Library prosa.results.gel.rta.bounded_pi

Require Export prosa.analysis.abstract.ideal.abstract_seq_rta.
Require Import prosa.util.int.
Require Import prosa.analysis.abstract.ideal.cumulative_bounds.
Require Import prosa.analysis.facts.readiness.basic.
Require Import prosa.model.schedule.priority_driven.
Require Import prosa.model.priority.gel.
Require Import analysis.facts.priority.gel.
Require Export prosa.analysis.facts.model.task_cost.

Abstract RTA for GEL-Schedulers with Bounded Priority Inversion

In this module we instantiate the abstract response-time analysis (aRTA) for GEL-schedulers assuming the ideal uni-processor model and real-time tasks with arbitrary arrival models.

The important feature of this instantiation is that it allows reasoning about the meaningful notion of priority inversion. However, we do not specify the exact cause of priority inversion (as there may be different reasons for this, like execution of a non-preemptive segment or blocking due to resource locking). We only assume that the duration of priority inversion is bounded.

Section AbstractRTAforGELwithArrivalCurves.

We consider tasks and jobs with the given parameters.

  Context {Task : TaskType} `{TaskCost Task} `{TaskRunToCompletionThreshold Task}
    `{TaskMaxNonpreemptiveSegment Task} `{MaxArrivals Task}.
  Context {Job : JobType} `{JobTask Job Task} {Arrival : JobArrival Job}
    {Cost : JobCost Job} `{JobPreemptable Job} `{PriorityPoint Task}.

We assume the basic readiness model.

#[local] Existing Instance basic_ready_instance.

A. Defining the System Model

We begin by defining the system model. First, we model arrivals using an arrival sequence. We assume that the arrival is consistent and does not contain duplicates.

Variable arr_seq : arrival_sequence Job.
Hypothesis H_valid_arrival_sequence : valid_arrival_sequence arr_seq.

We consider a valid, ideal uniprocessor schedule...

Variable sched : schedule (ideal.processor_state Job).
Hypothesis H_sched_valid : valid_schedule sched arr_seq.

... that respects the GEL policy at every preemption point.

Hypothesis H_respects_policy : respects_JLFP_policy_at_preemption_point arr_seq sched (GEL Job Task).

We assume that all arrivals have valid job costs.

Hypothesis H_valid_job_cost:
arrivals_have_valid_job_costs arr_seq.

We model the tasks in the system using a task set ts. We assume that all jobs in the system come from this task set.

Variable ts : list Task.
Hypothesis H_all_jobs_from_taskset : all_jobs_from_taskset arr_seq ts.

We assume that the task set respects the arrival curve defined by max_arrivals.

Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.
Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.

The task under consideration tsk is contained in ts.

Variable tsk : Task.
Hypothesis H_tsk_in_ts : tsk \in ts.

While we do not assume an explicit preemption model, we assume that any preemption model under consideration is valid. We also assume that the run-to-completion-threshold of the task tsk is valid.

  Hypothesis H_valid_preemption_model:
    valid_preemption_model arr_seq sched.
  Hypothesis H_valid_run_to_completion_threshold:
    valid_task_run_to_completion_threshold arr_seq tsk.

As mentioned, we assume that the priority inversion for the task tsk is bound by priority_inversion_bound.

  Variable priority_inversion_bound: duration → duration.
  Hypothesis H_priority_inversion_is_bounded:
    priority_inversion_is_bounded_by arr_seq sched tsk priority_inversion_bound.

B. Encoding the Scheduling Policy and Preemption Model

Next, we encode the scheduling policy and preemption model using the functions interference and interfering_workload. To this end, we simply reuse the general definitions of interference and interfering workload that apply to any JLFP policy, as defined in the module analysis.abstract.ideal.iw_instantiation.

  #[local] Instance ideal_jlfp_interference : Interference Job :=
    ideal_jlfp_interference arr_seq sched.

  #[local] Instance ideal_jlfp_interfering_workload : InterferingWorkload Job :=
    ideal_jlfp_interfering_workload arr_seq sched.

C. Abstract Work Conservation

Let us recall the abstract and classic notations of work conservation.

Let work_conserving_ab := definitions.work_conserving arr_seq sched.
Let work_conserving_cl := work_conserving.work_conserving arr_seq sched.

