Improved Task Management Techniques for Enforcing EDF Scheduling on Recurring Tasks

The management of tasks is an essential requirement in most real-time and embedded systems, but invariably leads to unwanted CPU overheads. This paper is concerned with task management in real-time and embedded systems employing the Earliest Deadline First (EDF) scheduling algorithm. Currently, the best known techniques to manage EDF scheduling lead to overheads with complexity O(log n), where n is the number of recurring (periodic/sporadic) tasks. In this paper it will be shown that if both the ready and waiting queues are represented by either i) timing and indexed deadline wheels or ii) digital search trees, then all scheduling decisions may be made in time proportional to the logarithm of the largest time representation required by the system, p_m. In cases where p_m is relatively small, for example in some embedded systems, extremely efficient task management may then be achieved. Experimental results are then presented, and it is shown that on an ARM7 microcontroller, when the number of tasks is comparatively large for such a platform (> 250), the worst-case scheduling overheads remain effectively constant and below 20 ¿s. The results indicate that the techniques provide some improved performance over previous methods, and also seem to indicate that there is little discernable difference between the overheads incurred between employing a fixed- or dynamic-priority scheduler in a given system.