Library prosa.results.fixed_priority.rta.bounded_nps

Require Export prosa.analysis.definitions.schedulability.
Require Export prosa.analysis.definitions.request_bound_function.
Require Export prosa.analysis.facts.model.sequential.
Require Export prosa.analysis.facts.busy_interval.priority_inversion.
Require Export prosa.results.fixed_priority.rta.bounded_pi.

Throughout this file, we assume ideal uni-processor schedules.
Require Import prosa.model.processor.ideal.

RTA for FP-schedulers with Bounded Non-Preemptive Segments

In this section we instantiate the Abstract RTA for FP-schedulers with Bounded Priority Inversion to FP-schedulers for ideal uni-processor model of real-time tasks with arbitrary arrival models and bounded non-preemptive segments.
Recall that Abstract RTA for FP-schedulers with Bounded Priority Inversion does not specify the cause of priority inversion. In this section, we prove that the priority inversion caused by execution of non-preemptive segments is bounded. Thus the Abstract RTA for FP-schedulers is applicable to this instantiation.
Section RTAforFPwithBoundedNonpreemptiveSegmentsWithArrivalCurves.

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

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

Consider an FP policy that indicates a higher-or-equal priority relation, and assume that the relation is reflexive and transitive.
  Context `{FP_policy Task}.
  Hypothesis H_priority_is_reflexive : reflexive_priorities.
  Hypothesis H_priority_is_transitive : transitive_priorities.

Consider any arrival sequence with consistent, non-duplicate arrivals.
  Variable arr_seq : arrival_sequence Job.
  Hypothesis H_arrival_times_are_consistent : consistent_arrival_times arr_seq.
  Hypothesis H_arr_seq_is_a_set : arrival_sequence_uniq arr_seq.

Next, consider any ideal uni-processor schedule of this arrival sequence, ...
  Variable sched : schedule (ideal.processor_state Job).

... allow for any work-bearing notion of job readiness, ...
  Context `{@JobReady Job (ideal.processor_state Job) _ Cost Arrival}.
  Hypothesis H_job_ready : work_bearing_readiness arr_seq sched.

... and assume that the schedule is valid.
  Hypothesis H_sched_valid : valid_schedule sched arr_seq.

In addition, we assume the existence of a function mapping jobs to their preemption points ...
  Context `{JobPreemptable Job}.

... and assume that it defines a valid preemption model with bounded non-preemptive segments.
  Hypothesis H_valid_model_with_bounded_nonpreemptive_segments:
    valid_model_with_bounded_nonpreemptive_segments arr_seq sched.

Next, we assume that the schedule is a work-conserving schedule...
  Hypothesis H_work_conserving : work_conserving arr_seq sched.

... and the schedule respects the policy defined by the job_preemptable function (i.e., jobs have bounded non-preemptive segments).
  Hypothesis H_respects_policy : respects_policy_at_preemption_point arr_seq sched.

Assume we have sequential tasks, i.e, jobs from the same task execute in the order of their arrival.
  Hypothesis H_sequential_tasks : sequential_tasks arr_seq sched.

Consider an arbitrary task set ts, ...
  Variable ts : list Task.

... assume that all jobs come from the task set, ...
  Hypothesis H_all_jobs_from_taskset : all_jobs_from_taskset arr_seq ts.

... and the cost of a job cannot be larger than the task cost.
  Hypothesis H_valid_job_cost:
    arrivals_have_valid_job_costs arr_seq.

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.
  Context `{MaxArrivals Task}.
  Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.
  Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.

Let tsk be any task in ts that is to be analyzed.
  Variable tsk : Task.
  Hypothesis H_tsk_in_ts : tsk \in ts.

Consider a valid preemption model...
  Hypothesis H_valid_preemption_model:
    valid_preemption_model arr_seq sched.

...and a valid task run-to-completion threshold function. That is, task_rtct tsk is (1) no bigger than tsk's cost, (2) for any job of task tsk job_rtct is bounded by task_rtct.
  Hypothesis H_valid_run_to_completion_threshold:
    valid_task_run_to_completion_threshold arr_seq tsk.

Let's define some local names for clarity.
  Let max_length_of_priority_inversion :=
    max_length_of_priority_inversion arr_seq.
  Let task_rbf := task_request_bound_function tsk.
  Let total_hep_rbf := total_hep_request_bound_function_FP ts tsk.
  Let total_ohep_rbf := total_ohep_request_bound_function_FP ts tsk.
  Let response_time_bounded_by := task_response_time_bound arr_seq sched.

We also define a bound for the priority inversion caused by jobs with lower priority.
  Definition blocking_bound :=
    \max_(tsk_other <- ts | ~~ hep_task tsk_other tsk)
     (task_max_nonpreemptive_segment tsk_other - ε).

Priority inversion is bounded

In this section, we prove that a priority inversion for task tsk is bounded by the maximum length of non-preemptive segments among the tasks with lower priority.
  Section PriorityInversionIsBounded.

First, we prove that the maximum length of a priority inversion of a job j is bounded by the maximum length of a non-preemptive section of a task with lower-priority task (i.e., the blocking term).
    Lemma priority_inversion_is_bounded_by_blocking:
      ∀ j t,
        arrives_in arr_seq j →
        job_of_task tsk j →
        max_length_of_priority_inversion j t ≤ blocking_bound.

Using the above lemma, we prove that the priority inversion of the task is bounded by blocking_bound.
    Lemma priority_inversion_is_bounded:
      priority_inversion_is_bounded_by
        arr_seq sched tsk blocking_bound.

  End PriorityInversionIsBounded.

Response-Time Bound

In this section, we prove that the maximum among the solutions of the response-time bound recurrence is a response-time bound for tsk.
  Section ResponseTimeBound.

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 = blocking_bound + total_hep_rbf L.

To reduce the time complexity of the analysis, recall the notion of search space.
    Let is_in_search_space := is_in_search_space tsk L.

Next, consider any value R, and assume that for any given arrival offset A from the search space there is a solution of the response-time bound recurrence that 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) - (task_cost tsk - task_rtct tsk))
                  + total_ohep_rbf (A + F) ∧
          F + (task_cost tsk - task_rtct tsk) ≤ R.

Then, using the results for the general RTA for FP-schedulers, we establish a response-time bound for the more concrete model of bounded nonpreemptive segments. Note that in case of the general RTA for FP-schedulers, we just assume that the priority inversion is bounded. In this module we provide the preemption model with bounded nonpreemptive segments and prove that the priority inversion is bounded.
    Theorem uniprocessor_response_time_bound_fp_with_bounded_nonpreemptive_segments:
      response_time_bounded_by tsk R.

  End ResponseTimeBound.

End RTAforFPwithBoundedNonpreemptiveSegmentsWithArrivalCurves.