Library prosa.classic.analysis.global.basic.bertogna_fp_comp
Require Import prosa.classic.util.all.
Require Import prosa.classic.analysis.global.basic.bertogna_fp_theory.
From mathcomp Require Import ssreflect ssrbool ssrfun eqtype ssrnat seq fintype bigop div path.
Module ResponseTimeIterationFP.
Import ResponseTimeAnalysisFP.
(* In this section, we define the algorithm of Bertogna and Cirinei's
response-time analysis for FP scheduling. *)
Section Analysis.
Context {sporadic_task: eqType}.
Variable task_cost: sporadic_task → time.
Variable task_period: sporadic_task → time.
Variable task_deadline: sporadic_task → time.
(* As input for each iteration of the algorithm, we consider pairs
of tasks and computed response-time bounds. *)
Let task_with_response_time := (sporadic_task × time)%type.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_cost: Job → time.
Variable job_deadline: Job → time.
Variable job_task: Job → sporadic_task.
(* Consider a platform with num_cpus processors, ... *)
Variable num_cpus: nat.
(* ..., and priorities based on an FP policy. *)
Variable higher_priority: FP_policy sporadic_task.
(* Next we define the fixed-point iteration for computing
Bertogna's response-time bound of a task set. *)
(* First, given a sequence of pairs R_prev = <..., (tsk_hp, R_hp)> of
response-time bounds for the higher-priority tasks, we define an
iteration that computes the response-time bound of the current task:
R_tsk (0) = task_cost tsk
R_tsk (step + 1) = f (R step),
where f is the response-time recurrence, step is the number of iterations,
and R_tsk (0) is the initial state. *)
Definition per_task_rta (tsk: sporadic_task)
(R_prev: seq task_with_response_time) (step: nat) :=
iter step
(fun t ⇒ task_cost tsk +
div_floor
(total_interference_bound_fp task_cost task_period tsk
R_prev t)
num_cpus)
(task_cost tsk).
(* To ensure that the iteration converges, we will apply per_task_rta
a "sufficient" number of times: task_deadline tsk - task_cost tsk + 1.
This corresponds to the time complexity of the iteration. *)
Definition max_steps (tsk: sporadic_task) := task_deadline tsk - task_cost tsk + 1.
(* Next we compute the response-time bounds for the entire task set.
Since high-priority tasks may not be schedulable, we allow the
computation to fail.
Thus, given the response-time bound of previous tasks, we either
(a) append the computed response-time bound (tsk, R) of the current task
to the list of pairs, or,
(b) return None if the response-time analysis failed. *)
Definition fp_bound_of_task hp_pairs tsk :=
if hp_pairs is Some rt_bounds then
let R := per_task_rta tsk rt_bounds (max_steps tsk) in
if R ≤ task_deadline tsk then
Some (rcons rt_bounds (tsk, R))
else None
else None.
(* The response-time analysis for a given task set is defined
as a left-fold (reduce) based on the function above.
This either returns a list of task and response-time bounds, or None. *)
Definition fp_claimed_bounds (ts: seq sporadic_task) :=
foldl fp_bound_of_task (Some [::]) ts.
(* The schedulability test simply checks if we got a list of
response-time bounds (i.e., if the computation did not fail). *)
Definition fp_schedulable (ts: seq sporadic_task) :=
fp_claimed_bounds ts != None.
(* In the following section, we prove several helper lemmas about the
list of response-time bounds. The results seem trivial, but must be proven
nonetheless since the list of response-time bounds is computed with
a specific algorithm and there are no lemmas in the library for that. *)
Section SimpleLemmas.
(* First, we show that the first component of the computed list is the set of tasks. *)
Lemma fp_claimed_bounds_unzip :
∀ ts hp_bounds,
fp_claimed_bounds ts = Some hp_bounds →
unzip1 hp_bounds = ts.