We assume that the schedule is work-conserving in the classic sense, which allows us to apply instantiated_i_and_w_are_coherent_with_schedule to conclude that abstract work-conservation also holds.

Hypothesis H_work_conserving : work_conserving_cl.

D. Bounding the Maximum Busy-Window Length

Next, we define L as the fixed point of the given equation. Given this definition of L, we can apply the theorem instantiated_busy_intervals_are_bounded to prove that L bounds the length of the busy window.

  Variable L : duration.
  Hypothesis H_L_positive : L > 0.
  Hypothesis H_fixed_point : L = total_request_bound_function ts L.

E. Defining task_IBF

Next, we define task_IBF and prove that task_IBF bounds the interference incurred by any job of tsk.

Consider the following parametrized interval.

Let interval (tsk_o : Task) (A : instant):=
((A + ε )%:R + task_priority_point tsk - task_priority_point tsk_o)%R.

We define the bound on the total higher-or-equal-priority (HEP) workload produced during the interval Δ as the sum of the RBFs of all tasks in the task set ts (excluding tsk) over the minimum of Δ and interval.

  Let bound_on_total_hep_workload (A Δ : duration) :=
        \sum_(tsk_o <- ts | tsk_o != tsk )
          task_request_bound_function tsk_o (minn `|Num.max 0%R (interval tsk_o A)| Δ).

Finally, task_IBF for an interval R is defined as the sum of bound_on_total_hep_workload in R and priority_inversion_bound.

Let task_IBF (A R : duration) := priority_inversion_bound A + bound_on_total_hep_workload A R.

For convenience, we define the following acronym.

Let PP (task : Task) := (task_priority_point task).

In this section, we prove the soundness of task_IBF. We start by establishing a bound on the HEP workload.

Section HepWorkloadBound.

Consider any job j of task tsk that has a positive job cost and is in the arrival sequence.

      Variable j : Job.
      Hypothesis H_j_arrives : arrives_in arr_seq j.
      Hypothesis H_job_of_tsk : job_of_task tsk j.
      Hypothesis H_job_cost_positive: job_cost_positive j.

Assume the busy interval of j is given by [t1,t2).

Variable t1 t2 : duration.
Hypothesis H_busy_interval : definitions.busy_interval sched j t1 t2.

Assume the relative arrival time of j is given by A.

Let A := job_arrival j - t1.

Consider any arbitrary interval Δ within the busy window.

Variable Δ : duration.
Hypothesis H_Δ_in_busy : t1 + Δ < t2.

Consider the set of jobs arriving in [t1, t1 + Δ).

Let jobs := arrivals_between arr_seq t1 (t1 + Δ).

We define a predicate to identify higher priority jobs coming from the task tsk.

Let GEL_from (tsk : Task) := fun (jo : Job) ⇒ hep_job jo j && (job_task jo == tsk ).

First, consider the case where interval ≤ Δ.

Section ShortenRange.

Consider any task tsk_o distinct from tsk. Assume tsk_o is in ts.

        Variable tsk_o : Task.
        Hypothesis H_tsko_in_ts: tsk_o \in ts.
        Hypothesis H_neq: tsk_o != tsk.

If Δ is greater than interval for tsk_o and A, ...

Hypothesis H_Δ_ge: (interval tsk_o A ≤ Δ %:R)%R.

... then the workload of jobs satisfying the predicate GEL_from in the interval [t1,t1 + Δ) is equal to the workload in the interval [t1, t1 + interval [tsk_o] [A]). Note that, we use the functions Z.to_nat to Z.of_nat to convert integers to natural numbers and vice-versa.

        Lemma total_workload_shorten_range:
          workload_of_jobs (GEL_from tsk_o)
            (arrivals_between arr_seq t1 (t1 + Δ))
          ≤ workload_of_jobs (GEL_from tsk_o)
              (arrivals_between arr_seq t1
                 `|Num.max 0%R (t1 %:R + interval tsk_o A)%R|).

      End ShortenRange.

Using the above lemma, we prove that bound_on_total_hep_workload bounds the sum of higher-priority workload over all tasks in ts.

      Corollary sum_of_workloads_is_at_most_bound_on_total_hep_workload :
        \sum_(tsk_o <- ts | tsk_o != tsk ) workload_of_jobs (GEL_from tsk_o) jobs
        ≤ bound_on_total_hep_workload A Δ.

    End HepWorkloadBound.

