Library rt.model.arrival.basic.task_arrival
Require Import rt.util.all.
Require Import rt.model.arrival.basic.arrival_sequence rt.model.arrival.basic.task rt.model.arrival.basic.job.
(* Properties of job arrival. *)
Module TaskArrival.
Import ArrivalSequence SporadicTaskset.
Section ArrivalModels.
Context {Task: eqType}.
Variable task_period: Task → time.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any job arrival sequence. *)
Variable arr_seq: arrival_sequence Job.
(* Then, we define the sporadic task model as follows.*)
Definition sporadic_task_model :=
∀ (j j': Job),
j ≠ j' → (* Given two different jobs j and j' ... *)
arrives_in arr_seq j → (* ...that belong to the arrival sequence... *)
arrives_in arr_seq j' →
job_task j = job_task j' → (* ... and that are spawned by the same task, ... *)
job_arrival j ≤ job_arrival j' → (* ... if the arrival of j precedes the arrival of j' ..., *)
(* then the arrival of j and the arrival of j' are separated by at least one period. *)
job_arrival j' ≥ job_arrival j + task_period (job_task j).
End ArrivalModels.
Section NumberOfArrivals.
Context {Task: eqType}.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any job arrival sequence ...*)
Variable arr_seq: arrival_sequence Job.
(* ...and recall the list of jobs that arrive in any interval. *)
Let arrivals_between := jobs_arrived_between arr_seq.
(* Let tsk be any task. *)
Variable tsk: Task.
(* By checking the task that spawns each job, ...*)
Definition is_job_of_task (j: Job) := job_task j == tsk.
(* ...we can identify the jobs of tsk that arrived in any interval [t1, t2) ... *)
Definition arrivals_of_task_between (t1 t2: time) :=
[seq j <- arrivals_between t1 t2 | is_job_of_task j].
(* ...and also count the number of job arrivals. *)
Definition num_arrivals_of_task (t1 t2: time) :=
size (arrivals_of_task_between t1 t2).
End NumberOfArrivals.
(* In this section, we prove some basic results regarding the
distance between sporadically released jobs. *)
Section DistanceBetweenSporadicJobs.
Context {Task: eqType}.
Variable task_period: Task → time.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any arrival sequence with consistent, duplicate-free arrivals, ... *)
Variable arr_seq: arrival_sequence Job.
Hypothesis H_consistent_arrivals: arrival_times_are_consistent job_arrival arr_seq.
Hypothesis H_no_duplicate_arrivals: arrival_sequence_is_a_set arr_seq.
(* ...where jobs follow the sporadic task model. *)
Hypothesis H_sporadic_jobs:
sporadic_task_model task_period job_arrival job_task arr_seq.
(* Let tsk be any task to be scheduled. *)
Variable tsk: Task.
(* Consider any time interval [t1, t2). *)
Variable t1 t2: time.
(* Recall the jobs of tsk during [t1, t2), along with the number of arrivals. *)
Let arriving_jobs := arrivals_of_task_between job_task arr_seq tsk t1 t2.
Let num_arrivals := num_arrivals_of_task job_task arr_seq tsk t1 t2.
(* Consider the sequence of jobs ordered by arrival times. *)
Let by_arrival_time (j j': Job) := job_arrival j ≤ job_arrival j'.
Let sorted_jobs := sort by_arrival_time arriving_jobs.
(* Let (nth_job i) denote the i-th job in the sorted sequence. *)
Variable elem: Job.
Let nth_job := nth elem sorted_jobs.
(* First, we recall some trivial properties about nth_job. *)
Remark sorted_arrivals_properties_of_nth:
∀ idx,
idx < num_arrivals →
t1 ≤ job_arrival (nth_job idx) < t2 ∧
job_task (nth_job idx) = tsk ∧
arrives_in arr_seq (nth_job idx).
(* Next, we conclude that consecutive jobs are different. *)
Lemma sorted_arrivals_current_differs_from_next:
∀ idx,
idx < num_arrivals.-1 →
nth_job idx ≠ nth_job idx.+1.
(* Since the list is sorted, we prove that each job arrives at
least (task_period tsk) time units after the previous job. *)
Lemma sorted_arrivals_separated_by_period:
∀ idx,
idx < num_arrivals.-1 →
job_arrival (nth_job idx.+1) ≥ job_arrival (nth_job idx) + task_period tsk.
(* If the list of arrivals is not empty, we analyze the distance between
the first and last jobs. *)
Section FirstAndLastJobs.
(* Suppose that there is at least one job, ... *)
Hypothesis H_at_least_one_job:
num_arrivals ≥ 1.
(* ...in which case we identify the first and last jobs and their
respective arrival times (note that they could be the same job). *)
Let j_first := nth_job 0.
Let j_last := nth_job (num_arrivals.-1).
Let a_first := job_arrival j_first.
Let a_last := job_arrival j_last.
(* By induction, we prove that that each job with index 'idx' arrives at
least idx*(task_period tsk) units after the first job. *)
Lemma sorted_arrivals_distance_from_first_job:
∀ idx,
idx < num_arrivals →
job_arrival (nth_job idx) ≥ a_first + idx × task_period tsk.
(* Therefore, the first and last jobs are separated by at least
(num_arrivals - 1) periods. *)
Corollary sorted_arrivals_distance_between_first_and_last:
a_last ≥ a_first + (num_arrivals-1) × task_period tsk.
End FirstAndLastJobs.
End DistanceBetweenSporadicJobs.