(* Next, we show that some properties of the analysis are preserved for the
prefixes of the list: (a) the tasks do not change, (b) R <= deadline,
(c) R is computed using the response-time equation, ... *)
Lemma fp_claimed_bounds_rcons :
∀ ts' hp_bounds tsk1 tsk2 R,
(fp_claimed_bounds (rcons ts' tsk1) = Some (rcons hp_bounds (tsk2, R)) →
(fp_claimed_bounds ts' = Some hp_bounds ∧
tsk1 = tsk2 ∧
R = per_task_rta tsk1 hp_bounds (max_steps tsk1) ∧
R ≤ task_deadline tsk1)).
(* ..., which implies that any prefix of the computation is the computation
of the prefix. *)
Lemma fp_claimed_bounds_take :
∀ ts hp_bounds i,
fp_claimed_bounds ts = Some hp_bounds →
i ≤ size hp_bounds →
fp_claimed_bounds (take i ts) = Some (take i hp_bounds).
(* If the analysis suceeds, the computed response-time bounds are no larger
than the deadlines ... *)
Lemma fp_claimed_bounds_le_deadline :
∀ ts' rt_bounds tsk R,
fp_claimed_bounds ts' = Some rt_bounds →
(tsk, R) \in rt_bounds →
R ≤ task_deadline tsk.
(* ... and no smaller than the task costs. *)
Lemma fp_claimed_bounds_ge_cost :
∀ ts' rt_bounds tsk R,
fp_claimed_bounds ts' = Some rt_bounds →
(tsk, R) \in rt_bounds →
R ≥ task_cost tsk.
(* Short lemma about unfolding the iteration one step. *)
Lemma per_task_rta_fold :
∀ tsk rt_bounds,
task_cost tsk +
div_floor (total_interference_bound_fp task_cost task_period tsk rt_bounds
(per_task_rta tsk rt_bounds (max_steps tsk))) num_cpus
= per_task_rta tsk rt_bounds (max_steps tsk).+1.
End SimpleLemmas.
(* In this section, we prove that if the task set is sorted by priority,
the tasks in fp_claimed_bounds are interfering tasks. *)
Section HighPriorityTasks.
(* Consider a list of previous tasks and a task tsk to be analyzed. *)
Variable ts: taskset_of sporadic_task.
(* Assume that the task set is sorted by unique priorities, ... *)
Hypothesis H_task_set_is_sorted: sorted higher_priority ts.
Hypothesis H_task_set_has_unique_priorities:
FP_is_antisymmetric_over_task_set higher_priority ts.
(* ...the priority order is transitive, ...*)
Hypothesis H_priority_transitive: FP_is_transitive higher_priority.
(* ... and that the response-time analysis succeeds. *)
Variable hp_bounds: seq task_with_response_time.
Variable R: time.
Hypothesis H_analysis_succeeds: fp_claimed_bounds ts = Some hp_bounds.
(* Let's refer to tasks by index. *)
Variable elem: sporadic_task.
Let TASK := nth elem ts.
(* We prove that higher-priority tasks have smaller index. *)
Lemma fp_claimed_bounds_hp_tasks_have_smaller_index :
∀ hp_idx idx,
hp_idx < size ts →
idx < size ts →
hp_idx != idx →
higher_priority (TASK hp_idx) (TASK idx) →
hp_idx < idx.
End HighPriorityTasks.
(* In this section, we show that the fixed-point iteration converges. *)
Section Convergence.
(* Consider any list of higher-priority tasks. *)
Variable ts_hp: list sporadic_task.
(* Assume that the response-time analysis succeeds for the higher-priority tasks. *)
Variable rt_bounds: seq task_with_response_time.
Hypothesis H_test_succeeds: fp_claimed_bounds ts_hp = Some rt_bounds.
(* Consider any task tsk to be analyzed, ... *)
Variable tsk: sporadic_task.
(* ... and assume all tasks have valid parameters. *)
Hypothesis H_valid_task_parameters:
valid_sporadic_taskset task_cost task_period task_deadline (rcons ts_hp tsk).
