rithms or policies. This is a more complicated approach and requires more code in the scheduler to implement. This leads to more overhead in managing a task set in a DSP system because the scheduler must now spend more time dynamically sorting through the system task set and prioritizing tasks for execution based on the scheduling policy. This leads to nondeterminism, which is not favorable, especially for hard real-time systems. Dynamic scheduling algorithms are online scheduling algorithms. The scheduling policy is applied to the task set during the execution of the system. The active task set changes dynamically as the system runs. The priority of the tasks can also change dynamically.
Static Scheduling Policies
Examples of static scheduling policies are rate monotonic scheduling and deadline monotonic scheduling. Examples of dynamic scheduling policies are earliest deadline first and least slack scheduling.
Rate monotonic scheduling is an optimal fixed-priority policy where the higher the frequency (1/period) of a task, the higher is its priority. This approach can be implemented in any operating system supporting the fixed-priority preemptive scheme, such as DSP/BIOS and VxWorks. Rate monotonic scheduling assumes that the deadline of a periodic task is the same as its period. Rate monotonic scheduling approaches are not new, being used by NASA, for example, on the Apollo space missions.
Deadline monotonic scheduling is a generalization of the Rate-Monotonic scheduling policy. In this approach, the deadline of a task is a fixed (relative) point in time from the beginning of the period. The shorter this (fixed) deadline, the higher the priority.
Dynamic Scheduling Policies
Dynamic scheduling algorithms can be broken into two main classes of algorithms. The first is referred to as a "dynamic planning based approach." This approach is very useful for systems that must dynamically accept new tasks into the system; for example a wireless base station that must accept new calls into the system at a some dynamic rate. This approach combines some of the flexibility of a dynamic approach and some of the predictability of a more static approach. After a task arrives, but before its execution begins, a check is made to determine whether a schedule can be created that can handle the new task as well as the currently executing tasks. Another approach, called the dynamic best effort apprach, uses the task deadlines to set the priorities. With this approach, a task could be pre-empted at any time during its execution. So, until the deadline arrives or the task finishes execution, we do not have a guarantee that a timing constraint can be met. Examples of dynamic best effort algorithms are Earliest Deadline First and Least Slack scheduling.
Earliest deadline first scheduling
Earliest deadline first scheduling is a dynamic priority preemptive policy. With this approach, the deadline of a task instance is the absolute point in time by which the instance must complete. The task deadline is computed when the instance is created. The operating system scheduler picks the task with the earliest deadline to run. A task with an earlier deadline preempts a task with a later deadline.
Least slack scheduling
Least slack scheduling is also a dynamic priority preemptive policy. The slack of a task instance is the absolute deadline minus the remaining execution time for the instance to complete. The OS scheduler picks the task with the shortest slack to run first. A task with a smaller slack preempts a task with a larger slack. This approach maximizes the minimum lateness of tasks.
Dynamic priority preemptive scheduling
In a dynamic scheduling approach such as dynamic priority preemptive scheduling, the priority of a task can change from instance to instance or within the execution of an instance. In this approach a higher priority task preempts a lower priority task. Very few commercial RTOS support such policies because this approach leads to systems that are hard to analyze for real-time and determinism properties. Thus, the analysis in the fol