Library prosa.classic.model.schedule.uni.limited.rbf
Require Import prosa.classic.util.all.
Require Import prosa.classic.model.time prosa.classic.model.arrival.basic.job
prosa.classic.model.arrival.basic.task_arrival
prosa.classic.model.priority
prosa.classic.model.arrival.basic.arrival_sequence.
Require Import prosa.classic.model.schedule.uni.schedule.
Require Import prosa.classic.model.arrival.curves.bounds.
Require Import prosa.classic.analysis.uni.arrival_curves.workload_bound.
From mathcomp Require Import ssreflect ssrbool eqtype ssrnat seq path fintype bigop.
Module RBF.
Import Job Time ArrivalSequence ArrivalCurves TaskArrival Priority MaxArrivalsWorkloadBound.
(* In this section, we prove some properties of Request Bound Functions (RBF). *)
Section RBFProperties.
Context {Task: eqType}.
Variable task_cost: Task → time.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any arrival sequence. *)
Variable arr_seq: arrival_sequence Job.
Hypothesis H_arrival_times_are_consistent:
arrival_times_are_consistent job_arrival arr_seq.
(* Consider an FP policy that indicates a higher-or-equal priority relation,
and assume that the relation is reflexive and transitive. *)
Variable higher_eq_priority: FP_policy Task.
Hypothesis H_priority_is_reflexive: FP_is_reflexive higher_eq_priority.
Hypothesis H_priority_is_transitive: FP_is_transitive higher_eq_priority.
(* Let tsk be any task. *)
Variable tsk: Task.
(* Let max_arrivals be a proper arrival curve for task tsk, i.e.,
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. *)
Variable max_arrivals: Task → time → nat.
Hypothesis H_proper_arrival_curve:
proper_arrival_curve job_task arr_seq max_arrivals tsk.
(* Let's define some local names for clarity. *)
Let task_rbf := task_request_bound_function task_cost max_arrivals tsk.
(* We prove that task_rbf 0 is equal to 0. *)
Lemma task_rbf_0_zero:
task_rbf 0 = 0.
Proof.
rewrite /task_rbf /task_request_bound_function.
apply/eqP; rewrite muln_eq0; apply/orP; right; apply/eqP.
by move: H_proper_arrival_curve ⇒ [_ [T _]]; apply T.
Qed.
(* We prove that task_rbf is monotone. *)
Lemma task_rbf_monotone:
monotone task_rbf leq.
Proof.
rewrite /monotone; intros.
rewrite /task_rbf /task_request_bound_function leq_mul2l.
apply/orP; right.
by move: H_proper_arrival_curve ⇒ [_ T]; apply T.
Qed.
(* Consider any job j of tsk. *)
Variable j: Job.
Hypothesis H_j_arrives: arrives_in arr_seq j.
Hypothesis H_job_of_tsk: job_task j = tsk.
(* Then we prove that task_rbf 1 is greater than or equal to task cost. *)
Lemma task_rbf_1_ge_task_cost:
task_rbf 1 ≥ task_cost tsk.
Proof.
have ALT: ∀ n, n = 0 ∨ n > 0.
{ by clear; intros n; destruct n; [left | right]. }
specialize (ALT (task_cost tsk)); destruct ALT as [Z | POS]; first by rewrite Z.
rewrite leqNgt; apply/negP; intros CONTR.
move: H_proper_arrival_curve ⇒ [ARRB _].
specialize (ARRB (job_arrival j) (job_arrival j + 1)).
feed ARRB; first by rewrite leq_addr.
rewrite addKn in ARRB.
move: CONTR; rewrite /task_rbf /task_request_bound_function; move ⇒ CONTR.
move: CONTR; rewrite -{2}[task_cost tsk]muln1 ltn_mul2l; move ⇒ /andP [_ CONTR].
move: CONTR; rewrite -addn1 -{3}[1]add0n leq_add2r leqn0; move ⇒ /eqP CONTR.
move: ARRB; rewrite CONTR leqn0 eqn0Ngt; move ⇒ /negP T; apply: T.
rewrite /num_arrivals_of_task -has_predT.
rewrite /arrivals_of_task_between /is_job_of_task.
apply/hasP; ∃ j; last by done.
rewrite /jobs_arrived_between addn1 big_nat_recl; last by done.
rewrite big_geq ?cats0; last by done.
rewrite mem_filter.
apply/andP; split.
- by apply/eqP.
- move: H_j_arrives ⇒ [t ARR].
move: (ARR) ⇒ CONS.
apply H_arrival_times_are_consistent in CONS.
by rewrite CONS.