(* To simplify, let f denote the fixed-point iteration. *)
Let f := per_task_rta tsk rt_bounds.
(* Assume that f (max_steps tsk) is no larger than the deadline. *)
Hypothesis H_no_larger_than_deadline: f (max_steps tsk) ≤ task_deadline tsk.
(* First, we show that f is monotonically increasing. *)
Lemma bertogna_fp_comp_f_monotonic :
∀ x1 x2, x1 ≤ x2 → f x1 ≤ f x2.
(* If the iteration converged at an earlier step, it remains as a fixed point. *)
Lemma bertogna_fp_comp_f_converges_early :
(∃ k, k ≤ max_steps tsk ∧ f k = f k.+1) →
f (max_steps tsk) = f (max_steps tsk).+1.
(* Else, we derive a contradiction. *)
Section DerivingContradiction.
(* Assume instead that the iteration continued to diverge. *)
Hypothesis H_keeps_diverging:
∀ k,
k ≤ max_steps tsk → f k != f k.+1.
(* By monotonicity, it follows that the value always increases. *)
Lemma bertogna_fp_comp_f_increases :
∀ k,
k ≤ max_steps tsk →
f k < f k.+1.
(* In the end, the response-time bound must exceed the deadline. Contradiction! *)
Lemma bertogna_fp_comp_rt_grows_too_much :
∀ k,
k ≤ max_steps tsk →
f k > k + task_cost tsk - 1.
End DerivingContradiction.
(* Using the lemmas above, we prove the convergence of the iteration after max_steps. *)
Lemma per_task_rta_converges:
f (max_steps tsk) = f (max_steps tsk).+1.
End Convergence.
Section MainProof.
(* Consider a task set ts. *)
Variable ts: taskset_of sporadic_task.
(* Assume that all tasks have valid parameters, ... *)
Hypothesis H_valid_task_parameters:
valid_sporadic_taskset task_cost task_period task_deadline ts.
(* ...constrained deadlines.*)
Hypothesis H_constrained_deadlines:
∀ tsk, tsk \in ts → task_deadline tsk ≤ task_period tsk.
(* Assume that the task set is totally ordered by unique priorities,
and that the priority order is transitive. *)
Hypothesis H_task_set_is_sorted: sorted higher_priority ts.
Hypothesis H_task_set_has_unique_priorities:
FP_is_antisymmetric_over_task_set higher_priority ts.
Hypothesis H_priority_is_total:
FP_is_total_over_task_set higher_priority ts.
Hypothesis H_priority_transitive: FP_is_transitive higher_priority.
(* Next, consider any arrival sequence such that...*)
Context {arr_seq: arrival_sequence Job}.
(* ...all jobs come from task set ts, ...*)
Hypothesis H_all_jobs_from_taskset:
∀ j, arrives_in arr_seq j → job_task j \in ts.
(* ...jobs have valid parameters,...*)
Hypothesis H_valid_job_parameters:
∀ j,
arrives_in arr_seq j →
valid_sporadic_job task_cost task_deadline job_cost job_deadline job_task j.
(* ... and satisfy the sporadic task model.*)
Hypothesis H_sporadic_tasks:
sporadic_task_model task_period job_arrival job_task arr_seq.
(* Then, consider any schedule of this arrival sequence such that... *)
Variable sched: schedule Job num_cpus.
Hypothesis H_at_least_one_cpu: num_cpus > 0.
Hypothesis H_jobs_come_from_arrival_sequence:
jobs_come_from_arrival_sequence sched arr_seq.
(* ...jobs only execute after they arrived and no longer
than their execution costs. *)
Hypothesis H_jobs_must_arrive_to_execute:
jobs_must_arrive_to_execute job_arrival sched.
Hypothesis H_completed_jobs_dont_execute:
completed_jobs_dont_execute job_cost sched.
(* Also assume that jobs are sequential (as required by the workload bound). *)
Hypothesis H_sequential_jobs: sequential_jobs sched.
