Library prosa.results.edf.rta.fully_nonpreemptive

RTA for Fully Non-Preemptive EDF

In this module we prove the RTA theorem for the fully non-preemptive EDF model.

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 and tasks are fully nonpreemptive.
  #[local] Existing Instance fully_nonpreemptive_job_model.
  #[local] Existing Instance fully_nonpreemptive_task_model.
  #[local] Existing Instance fully_nonpreemptive_rtc_threshold.

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.
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 non-preemptive uniprocessor schedule of this arrival sequence ...
Next, we assume that the schedule is a work-conserving schedule...
... and the schedule respects the policy defined by the job_preemptable function (i.e., jobs have bounded nonpreemptive segments).

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 also define a bound for the priority inversion caused by jobs with lower priority.
  Let blocking_bound A :=
    \max_(tsk_o <- ts | (blocking_relevant tsk_o)
                         && (task_deadline tsk_o > task_deadline tsk + A))
     (task_cost tsk_o - 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: nat.
  Hypothesis H_R_is_maximum:
      is_in_search_space A
        A + F blocking_bound A + (task_rbf (A + ε) - (task_cost tsk - ε))
                + bound_on_total_hep_workload A (A + F)
        R F + (task_cost tsk - ε).

Now, we can leverage the results for the abstract model with bounded nonpreemptive segments to establish a response-time bound for the more concrete model of fully nonpreemptive scheduling.