This paper presents a novel methodology to develop scheduling algorithms. The scheduling problem is phrased as a control problem, and control-theoretical techniques are used to design a scheduling algorithm that meets specific requirements. Unlike most approaches to feedback scheduling, where a controller integrates a "basic" scheduling algorithm and dynamically tunes its parameters and hence its performances, our methodology essentially reduces the design of a scheduling algorithm to the synthesis of a controller that closes the feedback loop. This approach allows the re-use of control-theoretical techniques to design efficient scheduling algorithms; it frames and solves the scheduling problem in a general setting; and it can naturally tackle certain peculiar requirements such as robustness and dynamic performance tuning. A few experiments demonstrate the feasibility of the approach on a real-time benchmark.
In @cite_15 , the controller adjusts the reservation time to within an upper bound given by the most frequently activated task. The model of the plant is a continuous-time system whose variables record the queuing time of tasks. The effectiveness of the method proposed is validated through simulations.
@cite_15
2156368412 2156946069 2588494934 2019116530
This paper defines and simulates a closed-loop optimal control strategy for load shifting in a plant that is charged for electricity on both time-of-use (TOU) and maximum demand (MD). A model predictive control approach is used to implement the closed-loop optimal control model, and the optimization problem is solved with integer programming. The simulated control model yields near optimal switching times which reduce the TOU and MD costs. The results show a saving of 5.8 for the overall plant, and the largest portion of the saving is due to a reduction in MD. The effect of disturbances, model uncertainty and plant failure is also simulated to demonstrate the benefits of a model predictive control model. When executing soft real-time tasks in a shared processor, it is important to properly allocate the computational resources such that the quality of service requirements of each task are satisfied. In this paper we propose Adaptive Reservations, based on applying a feedback scheme to a reservation based scheduler After providing a precise mathematical model of the scheduler, we describe how this model can be used for synthesising the controller by applying results from control theory. Finally, we show the effectiveness of our method by simulation and by experiments with an MPEG player running on a modified Linux kernel. Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and reduces the state space to be verified compared to the open-loop case. To be applied, it needs the controller and the plant formal models to be coupled. There are approaches for controller synthesis, but little has been done regarding plant model construction. While manual plant modeling is time consuming and error-prone, discretizing a simulation model of the plant leads to state excess. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples, or traces, and temporal properties. The proposed method, which is based on the translation of the problem to the Boolean satisfiability problem, is evaluated and shown to be applicable on several case study plant model synthesis tasks and on randomly generated problem instances. Abstract This paper investigates the stabilizability of a linear, discrete-time plant with a real-valued output when the controller, which may be nonlinear, receives observation data at a known rate. It is first shown that, under a finite horizon cost equal to the m th output moment, the problem reduces to quantizing the initial output. Asymptotic quantization theory is then applied to directly obtain the limiting coding and control scheme as the horizon approaches infinity. This is proven to minimize a particular infinite horizon cost, the value of which is derived. A necessary and sufficient condition then follows for there to exist a coding and control scheme with the specified data rate that takes the m th output moment to zero asymptotically with time. If the open-loop plant is finite-dimensional and time-invariant, this condition simplifies to an inequality involving the data rate and the unstable plant pole with greatest magnitude. Analagous results automatically hold for the related problem of state estimation with a finite data rate.