(* Assume that the scheduler is work-conserving and respects the FP policy. *)
Hypothesis H_work_conserving: work_conserving job_arrival job_cost arr_seq sched.
Hypothesis H_respects_FP_policy:
respects_FP_policy job_arrival job_cost job_task arr_seq sched higher_priority.
Let no_deadline_missed_by_task (tsk: sporadic_task) :=
task_misses_no_deadline job_arrival job_cost job_deadline job_task arr_seq sched tsk.
Let no_deadline_missed_by_job :=
job_misses_no_deadline job_arrival job_cost job_deadline sched.
Let response_time_bounded_by (tsk: sporadic_task) :=
is_response_time_bound_of_task job_arrival job_cost job_task arr_seq sched tsk.
(* In the following theorem, we prove that any response-time bound contained
in fp_claimed_bounds is safe. The proof follows by induction on the task set:
Induction hypothesis: assume all higher-priority tasks have safe response-time bounds.
Inductive step: we prove that the response-time bound of the current task is safe.
Note that the inductive step is a direct application of the main Theorem from
bertogna_fp_theory.v. *)
Theorem fp_analysis_yields_response_time_bounds :
∀ tsk R,
(tsk, R) \In fp_claimed_bounds ts →
response_time_bounded_by tsk R.
(* Therefore, if the schedulability test suceeds, ...*)
Hypothesis H_test_succeeds: fp_schedulable ts.
(*..., no task misses its deadline. *)
Theorem taskset_schedulable_by_fp_rta :
∀ tsk, tsk \in ts → no_deadline_missed_by_task tsk.
(* For completeness, since all jobs of the arrival sequence
are spawned by the task set, we also conclude that no job in
the schedule misses its deadline. *)
Theorem jobs_schedulable_by_fp_rta :
∀ j, arrives_in arr_seq j → no_deadline_missed_by_job j.
End MainProof.
End Analysis.
End ResponseTimeIterationFP.
Require Import prosa.classic.analysis.global.basic.bertogna_fp_theory.
From mathcomp Require Import ssreflect ssrbool ssrfun eqtype ssrnat seq fintype bigop div path.
Module ResponseTimeIterationFP.
Import ResponseTimeAnalysisFP.
(* In this section, we define the algorithm of Bertogna and Cirinei's
response-time analysis for FP scheduling. *)
Section Analysis.
Context {sporadic_task: eqType}.
Variable task_cost: sporadic_task → time.
Variable task_period: sporadic_task → time.
Variable task_deadline: sporadic_task → time.
(* As input for each iteration of the algorithm, we consider pairs
of tasks and computed response-time bounds. *)
Let task_with_response_time := (sporadic_task × time)%type.
Context {Job: eqType}.
Variable job_arrival: Job → time.
Variable job_cost: Job → time.
Variable job_deadline: Job → time.
Variable job_task: Job → sporadic_task.
(* Consider a platform with num_cpus processors, ... *)
Variable num_cpus: nat.
(* ..., and priorities based on an FP policy. *)
Variable higher_priority: FP_policy sporadic_task.
(* Next we define the fixed-point iteration for computing
Bertogna's response-time bound of a task set. *)
(* First, given a sequence of pairs R_prev = <..., (tsk_hp, R_hp)> of
response-time bounds for the higher-priority tasks, we define an
iteration that computes the response-time bound of the current task:
R_tsk (0) = task_cost tsk
R_tsk (step + 1) = f (R step),
where f is the response-time recurrence, step is the number of iterations,
and R_tsk (0) is the initial state. *)
Definition per_task_rta (tsk: sporadic_task)
(R_prev: seq task_with_response_time) (step: nat) :=
iter step
(fun t ⇒ task_cost tsk +
div_floor
(total_interference_bound_fp task_cost task_period tsk
R_prev t)
num_cpus)
(task_cost tsk).