End TaskArrival.
Require Import rt.model.arrival.basic.arrival_sequence rt.model.arrival.basic.task rt.model.arrival.basic.job.
(* Properties of job arrival. *)
Module TaskArrival.
Import ArrivalSequence SporadicTaskset.
Section ArrivalModels.
Context {Task: eqType}.
Variable task_period: Task → time.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any job arrival sequence. *)
Variable arr_seq: arrival_sequence Job.
(* Then, we define the sporadic task model as follows.*)
Definition sporadic_task_model :=
∀ (j j': Job),
j ≠ j' → (* Given two different jobs j and j' ... *)
arrives_in arr_seq j → (* ...that belong to the arrival sequence... *)
arrives_in arr_seq j' →
job_task j = job_task j' → (* ... and that are spawned by the same task, ... *)
job_arrival j ≤ job_arrival j' → (* ... if the arrival of j precedes the arrival of j' ..., *)
(* then the arrival of j and the arrival of j' are separated by at least one period. *)
job_arrival j' ≥ job_arrival j + task_period (job_task j).
End ArrivalModels.
Section NumberOfArrivals.
Context {Task: eqType}.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any job arrival sequence ...*)
Variable arr_seq: arrival_sequence Job.
(* ...and recall the list of jobs that arrive in any interval. *)
Let arrivals_between := jobs_arrived_between arr_seq.
(* Let tsk be any task. *)
Variable tsk: Task.
(* By checking the task that spawns each job, ...*)
Definition is_job_of_task (j: Job) := job_task j == tsk.
(* ...we can identify the jobs of tsk that arrived in any interval [t1, t2) ... *)
Definition arrivals_of_task_between (t1 t2: time) :=
[seq j <- arrivals_between t1 t2 | is_job_of_task j].
(* ...and also count the number of job arrivals. *)
Definition num_arrivals_of_task (t1 t2: time) :=
size (arrivals_of_task_between t1 t2).
End NumberOfArrivals.
(* In this section, we prove some basic results regarding the
distance between sporadically released jobs. *)
Section DistanceBetweenSporadicJobs.
Context {Task: eqType}.
Variable task_period: Task → time.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_task: Job → Task.
(* Consider any arrival sequence with consistent, duplicate-free arrivals, ... *)
Variable arr_seq: arrival_sequence Job.
Hypothesis H_consistent_arrivals: arrival_times_are_consistent job_arrival arr_seq.
Hypothesis H_no_duplicate_arrivals: arrival_sequence_is_a_set arr_seq.
(* ...where jobs follow the sporadic task model. *)
Hypothesis H_sporadic_jobs:
sporadic_task_model task_period job_arrival job_task arr_seq.
(* Let tsk be any task to be scheduled. *)
Variable tsk: Task.
(* Consider any time interval [t1, t2). *)
Variable t1 t2: time.
(* Recall the jobs of tsk during [t1, t2), along with the number of arrivals. *)
Let arriving_jobs := arrivals_of_task_between job_task arr_seq tsk t1 t2.
Let num_arrivals := num_arrivals_of_task job_task arr_seq tsk t1 t2.
(* Consider the sequence of jobs ordered by arrival times. *)
Let by_arrival_time (j j': Job) := job_arrival j ≤ job_arrival j'.
Let sorted_jobs := sort by_arrival_time arriving_jobs.
(* Let (nth_job i) denote the i-th job in the sorted sequence. *)
Variable elem: Job.
Let nth_job := nth elem sorted_jobs.
(* First, we recall some trivial properties about nth_job. *)
Remark sorted_arrivals_properties_of_nth:
∀ idx,
idx < num_arrivals →
t1 ≤ job_arrival (nth_job idx) < t2 ∧
job_task (nth_job idx) = tsk ∧
arrives_in arr_seq (nth_job idx).
(* Next, we conclude that consecutive jobs are different. *)
Lemma sorted_arrivals_current_differs_from_next:
∀ idx,
idx < num_arrivals.-1 →
nth_job idx ≠ nth_job idx.+1.
(* Since the list is sorted, we prove that each job arrives at
least (task_period tsk) time units after the previous job. *)
Lemma sorted_arrivals_separated_by_period:
∀ idx,
idx < num_arrivals.-1 →
job_arrival (nth_job idx.+1) ≥ job_arrival (nth_job idx) + task_period tsk.
(* If the list of arrivals is not empty, we analyze the distance between
the first and last jobs. *)
Section FirstAndLastJobs.
(* Suppose that there is at least one job, ... *)
Hypothesis H_at_least_one_job:
num_arrivals ≥ 1.
(* ...in which case we identify the first and last jobs and their
respective arrival times (note that they could be the same job). *)
Let j_first := nth_job 0.
Let j_last := nth_job (num_arrivals.-1).
Let a_first := job_arrival j_first.
Let a_last := job_arrival j_last.
(* By induction, we prove that that each job with index 'idx' arrives at
least idx*(task_period tsk) units after the first job. *)
Lemma sorted_arrivals_distance_from_first_job:
∀ idx,
idx < num_arrivals →
job_arrival (nth_job idx) ≥ a_first + idx × task_period tsk.
(* Therefore, the first and last jobs are separated by at least
(num_arrivals - 1) periods. *)
Corollary sorted_arrivals_distance_between_first_and_last:
a_last ≥ a_first + (num_arrivals-1) × task_period tsk.
End FirstAndLastJobs.
End DistanceBetweenSporadicJobs.
End TaskArrival.