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 rat