(* To ensure that the iteration converges, we will apply per_task_rta
a "sufficient" number of times: task_deadline tsk - task_cost tsk + 1.
This corresponds to the time complexity of the iteration. *)
Definition max_steps (tsk: sporadic_task) := task_deadline tsk - task_cost tsk + 1.
(* Next we compute the response-time bounds for the entire task set.
Since high-priority tasks may not be schedulable, we allow the
computation to fail.
Thus, given the response-time bound of previous tasks, we either
(a) append the computed response-time bound (tsk, R) of the current task
to the list of pairs, or,
(b) return None if the response-time analysis failed. *)
Definition fp_bound_of_task hp_pairs tsk :=
if hp_pairs is Some rt_bounds then
let R := per_task_rta tsk rt_bounds (max_steps tsk) in
if R ≤ task_deadline tsk then
Some (rcons rt_bounds (tsk, R))
else None
else None.
(* The response-time analysis for a given task set is defined
as a left-fold (reduce) based on the function above.
This either returns a list of task and response-time bounds, or None. *)
Definition fp_claimed_bounds (ts: seq sporadic_task) :=
foldl fp_bound_of_task (Some [::]) ts.
(* The schedulability test simply checks if we got a list of
response-time bounds (i.e., if the computation did not fail). *)
Definition fp_schedulable (ts: seq sporadic_task) :=
fp_claimed_bounds ts != None.
(* In the following section, we prove several helper lemmas about the
list of response-time bounds. The results seem trivial, but must be proven
nonetheless since the list of response-time bounds is computed with
a specific algorithm and there are no lemmas in the library for that. *)
Section SimpleLemmas.
(* First, we show that the first component of the computed list is the set of tasks. *)
Lemma fp_claimed_bounds_unzip :
∀ ts hp_bounds,
fp_claimed_bounds ts = Some hp_bounds →
unzip1 hp_bounds = ts.
(* Next, we show that some properties of the analysis are preserved for the
prefixes of the list: (a) the tasks do not change, (b) R <= deadline,
(c) R is computed using the response-time equation, ... *)
Lemma fp_claimed_bounds_rcons :
∀ ts' hp_bounds tsk1 tsk2 R,
(fp_claimed_bounds (rcons ts' tsk1) = Some (rcons hp_bounds (tsk2, R)) →
(fp_claimed_bounds ts' = Some hp_bounds ∧
tsk1 = tsk2 ∧
R = per_task_rta tsk1 hp_bounds (max_steps tsk1) ∧
R ≤ task_deadline tsk1)).
(* ..., which implies that any prefix of the computation is the computation
of the prefix. *)
Lemma fp_claimed_bounds_take :
∀ ts hp_bounds i,
fp_claimed_bounds ts = Some hp_bounds →
i ≤ size hp_bounds →
fp_claimed_bounds (take i ts) = Some (take i hp_bounds).
(* If the analysis suceeds, the computed response-time bounds are no larger
than the deadlines ... *)
Lemma fp_claimed_bounds_le_deadline :
∀ ts' rt_bounds tsk R,
fp_claimed_bounds ts' = Some rt_bounds →
(tsk, R) \in rt_bounds →
R ≤ task_deadline tsk.
(* ... and no smaller than the task costs. *)
Lemma fp_claimed_bounds_ge_cost :
∀ ts' rt_bounds tsk R,
fp_claimed_bounds ts' = Some rt_bounds →
(tsk, R) \in rt_bounds →
R ≥ task_cost tsk.
(* Short lemma about unfolding the iteration one step. *)
Lemma per_task_rta_fold :
∀ tsk rt_bounds,
task_cost tsk +
div_floor (total_interference_bound_fp task_cost task_period tsk rt_bounds
(per_task_rta tsk rt_bounds (max_steps tsk))) num_cpus
= per_task_rta tsk rt_bounds (max_steps tsk).+1.
End SimpleLemmas.
