Optimization-Based Dynamic Reconfiguration of Real-Time Schedulers With Support for Stochastic Processor Consumption

The complexity of real-time systems has substantially increased in the past few years regarding both hardware and software aspects. The use of modern sensors, able to capture image and audio data, demands predictable multimedia-like data processing. Moreover, applications like autonomous robots, surveillance, or modern multimedia players may well be characterized by several operation modes, each one associated with light conditions, vision angle, change in user requirements, etc. In this paper, we describe suitable scheduling mechanisms that address these aspects. Application modes are characterized by their required processing bandwidth and benefit values. By using bandwidth reservation schedulers, dynamic reconfiguring scheduling parameters is seen as an optimization problem whose goal is to maximize the overall system benefit subject to schedulability constraints. Two different models for the problem are defined, Discrete and Continuous. The former gives rise to an NP-Hard problem for which efficient approximate solutions are derived. An optimal and polynomial solution to the Continuous model is derived. Both models are then extended to incorporate task execution times described as probability distributions. Making use of this stochastic modeling one is able to dynamically reconfigure the scheduler subject to probabilistic schedulability guarantees. The derived solutions are evaluated by extensive simulation, which indicates the good performance of the proposed reconfiguration mechanisms.