# Library prosa.analysis.facts.sporadic.arrival_bound

Require Import prosa.util.all.

Require Export prosa.model.task.arrival.sporadic.

Require Export prosa.analysis.facts.model.task_arrivals.

Require Export prosa.model.task.arrival.sporadic.

Require Export prosa.analysis.facts.model.task_arrivals.

# Sporadic Arrival Bound

Consider any sporadic tasks ...

... and their jobs.

We define the classic "ceiling of the interval length divided by
minimum inter-arrival time", which we prove to be correct in the
following.

Definition max_sporadic_arrivals (tsk : Task) (delta : duration) :=

div_ceil delta (task_min_inter_arrival_time tsk).

div_ceil delta (task_min_inter_arrival_time tsk).

To establish the bound's soundness, consider any well-formed
arrival sequence, ...

Variable arr_seq : arrival_sequence Job.

Hypothesis H_valid_arrival_sequence : valid_arrival_sequence arr_seq.

Hypothesis H_valid_arrival_sequence : valid_arrival_sequence arr_seq.

... and any valid sporadic task tsk to be analyzed.

Variable tsk : Task.

Hypothesis H_sporadic_model: respects_sporadic_task_model arr_seq tsk.

Hypothesis H_valid_inter_min_arrival: valid_task_min_inter_arrival_time tsk.

Hypothesis H_sporadic_model: respects_sporadic_task_model arr_seq tsk.

Hypothesis H_valid_inter_min_arrival: valid_task_min_inter_arrival_time tsk.

Before we can establish the bound, we require two auxiliary
bounds, which we derive next. First, we consider minimum offset
of the

`n-th`

job of the task that arrives in a given interval.
For technical reasons, we require a "dummy" job in scope to
use the nth function. In the proofs, we establish that the
dummy job is never used, i.e., it is an irrelevant artifact
induced by the ssreflect API. It may be safely ignored.

We observe that the

`i-th`

job to arrive in an interval `[t1,t2)`

arrives no earlier than (task_min_inter_arrival_time tsk) ×i
time units after the beginning of the interval due the minimum
inter-arrival time of the sporadic task.
Lemma arrival_of_nth_job :

∀ t1 t2 n i j,

n = number_of_task_arrivals arr_seq tsk t1 t2 →

i < n →

j = nth dummy (task_arrivals_between arr_seq tsk t1 t2) i →

job_arrival j ≥ t1 + (task_min_inter_arrival_time tsk) × i.

End NthJob.

∀ t1 t2 n i j,

n = number_of_task_arrivals arr_seq tsk t1 t2 →

i < n →

j = nth dummy (task_arrivals_between arr_seq tsk t1 t2) i →

job_arrival j ≥ t1 + (task_min_inter_arrival_time tsk) × i.

End NthJob.

As a second auxiliary lemma, we establish a minimum length on
the interval for a given number of arrivals by applying the
previous lemma to the last job in the interval. We consider only
the case of "many" jobs, i.e., n ≥ 2, which ensures that the
interval

`[t1, t2)`

spans at least one inter-arrival time.
Lemma minimum_distance_for_n_sporadic_arrivals:

∀ t1 t2 n,

number_of_task_arrivals arr_seq tsk t1 t2 = n →

n ≥ 2 →

t2 > t1 + (task_min_inter_arrival_time tsk) × n.-1.

∀ t1 t2 n,

number_of_task_arrivals arr_seq tsk t1 t2 = n →

n ≥ 2 →

t2 > t1 + (task_min_inter_arrival_time tsk) × n.-1.

Based on the above lemma, it is easy to see that
max_sporadic_arrivals is indeed a correct upper bound on the
maximum number of arrivals in a given interval.

Theorem sporadic_task_arrivals_bound:

∀ t1 t2,

number_of_task_arrivals arr_seq tsk t1 t2 ≤ max_sporadic_arrivals tsk (t2 - t1).

End SporadicArrivalBound.

∀ t1 t2,

number_of_task_arrivals arr_seq tsk t1 t2 ≤ max_sporadic_arrivals tsk (t2 - t1).

End SporadicArrivalBound.