(* In this section, we prove that if the task set is sorted by priority,
the tasks in fp_claimed_bounds are interfering tasks. *)
Section HighPriorityTasks.
(* Consider a list of previous tasks and a task tsk to be analyzed. *)
Variable ts: taskset_of sporadic_task.
(* Assume that the task set is sorted by unique priorities, ... *)
Hypothesis H_task_set_is_sorted: sorted higher_priority ts.
Hypothesis H_task_set_has_unique_priorities:
FP_is_antisymmetric_over_task_set higher_priority ts.
(* ...the priority order is transitive, ...*)
Hypothesis H_priority_transitive: FP_is_transitive higher_priority.
(* ... and that the response-time analysis succeeds. *)
Variable hp_bounds: seq task_with_response_time.
Variable R: time.
Hypothesis H_analysis_succeeds: fp_claimed_bounds ts = Some hp_bounds.
(* Let's refer to tasks by index. *)
Variable elem: sporadic_task.
Let TASK := nth elem ts.
(* We prove that higher-priority tasks have smaller index. *)
Lemma fp_claimed_bounds_hp_tasks_have_smaller_index :
∀ hp_idx idx,
hp_idx < size ts →
idx < size ts →
hp_idx != idx →
higher_priority (TASK hp_idx) (TASK idx) →
hp_idx < idx.
End HighPriorityTasks.
(* In this section, we show that the fixed-point iteration converges. *)
Section Convergence.
(* Consider any list of higher-priority tasks. *)
Variable ts_hp: list sporadic_task.
(* Assume that the response-time analysis succeeds for the higher-priority tasks. *)
Variable rt_bounds: seq task_with_response_time.
Hypothesis H_test_succeeds: fp_claimed_bounds ts_hp = Some rt_bounds.
(* Consider any task tsk to be analyzed, ... *)
Variable tsk: sporadic_task.
(* ... and assume all tasks have valid parameters. *)
Hypothesis H_valid_task_parameters:
valid_sporadic_taskset task_cost task_period task_deadline (rcons ts_hp tsk).
(* To simplify, let f denote the fixed-point iteration. *)
Let f := per_task_rta tsk rt_bounds.
(* Assume that f (max_steps tsk) is no larger than the deadline. *)
Hypothesis H_no_larger_than_deadline: f (max_steps tsk) ≤ task_deadline tsk.
(* First, we show that f is monotonically increasing. *)
Lemma bertogna_fp_comp_f_monotonic :
∀ x1 x2, x1 ≤ x2 → f x1 ≤ f x2.
(* If the iteration converged at an earlier step, it remains as a fixed point. *)
Lemma bertogna_fp_comp_f_converges_early :
(∃ k, k ≤ max_steps tsk ∧ f k = f k.+1) →
f (max_steps tsk) = f (max_steps tsk).+1.
(* Else, we derive a contradiction. *)
Section DerivingContradiction.
(* Assume instead that the iteration continued to diverge. *)
Hypothesis H_keeps_diverging:
∀ k,
k ≤ max_steps tsk → f k != f k.+1.
(* By monotonicity, it follows that the value always increases. *)
Lemma bertogna_fp_comp_f_increases :
∀ k,
k ≤ max_steps tsk →
f k < f k.+1.
(* In the end, the response-time bound must exceed the deadline. Contradiction! *)
Lemma bertogna_fp_comp_rt_grows_too_much :
∀ k,
k ≤ max_steps tsk →
f k > k + task_cost tsk - 1.
End DerivingContradiction.
(* Using the lemmas above, we prove the convergence of the iteration after max_steps. *)
Lemma per_task_rta_converges:
f (max_steps tsk) = f (max_steps tsk).+1.
End Convergence.
Section MainProof.
(* Consider a task set ts. *)
Variable ts: taskset_of sporadic_task.
(* Assume that all tasks have valid parameters, ... *)
Hypothesis H_valid_task_parameters:
valid_sporadic_taskset task_cost task_period task_deadline ts.
