Library prosa.results.edf.rta.fully_preemptive

RTA for Fully Preemptive EDF

In this section we prove the RTA theorem for the fully preemptive EDF model
Throughout this file, we assume the EDF priority policy, ideal uni-processor schedules, and the basic (i.e., Liu & Layland) readiness model.
Require Import prosa.model.priority.edf.
Require Import prosa.model.processor.ideal.
Require Import prosa.model.readiness.basic.

Furthermore, we assume the fully preemptive task 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}.

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 ideal uniprocessor schedule of the arrival sequence ...
... where jobs do not execute before their arrival or after completion.
Assume we have sequential tasks, i.e, jobs from the same task execute in the order of their arrival.
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 non-preemptive 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).
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 = task_rbf (A + ε) + bound_on_total_hep_workload A (A + F)
        F R.

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 fully preemptive scheduling.

  Let response_time_bounded_by := task_response_time_bound arr_seq sched.

  Theorem uniprocessor_response_time_bound_fully_preemptive_edf:
    response_time_bounded_by tsk R.
    have BLOCK: blocking_bound ts tsk = 0.
    { by rewrite /blocking_bound /parameters.task_max_nonpreemptive_segment
                 /fully_preemptive.fully_preemptive_model subnn big1_eq. }
    eapply uniprocessor_response_time_bound_edf_with_bounded_nonpreemptive_segments with (L0 := L) .
    all: eauto 2 with basic_facts.
    - moveA /andP [LT NEQ].
      specialize (H_R_is_maximum A); feed H_R_is_maximum.
      { by apply/andP; split. }
      move: H_R_is_maximum ⇒ [F [FIX BOUND]].
       F; split.
      + by rewrite BLOCK add0n subnn subn0.
      + by rewrite subnn addn0.

End RTAforFullyPreemptiveEDFModelwithArrivalCurves.