Abstract

This paper presents an eficient way to analyse the performance of task sets, where the task execution time is specijied as a generalized continuous probability distribution. We consider $xed task sets of periodic, possibly dependent, nonpre-emptable tasks with deadlines less than or equal to the period. Our method is not restricted to any specijic scheduling policy and supports policies with both dynamic and static priorities. An algorithm to construct the underlying stochastic process in a memory and time eficient way is presented.We discuss the impact of various parameters on complexity, in terms of analysis time and required memoty Experimental results show the eficiency of the proposed approach.

In the past decade, the limitations of models considering fixed (worst-case) task execution times have been acknowledged for large application classes within soft real-time systems. A more realistic model considers the tasks having varying execution times with given probability distributions. Considering such a model with specified task execution time probability distribution functions, an important performance indicator of the system is the expected deadline miss ratio of the tasks and of the task graphs. This article presents an approach for obtaining this indicator in an analytic way. Our goal is to keep the analysis cost low, in terms of required analysis time and memory, while considering as general classes of target application models as possible. The following main assumptions have been made on the applications that are modeled as sets of task graphs: the tasks are periodic, the task execution times have given generalized probability distribution functions, the task execution deadlines are given and arbitrary, the scheduling policy can belong to practically any class of non-preemptive scheduling policies, and a designer supplied maximum number of concurrent instantiations of the same task graph is tolerated in the system. Experiments show the efficiency of the proposed technique for monoprocessor systems.

No abstract

Both analysis and design optimisation of real-time systems has predominantly concentrated on considering hard real-time constraints. For a large class of applications, however, this is both unrealistic and leads to unnecessarily expensive implementations. This paper addresses the problem of task priority assignment and task mapping in the context of multiprocessor applications with stochastic execution times and in the presence of constraints on the percentage of missed deadlines. We propose a design space exploration strategy together with a fast method for system performance analysis. Experiments emphasize the efficiency of the proposed analysis method and optimisation heuristic in generating high-quality implementations of soft real-time systems with stochastic task execution times and constraints on deadline miss ratios. Manolache, S., Eles, P., and Peng, Z. 2008. Task mapping and priority assignment for soft realtime applications under deadline miss ratio constraints.