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# Library prosa.analysis.facts.model.overheads.sbf.fifo

Require Export prosa.analysis.facts.readiness.basic.
Require Export prosa.analysis.facts.model.overheads.blackout_bound.
Require Export prosa.analysis.facts.model.overheads.schedule_change_bound.
Require Export prosa.analysis.definitions.sbf.busy.
Require Export prosa.model.priority.fifo.
In this section, we define an SBF for the FIFO scheduling policy in the presence of overheads.
Section OverheadResourceModelValidSBF.
Consider any type of tasks,...
Context {Task : TaskType}.
Context `{MaxArrivals Task}.
... an arbitrary task set ts, ...
Variable ts : seq Task.
... and bounds DB, CSB, and CRPDB on dispatch overhead, context-switch overhead, and preemption-related overhead, respectively.
Variable DB CSB CRPDB : duration.
We define the blackout bound for FIFO in an interval of length Δ as the number of jobs that can arrive in Δ, plus one, multiplied by the sum of all overhead bounds.
The "+1" accounts for the fact that n arrivals can result in up to n + 1 segments without a schedule change, and thus up to n + 1 intervals wherein overhead duration is bounded by DB + CSB + CRPDB.
Unlike JLFP and FP, FIFO does not require doubling the arrivals, because all jobs are treated uniformly and there are no preemptions caused by higher-priority jobs.
Definition fifo_blackout_bound (Δ : duration) :=
(DB + CSB + CRPDB) × (1 + \sum_(tsk <- ts) max_arrivals tsk Δ).
First, we define the FIFO SBF as the interval length minus the FIFO blackout bound.
#[local] Instance fifo_ovh_sbf : SupplyBoundFunction :=
{
supply_bound_function Δ := Δ - fifo_blackout_bound Δ
}.
Next, we define the "slowed-down" version of the FIFO SBF as the interval length minus the slowed-down blackout bound. The slowdown ensures that the resulting SBF is monotone and unit-growth, which is necessary to obtain response-time bounds using aRSA. This slowed-down FIFO SBF is used internally in the analysis, while the unmodified FIFO SBF is used to state the top-level analysis result.
Definition fifo_ovh_sbf_slow : SupplyBoundFunction :=
{|
supply_bound_function Δ := Δ - slowed fifo_blackout_bound Δ
|}.
End OverheadResourceModelValidSBF.
In the following section, we show that the SBF defined above is indeed a valid SBF.
Section OverheadResourceModelValidSBF.
Consider any type of tasks ...
Context {Task : TaskType}.
Context `{MaxArrivals Task}.
... and any type of jobs associated with these tasks.
Context {Job : JobType}.
Context `{JobTask Job Task}.
Context `{JobArrival Job}.
Context `{JobCost Job}.
Context `{JobPreemptable Job}.
We assume the classic (i.e., Liu & Layland) model of readiness without jitter or self-suspensions, wherein pending jobs are always ready.
Context {RM : JobReady Job (overheads.processor_state Job)}.
Hypothesis H_basic_readiness : basic_readiness RM.
Consider a FIFO priority policy that indicates a higher-or-equal priority relation.
Context {JLFP : JLFP_policy Job}.
Hypothesis H_FIFO : policy_is_FIFO JLFP.
Consider any valid arrival sequence...
Variable arr_seq : arrival_sequence Job.
Hypothesis H_valid_arrival_sequence : valid_arrival_sequence arr_seq.
... and any explicit-overhead uni-processor schedule without superfluous preemptions of this arrival sequence.
Variable sched : schedule (overheads.processor_state Job).
Hypothesis H_valid_schedule : valid_schedule sched arr_seq.
Hypothesis H_work_conserving : work_conserving arr_seq sched.
Hypothesis H_no_superfluous_preemptions : no_superfluous_preemptions sched.
Assume that the schedule respects the JLFP policy.
Hypothesis H_respects_policy :
respects_JLFP_policy_at_preemption_point arr_seq sched JLFP.
Assume that the preemption model is valid.
Hypothesis H_valid_preemption_model :
valid_preemption_model arr_seq sched.
We consider an arbitrary task set ts ...
Variable ts : seq Task.
... and assume that all jobs stem from tasks in this task set.
Hypothesis H_all_jobs_from_taskset : all_jobs_from_taskset arr_seq ts.