Finally, we prove that task_IBF bounds the interference incurred by tsk.

    Corollary instantiated_task_interference_is_bounded :
      task_interference_is_bounded_by
        arr_seq sched tsk task_IBF.

F. Defining the Search Space

In this section, we define the concrete search space for GEL. Then, we prove that, if a given A is in the abstract search space, then it is also included in the concrete search space.

In order to define the concrete search space, we define the predicates task_rbf_changes_at, ...

Definition task_rbf_changes_at (A : duration) :=
task_request_bound_function tsk A != task_request_bound_function tsk (A + ε).

... bound_on_total_hep_workload_changes_at, ...

  Definition bound_on_total_hep_workload_changes_at A :=
    has (fun tsko ⇒
           ((tsk != tsko ) && (interval tsko (A - ε) != interval tsko A )))
      ts.

... and priority_inversion_changes_at.

Definition priority_inversion_changes_at (A : duration) :=
priority_inversion_bound (A - ε) != priority_inversion_bound A.

Finally, we define the concrete search space as the set containing all points less than L at which any of the bounds on priority inversion, task rbf, or bound on total HEP workload changes.

  Definition is_in_search_space (A : duration) :=
    (A < L ) && (priority_inversion_changes_at A
                || task_rbf_changes_at A
                || bound_on_total_hep_workload_changes_at A ).

In this section, we prove that for any job j of tsk, if A is in the abstract search space, then it is also in the concrete search space.

Section ConcreteSearchSpace.

To rule out pathological cases with the concrete search space, we assume that the task cost is positive and the arrival curve is non-pathological.

Hypothesis H_task_cost_pos : 0 < task_cost tsk.
Hypothesis H_arrival_curve_pos : 0 < max_arrivals tsk ε.

For j, the total interference bound is defined as follows.

Let total_interference_bound (A Δ : duration) :=
task_request_bound_function tsk (A + ε) - task_cost tsk + task_IBF A Δ.

Consider any point A in the abstract search space.

    Variable A : duration.
    Hypothesis H_A_is_in_abstract_search_space:
      search_space.is_in_search_space L total_interference_bound A.

Then, A is also in the concrete search space.

    Lemma A_is_in_concrete_search_space:
      is_in_search_space A.

  End ConcreteSearchSpace.

G. Stating the Response-Time Bound R

Finally, we define the response-time bound R as follows.

  Variable R : duration.
  Hypothesis H_R_is_maximum:
    ∀ (A : duration),
      is_in_search_space A →
      ∃ (F : duration ),
        A + F ≥ priority_inversion_bound A
                + (task_request_bound_function tsk (A + ε) - (task_cost tsk - task_rtct tsk ))
                + bound_on_total_hep_workload A (A + F) ∧
          R ≥ F + (task_cost tsk - task_rtct tsk ).

  Section ResponseTimeReccurence.

In order to connect the concrete definition of R with the shape of response-time bound equation that aRTA expects, we prove the theorem correct_search_space.

To rule out pathological cases with the concrete search space, we assume that the task cost is positive and the arrival curve is non-pathological.

Hypothesis H_task_cost_pos : 0 < task_cost tsk.
Hypothesis H_arrival_curve_pos : 0 < max_arrivals tsk ε.

We define the total interference as defined above.

Let total_interference_bound (A Δ : duration) :=
task_request_bound_function tsk (A + ε) - task_cost tsk + task_IBF A Δ.

We know that if A is in the abstract search then it is in the concrete search space. We also know that if A is in the concrete search space then there exists an R that satisfies H_R_is_maximum. Using these facts, here we prove that if A is in the abstract search space then, there exists a solution to the response-time equation as stated in the aRTA.

    Corollary correct_search_space:
      ∀ A,
        search_space.is_in_search_space L total_interference_bound A →
        ∃ F ,
          A + F ≥ task_request_bound_function tsk (A + ε) - (task_cost tsk - task_rtct tsk ) +
                    task_IBF A (A + F) ∧ R ≥ F + (task_cost tsk - task_rtct tsk ).

  End ResponseTimeReccurence.

H. Soundness of the Response-Time Bound

Finally, we prove that R is a bound on the response time of the task tsk.

Theorem uniprocessor_response_time_bound_edf:
task_response_time_bound arr_seq sched tsk R.

End AbstractRTAforGELwithArrivalCurves.