Library rt.restructuring.analysis.fixed_priority.rta.nonpr_reg.concrete_models.floating
Require Export rt.restructuring.analysis.fixed_priority.rta.nonpr_reg.response_time_bound.
Require Export rt.restructuring.analysis.basic_facts.preemption.rtc_threshold.floating.
From mathcomp Require Import ssreflect ssrbool eqtype ssrnat seq path fintype bigop.
Require Export rt.restructuring.analysis.basic_facts.preemption.rtc_threshold.floating.
From mathcomp Require Import ssreflect ssrbool eqtype ssrnat seq path fintype bigop.
Throughout this file, we assume ideal uniprocessor schedules.
Throughout this file, we assume the basic (i.e., Liu & Layland) readiness model.
Throughout this file, we assume the task model with floating non-preemptive regions.
Require Import rt.restructuring.model.preemption.limited_preemptive.
Require Import rt.restructuring.model.task.preemption.floating_nonpreemptive.
Require Import rt.restructuring.model.task.preemption.floating_nonpreemptive.
RTA for Model with Floating Non-Preemptive Regions
In this module we prove the RTA theorem for floating non-preemptive regions FP model.
Consider any type of tasks ...
... and any type of jobs associated with these tasks.
Context {Job : JobType}.
Context `{JobTask Job Task}.
Context `{JobArrival Job}.
Context `{JobCost Job}.
Context `{JobTask Job Task}.
Context `{JobArrival Job}.
Context `{JobCost Job}.
Consider any arrival sequence with consistent, non-duplicate arrivals.
Variable arr_seq : arrival_sequence Job.
Hypothesis H_arrival_times_are_consistent : consistent_arrival_times arr_seq.
Hypothesis H_arr_seq_is_a_set : arrival_sequence_uniq arr_seq.
Hypothesis H_arrival_times_are_consistent : consistent_arrival_times arr_seq.
Hypothesis H_arr_seq_is_a_set : arrival_sequence_uniq arr_seq.
Assume we have the model with floating nonpreemptive regions.
I.e., for each task only the length of the maximal nonpreemptive
segment is known and each job level is divided into a number of
nonpreemptive segments by inserting preemption points.
Context `{JobPreemptionPoints Job}
`{TaskMaxNonpreemptiveSegment Task}.
Hypothesis H_valid_task_model_with_floating_nonpreemptive_regions:
valid_model_with_floating_nonpreemptive_regions arr_seq.
`{TaskMaxNonpreemptiveSegment Task}.
Hypothesis H_valid_task_model_with_floating_nonpreemptive_regions:
valid_model_with_floating_nonpreemptive_regions arr_seq.
Consider an arbitrary task set ts, ...
... assume that all jobs come from the task set, ...
... and the cost of a job cannot be larger than the task cost.
Let max_arrivals be a family of valid arrival curves, i.e., for any task tsk in ts
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.
Context `{MaxArrivals Task}.
Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.
Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.
Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.
Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.
Let tsk be any task in ts that is to be analyzed.
Next, consider any ideal uniprocessor schedule with limited preemptions of this arrival sequence ...
Variable sched : schedule (ideal.processor_state Job).
Hypothesis H_jobs_come_from_arrival_sequence:
jobs_come_from_arrival_sequence sched arr_seq.
Hypothesis H_schedule_with_limited_preemptions:
valid_schedule_with_limited_preemptions arr_seq sched.
Hypothesis H_jobs_come_from_arrival_sequence:
jobs_come_from_arrival_sequence sched arr_seq.
Hypothesis H_schedule_with_limited_preemptions:
valid_schedule_with_limited_preemptions arr_seq sched.
... where jobs do not execute before their arrival or after completion.
Hypothesis H_jobs_must_arrive_to_execute : jobs_must_arrive_to_execute sched.
Hypothesis H_completed_jobs_dont_execute : completed_jobs_dont_execute sched.
Hypothesis H_completed_jobs_dont_execute : completed_jobs_dont_execute sched.
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 : reflexive_priorities.
