Hard real-time systems focus on obtaining a feasible schedule while satisfying different temporal requirements. In safety-critical applications, this schedule is generated offline. This paper explores different integer linear programming techniques (ILP) to schedule uniprocessor hard real-time systems. The goal is to efficiently obtain a static schedule for periodic tasks and partitioned systems where temporal and spatial isolation is crucial. The advantage of the proposed ILP techniques is the possibility of choosing the optimization criteria so that deadlines are met and better performance quality is achieved. The drawback is the time spent finding an optimal solution. We propose an ILP method that reduces by 70% the time needed to obtain an optimal solution compared to basic approaches. This method is called the rolling task MILP approach and the optimization problem is addressed task by task. Experimental results show that our approach also achieves better results than heuristics when trying to reduce temporal parameters such as response times, context switches, and jitter. This makes our solution suitable for control systems and other applications. INDEX TERMS integer linear programming, optimization, partitioned systems, real-time systems, static scheduling.
System reliability is defined as the probability of satisfactory performance of a system under stated conditions for a specified period of time. According to this definition, four parameters, including probability, satisfactory performance, specific conditions, and time should be exactly characterized to evaluate the system reliability accurately. However, due to the uncertainty involved in real situations, it is hardly possible to assess the aforementioned parameters precisely. In this article, two general and distinct approaches, including Zadeh's extension principle and modification of fuzzy parametric programming (FPP), are proposed to take into account such uncertainty in a famous reliability problem called the overspeed protection system. According to Zadeh's extension principle, a pair of nonlinear programming problems is formulated to compute α‐level cuts of fuzzy system reliability. The membership function of fuzzy system reliability can then be constructed analytically by numerating different values of α. This fuzzy system reliability presents flexibility for further system analysis. In the second approach, from a different point of view, a variant of FPP is improved that provides a crisp value as a system reliability measure. The rewarding point of the latter procedure is to handle the problem in a computationally easier way.
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