Flexible Scheduling Methods and Tools for Real-Time Control Systems

This thesis deals with flexibility in the design of real-time control systems. By dynamic resource scheduling it is possible to achieve on-line adaptability and increased control performance under resource constraints. The approach requires simulation tools for control and real-time systems co-design. One approach to achieve flexibility in the run-time scheduling of control tasks is feedback scheduling, where resources are scheduled dynamically based on measurements of actual timing variations and control performance. An overview of feedback scheduling techniques for control systems is presented.A flexible strategy for implementation of model predictive control (MPC) is described. In MPC, the control signal in each sample is obtained by the solution of a constrained quadratic optimization problem. A termination criterion is derived that, unlike traditional MPC, takes the effects of computational delay into account in the optimization. A scheduling scheme is also described, where the MPC cost functions being minimized are used as dynamic task priorities for a set of MPC tasks. The MATLAB/Simulink-based simulator TrueTime is presented. TrueTime is a co-design tool that facilitates simulation of distributed real-time control systems, where the execution of controller tasks in a real-time kernel is simulated in parallel with network transmissions and the continuous-time plant dynamics. Using TrueTime it is possible to study the effects of CPU and network scheduling on control performance and to experiment with flexible scheduling techniques and compensation schemes. A general overview of the simulator is given and the event-based kernel implementation is described.TrueTime is used in two simulation case studies. The first emulates TCP on top of standard Ethernet to simulate networked control of a robot system. The second case study uses TrueTime to simulate a web server application. A feedback scheduling strategy for QoS control in the web server is described

Simulated metamorphosis - a novel optimizer

This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Using Imprecise Computing for Improved Real-Time Scheduling

Conventional hard real-time scheduling is often overly pessimistic due to the worst case execution time estimation. The pessimism can be mitigated by exploiting imprecise computing in applications where occasional small errors are acceptable. This leverage is investigated in a few previous works, which are restricted to preemptive cases. We study how to make use of imprecise computing in uniprocessor non-preemptive real-time scheduling, which is known to be more difficult than its preemptive counterpart. Several heuristic algorithms are developed for periodic tasks with independent or cumulative errors due to imprecision. Simulation results show that the proposed techniques can significantly improve task schedulability and achieve desired accuracy– schedulability tradeoff. The benefit of considering imprecise computing is further confirmed by a prototyping implementation in Linux system. Mixed-criticality system is a popular model for reducing pessimism in real-time scheduling while providing guarantee for critical tasks in presence of unexpected overrun. However, it is controversial due to some drawbacks. First, all low-criticality tasks are dropped in high-criticality mode, although they are still needed. Second, a single high-criticality job overrun leads to the pessimistic high-criticality mode for all high-criticality tasks and consequently resource utilization becomes inefficient. We attempt to tackle aforementioned two limitations of mixed-criticality system simultaneously in multiprocessor scheduling, while those two issues are mostly focused on uniprocessor scheduling in several recent works. We study how to achieve graceful degradation of low-criticality tasks by continuing their executions with imprecise computing or even precise computing if there is sufficient utilization slack. Schedulability conditions under this Variable-Precision Mixed-Criticality (VPMC) system model are investigated for partitioned scheduling and global fpEDF-VD scheduling. And a deferred switching protocol is introduced so that the chance of switching to high-criticality mode is significantly reduced. Moreover, we develop a precision optimization approach that maximizes precise computing of low-criticality tasks through 0-1 knapsack formulation. Experiments are performed through both software simulations and Linux proto- typing with consideration of overhead. Schedulability of the proposed methods is studied so that the Quality-of-Service for low-criticality tasks is improved with guarantee of satisfying all deadline constraints. The proposed precision optimization can largely reduce computing errors compared to constantly executing low-criticality tasks with imprecise computing in high-criticality mode

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

A multi-criteria approach for nurse scheduling : fuzzy simulated metamorphosis algorithm approach

Motivated by the biological metamorphosis process and the need to solve multi-objective optimization problems with conflicting and fuzzy goals and constraints, this paper proposes a simulated metamorphosis algorithm, based on the concepts of biological evolution in insects, such as moths, butterflies, and beetles. By mimicking the hormone controlled evolution process the algorithm works on a single candidate solution, going through initialization, iterative growth loop, and finally maturation loop. The method is a practical way to optimizing multi-objective problems with fuzzy conflicting goals and constraints. The approach is applied to the nurse scheduling problem. Equipped with the facility to incorporate the user’s choices and wishes, the algorithm offers an interactive approach that can accommodate the decision maker’s expert intuition and experience, which is otherwise impossible with other optimization algorithms. By using hormonal guidance and unique operators, the algorithm works on a single candidate solution, and efficiently evolves it to a near-optimal solution. Computational experiments show that the algorithm is competitive