Library prosa.results.edf.rta.bounded_pi
Require Export prosa.analysis.facts.priority.edf.
Require Export prosa.analysis.definitions.schedulability.
Require Import prosa.model.readiness.basic.
Require Import prosa.model.priority.edf.
Require Import prosa.model.task.absolute_deadline.
Require Import prosa.analysis.abstract.ideal.cumulative_bounds.
Require Import prosa.analysis.facts.busy_interval.carry_in.
Require Import prosa.analysis.facts.readiness.basic.
Require Export prosa.analysis.abstract.ideal.abstract_seq_rta.
Require Export prosa.analysis.facts.model.task_cost.
Require Export prosa.analysis.facts.workload.edf_athep_bound.
Require Export prosa.analysis.definitions.schedulability.
Require Import prosa.model.readiness.basic.
Require Import prosa.model.priority.edf.
Require Import prosa.model.task.absolute_deadline.
Require Import prosa.analysis.abstract.ideal.cumulative_bounds.
Require Import prosa.analysis.facts.busy_interval.carry_in.
Require Import prosa.analysis.facts.readiness.basic.
Require Export prosa.analysis.abstract.ideal.abstract_seq_rta.
Require Export prosa.analysis.facts.model.task_cost.
Require Export prosa.analysis.facts.workload.edf_athep_bound.
Abstract RTA for EDF-schedulers with Bounded Priority Inversion
In this module we instantiate the Abstract Response-Time analysis (aRTA) to EDF-schedulers for ideal uni-processor model of real-time tasks with arbitrary arrival models.
Consider any type of tasks ...
Context {Task : TaskType}.
Context `{TaskCost Task}.
Context `{TaskDeadline Task}.
Context `{TaskRunToCompletionThreshold Task}.
Context `{TaskMaxNonpreemptiveSegment Task}.
Context `{TaskCost Task}.
Context `{TaskDeadline Task}.
Context `{TaskRunToCompletionThreshold Task}.
Context `{TaskMaxNonpreemptiveSegment Task}.
... and any type of jobs associated with these tasks.
Context {Job : JobType}.
Context `{JobTask Job Task}.
Context {Arrival : JobArrival Job}.
Context {Cost : JobCost Job}.
Context `{JobPreemptable Job}.
Context `{JobTask Job Task}.
Context {Arrival : JobArrival Job}.
Context {Cost : JobCost Job}.
Context `{JobPreemptable Job}.
We assume the classic (i.e., Liu & Layland) model of readiness
without jitter or self-suspensions, wherein pending jobs are
always ready.
For clarity, let's denote the relative deadline of a task as D.
Consider the EDF policy that indicates a higher-or-equal
priority relation. Note that we do not relate the EDF policy
with the scheduler. However, we define functions for
Interference and Interfering Workload that actively use the
concept of priorities.
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.
Next, consider any valid ideal uni-processor schedule of this arrival sequence
that follows the scheduling policy.
Variable sched : schedule (ideal.processor_state Job).
Hypothesis H_sched_valid : valid_schedule sched arr_seq.
Hypothesis H_respects_policy : respects_JLFP_policy_at_preemption_point arr_seq sched EDF.
Hypothesis H_sched_valid : valid_schedule sched arr_seq.
Hypothesis H_respects_policy : respects_JLFP_policy_at_preemption_point arr_seq sched EDF.
To use the theorem uniprocessor_response_time_bound_seq from
the Abstract RTA module, we need to specify functions of
interference, interfering workload, and task_IBF. Next, we
define interference and interfering workload; we return to
task_IBF later.
Instantiation of Interference
We say that job j incurs interference at time t iff it cannot execute due to a higher-or-equal-priority job being scheduled, or if it incurs a priority inversion.
#[local] Instance ideal_jlfp_interference : Interference Job :=
ideal_jlfp_interference arr_seq sched.
ideal_jlfp_interference arr_seq sched.
Instantiation of Interfering Workload
The interfering workload, in turn, is defined as the sum of the priority inversion function and interfering workload of jobs with higher or equal priority.
#[local] Instance ideal_jlfp_interfering_workload : InterferingWorkload Job :=
ideal_jlfp_interfering_workload arr_seq sched.
ideal_jlfp_interfering_workload arr_seq sched.
Note that we differentiate between abstract and classical
notions of work-conserving schedule.
Let work_conserving_ab := definitions.work_conserving arr_seq sched.
Let work_conserving_cl := work_conserving.work_conserving arr_seq sched.
Let work_conserving_cl := work_conserving.work_conserving arr_seq sched.
We assume that the schedule is a work-conserving schedule
in the classical sense, and later prove that the hypothesis
about abstract work-conservation also holds.
Assume that a job cost cannot be larger than a task cost.
Consider an arbitrary task set ts.
Next, we assume that all jobs come from the task set.
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.
Consider a valid preemption model...
...and a valid task run-to-completion threshold function. That
is, task_rtct tsk is (1) no bigger than tsk's cost, (2) for
any job of task tsk job_rtct is bounded by task_rtct.
