Library rt.restructuring.analysis.fixed_priority.rta.nonpr_reg.concrete_models.floating

Throughout this file, we assume ideal uniprocessor schedules.
Throughout this file, we assume the basic (i.e., Liu & Layland) readiness model.
Throughout this file, we assume the task model with floating non-preemptive regions.

RTA for Model with Floating Non-Preemptive Regions

In this module we prove the RTA theorem for floating non-preemptive regions FP model.
Consider any type of tasks ...
  Context {Task : TaskType}.
  Context `{TaskCost 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.
Assume we have the model with floating nonpreemptive regions. I.e., for each task only the length of the maximal nonpreemptive segment is known and each job level is divided into a number of nonpreemptive segments by inserting preemption points.
Consider an arbitrary task set ts, ...
  Variable ts : list Task.

... assume that all jobs come from the 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 with limited preemptions of this arrival sequence ...
... where jobs do not execute before their arrival or after completion.
Consider an FP policy that indicates a higher-or-equal priority relation, and assume that the relation is reflexive and transitive.
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 thejob_preemptable function (i.e., jobs have bounded nonpreemptive segments).
Let's define some local names for clarity.
Next, we define a bound for the priority inversion caused by tasks of lower priority.
Let L be any positive fixed point of the busy interval recurrence, determined by the sum of blocking and higher-or-equal-priority workload.
  Variable L : duration.
  Hypothesis H_L_positive : L > 0.
  Hypothesis H_fixed_point : L = blocking_bound + total_hep_rbf L.

To reduce the time complexity of the analysis, recall the notion of search space.
Next, consider any value R, and assume that for any given arrival A from 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 + task_rbf (A + ε) + total_ohep_rbf (A + F)
        F R.

Now, we can reuse the results for the abstract model with bounded nonpreemptive segments to establish a response-time bound for the more concrete model with floating nonpreemptive regions.
  Theorem uniprocessor_response_time_bound_fp_with_floating_nonpreemptive_regions:
    response_time_bounded_by tsk R.
  Proof.
    move: (H_valid_task_model_with_floating_nonpreemptive_regions) ⇒ [LIMJ JMLETM].
    move: (LIMJ) ⇒ [BEG [END _]].
    eapply uniprocessor_response_time_bound_fp_with_bounded_nonpreemptive_segments.
    all: eauto 2 with basic_facts.
    intros A SP.
    rewrite subnn subn0.
    destruct (H_R_is_maximum _ SP) as [F [EQ LE]].
      by F; rewrite addn0; split.
  Qed.

End RTAforFloatingModelwithArrivalCurves.