Library prosa.results.edf.rta.limited_preemptive
Require Export prosa.model.readiness.basic.
Require Export prosa.results.edf.rta.bounded_nps.
Require Export prosa.analysis.facts.preemption.rtc_threshold.limited.
Require Export prosa.analysis.facts.readiness.basic.
Require Export prosa.model.task.preemption.limited_preemptive.
Require Export prosa.model.priority.edf.
Require Export prosa.analysis.definitions.blocking_bound.edf.
Require Export prosa.results.edf.rta.bounded_nps.
Require Export prosa.analysis.facts.preemption.rtc_threshold.limited.
Require Export prosa.analysis.facts.readiness.basic.
Require Export prosa.model.task.preemption.limited_preemptive.
Require Export prosa.model.priority.edf.
Require Export prosa.analysis.definitions.blocking_bound.edf.
RTA for EDF with Fixed Preemption Points
In this module we prove the RTA theorem for EDF-schedulers with fixed preemption points.Setup and Assumptions
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}.
We assume the classic (i.e., Liu & Layland) model of readiness
without jitter or self-suspensions, wherein pending jobs are
always ready.
We assume that jobs are limited-preemptive.
Consider any arrival sequence with consistent, non-duplicate arrivals.
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.
Consider an arbitrary task set ts, ...
... assume that all jobs come from this task set, ...
... and the cost of a job cannot be larger than the task cost.
Next, we assume we have the model with fixed preemption points.
I.e., each task is divided into a number of non-preemptive segments
by inserting statically predefined preemption points.
Context `{JobPreemptionPoints Job}.
Context `{TaskPreemptionPoints Task}.
Hypothesis H_valid_model_with_fixed_preemption_points:
valid_fixed_preemption_points_model arr_seq ts.
Context `{TaskPreemptionPoints Task}.
Hypothesis H_valid_model_with_fixed_preemption_points:
valid_fixed_preemption_points_model arr_seq ts.
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 valid ideal uni-processor schedule with limited
preemptions of this arrival sequence ...
Variable sched : schedule (ideal.processor_state Job).
Hypothesis H_sched_valid: valid_schedule sched arr_seq.
Hypothesis H_schedule_with_limited_preemptions:
schedule_respects_preemption_model arr_seq sched.
Hypothesis H_sched_valid: valid_schedule sched arr_seq.
Hypothesis H_schedule_with_limited_preemptions:
schedule_respects_preemption_model arr_seq sched.
Next, we assume that the schedule is a work-conserving schedule...
... and the schedule respects the scheduling policy.
Total Workload and Length of Busy Interval
Using the sum of individual request bound functions, we define the request bound
function of all tasks (total request bound function).
Let L be any positive fixed point of the busy interval recurrence.
Response-Time Bound
Consider any value R, and assume that for any given arrival
offset A in the 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 ts tsk A
+ (task_rbf (A + ε) - (task_last_nonpr_segment tsk - ε))
+ bound_on_athep_workload ts tsk A (A + F) ∧
R ≥ F + (task_last_nonpr_segment tsk - ε).
Hypothesis H_R_is_maximum:
∀ (A : duration),
is_in_search_space A →
∃ (F : duration),
A + F ≥ blocking_bound ts tsk A
+ (task_rbf (A + ε) - (task_last_nonpr_segment tsk - ε))
+ bound_on_athep_workload ts tsk A (A + F) ∧
R ≥ F + (task_last_nonpr_segment tsk - ε).
Now, we can leverage the results for the abstract model with bounded non-preemptive segments
to establish a response-time bound for the more concrete model of fixed preemption points.
Let response_time_bounded_by := task_response_time_bound arr_seq sched.
Theorem uniprocessor_response_time_bound_edf_with_fixed_preemption_points:
response_time_bounded_by tsk R.
Proof.
move: (H_valid_model_with_fixed_preemption_points) ⇒ [MLP [BEG [END [INCR [HYP1 [HYP2 HYP3]]]]]].
move: (MLP) ⇒ [BEGj [ENDj _]].
case: (posnP (task_cost tsk)) ⇒ [ZERO|POSt].
{ intros j ARR TSK.
move: (H_valid_job_cost _ ARR) ⇒ POSt.
move: TSK ⇒ /eqP TSK; move: POSt; rewrite /valid_job_cost TSK ZERO leqn0; move ⇒ /eqP Z.
by rewrite /job_response_time_bound /completed_by Z.
}
eapply uniprocessor_response_time_bound_edf_with_bounded_nonpreemptive_segments with (L := L) ⇒ //.
rewrite subKn//.
rewrite /task_last_nonpr_segment -(leq_add2r 1) subn1 !addn1 prednK; last first.
- rewrite /last0 -nth_last.
apply HYP3 ⇒ //.
rewrite -(ltn_add2r 1) !addn1 prednK //.
move: (number_of_preemption_points_in_task_at_least_two
_ _ H_valid_model_with_fixed_preemption_points _ H_tsk_in_ts POSt) ⇒ Fact2.
move: (Fact2) ⇒ Fact3.
by rewrite size_of_seq_of_distances // addn1 ltnS // in Fact2.
- apply leq_trans with (task_max_nonpreemptive_segment tsk).
+ by apply last_of_seq_le_max_of_seq.
+ rewrite -END// ltnW// ltnS//.
exact: max_distance_in_seq_le_last_element_of_seq.
Qed.
End RTAforFixedPreemptionPointsModelwithArrivalCurves.