Library prosa.results.edf.rta.limited_preemptive

RTA for EDF with Fixed Preemption Points

In this module we prove the RTA theorem for EDF-schedulers with fixed preemption points.

Setup and Assumptions

Consider any type of tasks ...
  Context {Task : TaskType}.
  Context `{TaskCost Task}.
  Context `{TaskDeadline Task}.

... and any type of jobs associated with these tasks.
  Context {Job : JobType}.
  Context `{JobTask Job Task}.
  Context `{JobArrival Job}.
  Context `{JobCost Job}.

We assume the classic (i.e., Liu & Layland) model of readiness without jitter or self-suspensions, wherein pending jobs are always ready.
  #[local] Existing Instance basic_ready_instance.

We assume that jobs are limited-preemptive.
  #[local] Existing Instance limited_preemptive_job_model.

Consider any arrival sequence with consistent, non-duplicate arrivals.
Consider an arbitrary task set ts, ...
  Variable ts : list Task.

... assume that all jobs come from this task set, ...
... and the cost of a job cannot be larger than the task cost.
Next, we assume we have the model with fixed preemption points. I.e., each task is divided into a number of non-preemptive segments by inserting statically predefined preemption points.
Let max_arrivals be a family of valid arrival curves, i.e., for any task tsk in ts max_arrival tsk is (1) an arrival bound of tsk, and (2) it is a monotonic function that equals 0 for the empty interval delta = 0.
Let tsk be any task in ts that is to be analyzed.
  Variable tsk : Task.
  Hypothesis H_tsk_in_ts : tsk \in ts.

Next, consider any valid ideal uni-processor schedule with limited preemptions of this arrival sequence ...
Next, we assume that the schedule is a work-conserving schedule...
... and the schedule respects the scheduling policy.

Total Workload and Length of Busy Interval

We introduce the abbreviation rbf for the task request bound function, which is defined as task_cost(T) × max_arrivals(T,Δ) for a task T.
Next, we introduce task_rbf as an abbreviation for the task request bound function of task tsk.
  Let task_rbf := rbf tsk.

Using the sum of individual request bound functions, we define the request bound function of all tasks (total request bound function).
We define a bound for the priority inversion caused by jobs with lower priority.
  Let blocking_bound A :=
    \max_(tsk_other <- ts | (blocking_relevant tsk_other)
                             && (task_deadline tsk_other > task_deadline tsk + A))
     (task_max_nonpreemptive_segment tsk_other - ε).

Next, we define an upper bound on interfering workload received from jobs of other tasks with higher-than-or-equal priority.
Let L be any positive fixed point of the busy interval recurrence.
  Variable L : duration.
  Hypothesis H_L_positive : L > 0.
  Hypothesis H_fixed_point : L = total_rbf L.

Response-Time Bound

To reduce the time complexity of the analysis, recall the notion of search space.
Consider any value R, and assume that for any given arrival offset A in the search space, there is a solution of the response-time bound recurrence which is bounded by R.
  Variable R : duration.
  Hypothesis H_R_is_maximum:
     (A : duration),
      is_in_search_space A
       (F : duration),
        A + F blocking_bound A
                + (task_rbf (A + ε) - (task_last_nonpr_segment tsk - ε))
                + bound_on_total_hep_workload A (A + F)
        R F + (task_last_nonpr_segment tsk - ε).

Now, we can leverage the results for the abstract model with bounded non-preemptive segments to establish a response-time bound for the more concrete model of fixed preemption points.