(* ...constrained deadlines.*)
Hypothesis H_constrained_deadlines:
∀ tsk, tsk \in ts → task_deadline tsk ≤ task_period tsk.
(* Assume that the task set is totally ordered by unique priorities,
and that the priority order is transitive. *)
Hypothesis H_task_set_is_sorted: sorted higher_priority ts.
Hypothesis H_task_set_has_unique_priorities:
FP_is_antisymmetric_over_task_set higher_priority ts.
Hypothesis H_priority_is_total:
FP_is_total_over_task_set higher_priority ts.
Hypothesis H_priority_transitive: FP_is_transitive higher_priority.
(* Next, consider any arrival sequence such that...*)
Context {arr_seq: arrival_sequence Job}.
(* ...all jobs come from task set ts, ...*)
Hypothesis H_all_jobs_from_taskset:
∀ j, arrives_in arr_seq j → job_task j \in ts.
(* ...jobs have valid parameters,...*)
Hypothesis H_valid_job_parameters:
∀ j,
arrives_in arr_seq j →
valid_sporadic_job task_cost task_deadline job_cost job_deadline job_task j.
(* ... and satisfy the sporadic task model.*)
Hypothesis H_sporadic_tasks:
sporadic_task_model task_period job_arrival job_task arr_seq.
(* Then, consider any schedule of this arrival sequence such that... *)
Variable sched: schedule Job num_cpus.
Hypothesis H_at_least_one_cpu: num_cpus > 0.
Hypothesis H_jobs_come_from_arrival_sequence:
jobs_come_from_arrival_sequence sched arr_seq.
(* ...jobs only execute after they arrived and no longer
than their execution costs. *)
Hypothesis H_jobs_must_arrive_to_execute:
jobs_must_arrive_to_execute job_arrival sched.
Hypothesis H_completed_jobs_dont_execute:
completed_jobs_dont_execute job_cost sched.
(* Also assume that jobs are sequential (as required by the workload bound). *)
Hypothesis H_sequential_jobs: sequential_jobs sched.
(* Assume that the scheduler is work-conserving and respects the FP policy. *)
Hypothesis H_work_conserving: work_conserving job_arrival job_cost arr_seq sched.
Hypothesis H_respects_FP_policy:
respects_FP_policy job_arrival job_cost job_task arr_seq sched higher_priority.
Let no_deadline_missed_by_task (tsk: sporadic_task) :=
task_misses_no_deadline job_arrival job_cost job_deadline job_task arr_seq sched tsk.
Let no_deadline_missed_by_job :=
job_misses_no_deadline job_arrival job_cost job_deadline sched.
Let response_time_bounded_by (tsk: sporadic_task) :=
is_response_time_bound_of_task job_arrival job_cost job_task arr_seq sched tsk.
(* In the following theorem, we prove that any response-time bound contained
in fp_claimed_bounds is safe. The proof follows by induction on the task set:
Induction hypothesis: assume all higher-priority tasks have safe response-time bounds.
Inductive step: we prove that the response-time bound of the current task is safe.
Note that the inductive step is a direct application of the main Theorem from
bertogna_fp_theory.v. *)
Theorem fp_analysis_yields_response_time_bounds :
∀ tsk R,
(tsk, R) \In fp_claimed_bounds ts →
response_time_bounded_by tsk R.
(* Therefore, if the schedulability test suceeds, ...*)
Hypothesis H_test_succeeds: fp_schedulable ts.
(*..., no task misses its deadline. *)
Theorem taskset_schedulable_by_fp_rta :
∀ tsk, tsk \in ts → no_deadline_missed_by_task tsk.
(* For completeness, since all jobs of the arrival sequence
are spawned by the task set, we also conclude that no job in
the schedule misses its deadline. *)
Theorem jobs_schedulable_by_fp_rta :
∀ j, arrives_in arr_seq j → no_deadline_missed_by_job j.
End MainProof.
End Analysis.
End ResponseTimeIterationFP.