We assume that max_arrivals is a family of valid arrival curves that constrains the arrival sequence arr_seq, i.e., for any task tsk in ts, max_arrival tsk is (1) an arrival bound of tsk, and ...
Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.
... (2) a monotone function that equals 0 for the empty interval delta = 0.
Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.
We assume that all jobs have positive cost. This restriction is not fundamental to the analysis, but rather an artifact of the current proof structure in the library.
Hypothesis H_all_jobs_have_positive_cost :
∀ j,
arrives_in arr_seq j →
job_cost_positive j.
Finally, we assume that the schedule respects a valid overhead resource model with dispatch overhead DB, context-switch overhead CSB, and preemption-related overhead CRPDB.
Variable DB CSB CRPDB : duration.
Hypothesis H_valid_overheads_model :
overhead_resource_model sched DB CSB CRPDB.
We show that the slowed SBF is monotone.
Lemma overheads_sbf_monotone :
sbf_is_monotone (fifo_ovh_sbf_slow ts DB CSB CRPDB).
Proof.
intros x y NEQ.
interval_to_duration x y k.
have EQ: ∀ a b c d, (a + d ≤ b + c) → (a - b ≤ c - d). { clear. intros. lia. }
apply: EQ.
rewrite [in leqRHS]addnC -addnA leq_add2l.
apply unit_growth_function_k_steps_bounded.
by apply slowed_is_unit_step.
Qed.
In addition, we note that fifo_blackout_bound is monotone as well.
Remark fifo_blackout_bound_monotone :
monotone leq (fifo_blackout_bound ts DB CSB CRPDB).
Proof.
unfold fifo_blackout_bound.
have Lem1 : ∀ f c, monotone leq f → monotone leq (fun x ⇒ c × f x).
{ intros f c MON x y LE; specialize (MON x y).
by apply MON in LE; apply leq_mul. }
have Lem2: ∀ f c, monotone leq f → monotone leq (fun x ⇒ c + f x).
{ intros f c MON x y LE; specialize (MON x y).
by apply MON in LE; apply leq_add. }
by apply Lem1, Lem2, sum_leq_mono, H_valid_arrival_curve.
Qed.
The slowed SBF is also a unit-supply SBF.
Lemma overheads_sbf_unit :
unit_supply_bound_function (fifo_ovh_sbf_slow ts DB CSB CRPDB).
Proof.
move⇒ δ; rewrite /fifo_ovh_sbf_slow /supply_bound_function.
have LE:
slowed (fifo_blackout_bound ts DB CSB CRPDB) δ
≤ slowed (fifo_blackout_bound ts DB CSB CRPDB) δ.+1.
{ apply slowed_respects_monotone; last by lia.
move⇒ x y LE; rewrite /fifo_blackout_bound; rewrite leq_mul2l; apply/orP; right.
rewrite leq_add2l big_seq_cond [leqRHS]big_seq_cond.
by apply leq_sum ⇒ tsk /andP [IN _]; apply H_valid_arrival_curve.
}
lia.
Qed.
Lastly, we prove that the slowed SBF is valid.
Lemma overheads_sbf_busy_valid :
∀ tsk,
valid_busy_sbf arr_seq sched tsk (fifo_ovh_sbf_slow ts DB CSB CRPDB).
Proof.
move ⇒ tsk; split; first by unfold fifo_ovh_sbf_slow.
move ⇒ j t1 t2 ARR [TSK PREF] t /andP [NEQ1 NEQ2].
interval_to_duration t1 t δ.
rewrite supply_during_complement; last first.
{ by apply: overheads_proc_model_provides_unit_supply; eauto 1. }
rewrite addKn; apply leq_sub2l.
apply slowed_respects_pointwise_leq with (f := fun δ ⇒ blackout_during sched t1 (t1 + δ)).
{ by move⇒ ?; rewrite addnA; apply blackout_during_unit_growth. }
move⇒ Δ LE; rewrite /fifo_blackout_bound.
apply: leq_trans.
{ by apply: finite_sched_changes_bounded_overheads_blackout ⇒ //. }
rewrite /blackout_during leq_mul2l; apply/orP; right.
have [Z|POSΔ] := posnP Δ.
{ by subst; rewrite addn0 /number_schedule_changes /index_iota subnS subnn //=. }
rewrite [_ + 1]addnC leq_add2l; apply: schedule_changes_bounded_by_total_arrivals_FIFO ⇒ //.
lia.
Qed.
End OverheadResourceModelValidSBF.