e. 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