Hypothesis H_priority_is_transitive : transitive_priorities.
Hypothesis H_priority_is_reflexive : reflexive_priorities.
Hypothesis H_priority_is_transitive : transitive_priorities.
Assume we have sequential tasks, i.e, jobs from the
same task execute in the order of their arrival.
Next, we assume that the schedule is a work-conserving schedule...
... and the schedule respects the policy defined by thejob_preemptable
function (i.e., jobs have bounded nonpreemptive segments).
Let's define some local names for clarity.
Let task_rbf := task_request_bound_function tsk.
Let total_hep_rbf := total_hep_request_bound_function_FP _ ts tsk.
Let total_ohep_rbf := total_ohep_request_bound_function_FP _ ts tsk.
Let response_time_bounded_by := task_response_time_bound arr_seq sched.
Let total_hep_rbf := total_hep_request_bound_function_FP _ ts tsk.
Let total_ohep_rbf := total_ohep_request_bound_function_FP _ ts tsk.
Let response_time_bounded_by := task_response_time_bound arr_seq sched.
Next, we define a bound for the priority inversion caused by tasks of lower priority.
Let blocking_bound :=
\max_(tsk_other <- ts | ~~ higher_eq_priority tsk_other tsk)
(task_max_nonpreemptive_segment tsk_other - ε).
\max_(tsk_other <- ts | ~~ higher_eq_priority tsk_other tsk)
(task_max_nonpreemptive_segment tsk_other - ε).
Let L be any positive fixed point of the busy interval recurrence, determined by
the sum of blocking and higher-or-equal-priority workload.
Variable L : duration.
Hypothesis H_L_positive : L > 0.
Hypothesis H_fixed_point : L = blocking_bound + total_hep_rbf L.
Hypothesis H_L_positive : L > 0.
Hypothesis H_fixed_point : L = blocking_bound + total_hep_rbf L.
To reduce the time complexity of the analysis, recall the notion of search space.
Next, consider any value R, and assume that for any given arrival A from search space
there is a solution of the response-time bound recurrence which is bounded by R.
Variable R : duration.
Hypothesis H_R_is_maximum:
∀ (A : duration),
is_in_search_space A →
∃ (F : duration),
A + F = blocking_bound + task_rbf (A + ε) + total_ohep_rbf (A + F) ∧
F ≤ R.
Hypothesis H_R_is_maximum:
∀ (A : duration),
is_in_search_space A →
∃ (F : duration),
A + F = blocking_bound + task_rbf (A + ε) + total_ohep_rbf (A + F) ∧
F ≤ R.
Now, we can reuse the results for the abstract model with bounded nonpreemptive segments
to establish a response-time bound for the more concrete model with floating nonpreemptive regions.
Theorem uniprocessor_response_time_bound_fp_with_floating_nonpreemptive_regions:
response_time_bounded_by tsk R.
Proof.
move: (H_valid_task_model_with_floating_nonpreemptive_regions) ⇒ [LIMJ JMLETM].
move: (LIMJ) ⇒ [BEG [END _]].
eapply uniprocessor_response_time_bound_fp_with_bounded_nonpreemptive_segments.
all: eauto 2 with basic_facts.
intros A SP.
rewrite subnn subn0.
destruct (H_R_is_maximum _ SP) as [F [EQ LE]].
by ∃ F; rewrite addn0; split.
Qed.
End RTAforFloatingModelwithArrivalCurves.
response_time_bounded_by tsk R.
Proof.
move: (H_valid_task_model_with_floating_nonpreemptive_regions) ⇒ [LIMJ JMLETM].
move: (LIMJ) ⇒ [BEG [END _]].
eapply uniprocessor_response_time_bound_fp_with_bounded_nonpreemptive_segments.
all: eauto 2 with basic_facts.
intros A SP.
rewrite subnn subn0.
destruct (H_R_is_maximum _ SP) as [F [EQ LE]].
by ∃ F; rewrite addn0; split.
Qed.
End RTAforFloatingModelwithArrivalCurves.