In many real time applications, especially those involving computer controlled systems, the application tasks often have a maximal acceptable latency, and small latency is preferred to large. The interaction between choosing task periods to meet the individual latency requirements and scheduling the resulting task set was investigated by D. Seto et al. (1996) using dynamic priority scheduling methods. We present algorithms based on static priority scheduling methods to determine optimal periods for each task in the task set. The solution to the period selection problem optimizes a system wide performance measure, subject to meeting the maximal acceptable latency requirements of each task. The paper also contributes to a new aspect of rate monotonic scheduling, the optimal design of task periods in connection with application related timing specifications and task set schedulability.

Most real-time computer-controlled systems are built in two separate steps, each in isolation: controller design and its digital implementation. Computational tasks that realize the control algorithms are usually scheduled by treating their execution times and periods as unchangeable parameters. Task scheduling therefore depends only on the limited computing resources available. On the other hand, controller design is primarily based on the continuous-time dynamics of the physical system being controlled. The set of tasks resulting from this controller design may not be schedulable with the limited computing resources available. Even if the given set of tasks is schedulable, the overall control performance may not be optimal in the sense that they do not make a full use of the computing resource. We propose an integrated approach to controller design and task scheduling. Specifically, task frequencies (or periods) are allowed to vary within a certain range as long as such a change does not affect critical control functions such as maintenance of system stability. We present an algorithm that optimizes task frequencies and then schedules the resulting tasks with the limited computing resources available. The proposed approach is also applicable to failure recovery and reconfiguration in real-time control systems.