Qed.
End RBFProperties.
End RBF.
Require Import prosa.classic.model.time prosa.classic.model.arrival.basic.job
prosa.classic.model.arrival.basic.task_arrival
prosa.classic.model.priority
prosa.classic.model.arrival.basic.arrival_sequence.
Require Import prosa.classic.model.schedule.uni.schedule.
Require Import prosa.classic.model.arrival.curves.bounds.
Require Import prosa.classic.analysis.uni.arrival_curves.workload_bound.
From mathcomp Require Import ssreflect ssrbool eqtype ssrnat seq path fintype bigop.
Module RBF.
Import Job Time ArrivalSequence ArrivalCurves TaskArrival Priority MaxArrivalsWorkloadBound.
(* In this section, we prove some properties of Request Bound Functions (RBF). *)
Section RBFProperties.
Context {Task: eqType}.
Variable task_cost: Task → time.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any arrival sequence. *)
Variable arr_seq: arrival_sequence Job.
Hypothesis H_arrival_times_are_consistent:
arrival_times_are_consistent job_arrival arr_seq.
(* Consider an FP policy that indicates a higher-or-equal priority relation,
and assume that the relation is reflexive and transitive. *)
Variable higher_eq_priority: FP_policy Task.
Hypothesis H_priority_is_reflexive: FP_is_reflexive higher_eq_priority.
Hypothesis H_priority_is_transitive: FP_is_transitive higher_eq_priority.
(* Let tsk be any task. *)
Variable tsk: Task.
(* Let max_arrivals be a proper arrival curve for task tsk, i.e.,
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. *)
Variable max_arrivals: Task → time → nat.
Hypothesis H_proper_arrival_curve:
proper_arrival_curve job_task arr_seq max_arrivals tsk.
(* Let's define some local names for clarity. *)
Let task_rbf := task_request_bound_function task_cost max_arrivals tsk.
(* We prove that task_rbf 0 is equal to 0. *)
Lemma task_rbf_0_zero:
task_rbf 0 = 0.
Proof.
rewrite /task_rbf /task_request_bound_function.
apply/eqP; rewrite muln_eq0; apply/orP; right; apply/eqP.
by move: H_proper_arrival_curve ⇒ [_ [T _]]; apply T.
Qed.
(* We prove that task_rbf is monotone. *)
Lemma task_rbf_monotone:
monotone task_rbf leq.
Proof.
rewrite /monotone; intros.
rewrite /task_rbf /task_request_bound_function leq_mul2l.
apply/orP; right.
by move: H_proper_arrival_curve ⇒ [_ T]; apply T.
Qed.
(* Consider any job j of tsk. *)
Variable j: Job.
Hypothesis H_j_arrives: arrives_in arr_seq j.
Hypothesis H_job_of_tsk: job_task j = tsk.
(* Then we prove that task_rbf 1 is greater than or equal to task cost. *)
Lemma task_rbf_1_ge_task_cost:
task_rbf 1 ≥ task_cost tsk.
Proof.
have ALT: ∀ n, n = 0 ∨ n > 0.
{ by clear; intros n; destruct n; [left | right]. }
specialize (ALT (task_cost tsk)); destruct ALT as [Z | POS]; first by rewrite Z.
rewrite leqNgt; apply/negP; intros CONTR.
move: H_proper_arrival_curve ⇒ [ARRB _].
specialize (ARRB (job_arrival j) (job_arrival j + 1)).
feed ARRB; first by rewrite leq_addr.
rewrite addKn in ARRB.
move: CONTR; rewrite /task_rbf /task_request_bound_function; move ⇒ CONTR.
move: CONTR; rewrite -{2}[task_cost tsk]muln1 ltn_mul2l; move ⇒ /andP [_ CONTR].
move: CONTR; rewrite -addn1 -{3}[1]add0n leq_add2r leqn0; move ⇒ /eqP CONTR.
move: ARRB; rewrite CONTR leqn0 eqn0Ngt; move ⇒ /negP T; apply: T.
rewrite /num_arrivals_of_task -has_predT.
rewrite /arrivals_of_task_between /is_job_of_task.
apply/hasP; ∃ j; last by done.
rewrite /jobs_arrived_between addn1 big_nat_recl; last by done.
rewrite big_geq ?cats0; last by done.
rewrite mem_filter.
apply/andP; split.
- by apply/eqP.
- move: H_j_arrives ⇒ [t ARR].
move: (ARR) ⇒ CONS.
apply H_arrival_times_are_consistent in CONS.
by rewrite CONS.
Qed.
End RBFProperties.
End RBF.