistic scenario in which tasks are allowed to adapt among a discrete set of candidate processor utilizations rather than over a continuous range. A runtime system for parallel elastic tasks is also presented and used to demonstrate the benefit of discrete elastic scheduling by enabling adaptation in the application domain of real-time hybrid simulation (RTHS)