We introduce rbf as an abbreviation of the task request bound
function, which is defined as task_cost(T) × max_arrivals(T,Δ)
for some task T.
Using the sum of individual request bound functions, we define
the request bound function of all tasks (total request bound
function).
Assume that there exists a bound on the length of any priority
inversion experienced by any job of task tsk. Since we analyze
only task tsk, we ignore the lengths of priority inversions
incurred by any other tasks.
Variable priority_inversion_bound : duration → duration.
Hypothesis H_priority_inversion_is_bounded :
priority_inversion_is_bounded_by
arr_seq sched tsk priority_inversion_bound.
Hypothesis H_priority_inversion_is_bounded :
priority_inversion_is_bounded_by
arr_seq sched tsk priority_inversion_bound.
Let L be any positive fixed point of the busy interval
recurrence.
To reduce the time complexity of the analysis, we introduce the
notion of search space for EDF. Intuitively, this corresponds to
all "interesting" arrival offsets that the job under analysis
might have with regard to the beginning of its busy-window.
In the case of the search space for EDF, we consider three
conditions. First, we ask whether task_rbf A ≠ task_rbf (A +
ε).
Second, we ask whether there exists a task tsko from ts such
that tsko ≠ tsk and rbf(tsko, A + D tsk - D tsko) ≠ rbf(tsko,
A + ε + D tsk - D tsko). Note that we use a slightly uncommon
notation has (λ tsko ⇒ P tskₒ) ts, which can be interpreted as
follows: the task set ts contains a task tsko such that a
predicate P holds for tsko.
Definition bound_on_total_hep_workload_changes_at A :=
has (fun tsko ⇒
(tsk != tsko)
&& (rbf tsko (A + D tsk - D tsko)
!= rbf tsko ((A + ε) + D tsk - D tsko))) ts.
has (fun tsko ⇒
(tsk != tsko)
&& (rbf tsko (A + D tsk - D tsko)
!= rbf tsko ((A + ε) + D tsk - D tsko))) ts.
Definition priority_inversion_changes_at (A : duration) :=
priority_inversion_bound (A - ε) != priority_inversion_bound A.
priority_inversion_bound (A - ε) != priority_inversion_bound A.
The final search space for EDF is a set of offsets that are less
than L and where priority_inversion_bound, task_rbf, or
bound_on_total_hep_workload changes in value.
Definition is_in_search_space (A : duration) :=
(A < L) && (priority_inversion_changes_at A
|| task_rbf_changes_at A
|| bound_on_total_hep_workload_changes_at A).
(A < L) && (priority_inversion_changes_at A
|| task_rbf_changes_at A
|| bound_on_total_hep_workload_changes_at A).
Let R be a value that upper-bounds the solution of each
response-time recurrence, i.e., for any relative arrival time
A in the search space, there exists a corresponding solution
F such that R ≥ F + (task cost - task lock-in service).
Variable R : duration.
Hypothesis H_R_is_maximum :
∀ (A : duration),
is_in_search_space A →
∃ (F : duration),
A + F ≥ priority_inversion_bound A
+ (task_rbf (A + ε) - (task_cost tsk - task_rtct tsk))
+ bound_on_athep_workload ts tsk A (A + F) ∧
R ≥ F + (task_cost tsk - task_rtct tsk).
Hypothesis H_R_is_maximum :
∀ (A : duration),
is_in_search_space A →
∃ (F : duration),
A + F ≥ priority_inversion_bound A
+ (task_rbf (A + ε) - (task_cost tsk - task_rtct tsk))
+ bound_on_athep_workload ts tsk A (A + F) ∧
R ≥ F + (task_cost tsk - task_rtct tsk).
Finally, we define the interference bound function
(task_IBF). task_IBF bounds the interference if tasks are
sequential. Since tasks are sequential, we exclude interference
from other jobs of the same task. For EDF, we define task_IBF
as the sum of the priority interference bound and the
higher-or-equal-priority workload.
Filling Out Hypothesis Of Abstract RTA Theorem
In this section we prove that all hypotheses necessary to use the abstract theorem are satisfied.
First, we prove that task_IBF is indeed a valid bound on the
cumulative task interference.
Lemma instantiated_task_interference_is_bounded :
task_interference_is_bounded_by arr_seq sched tsk task_IBF.
task_interference_is_bounded_by arr_seq sched tsk task_IBF.
Finally, we show that there exists a solution for the response-time recurrence.
To rule out pathological cases with the concrete search
space, we assume that the task cost is positive and the
arrival curve is non-pathological.
Hypothesis H_task_cost_pos : 0 < task_cost tsk.
Hypothesis H_arrival_curve_pos : 0 < max_arrivals tsk ε.
Hypothesis H_arrival_curve_pos : 0 < max_arrivals tsk ε.
Given any job j of task tsk that arrives exactly A units
after the beginning of the busy interval, the bound of the
total interference incurred by j within an interval of
length Δ is equal to task_rbf (A + ε) - task_cost tsk +
task_IBF(A, Δ).
Next, consider any A from the search space (in the abstract sense).
Variable A : duration.
Hypothesis H_A_is_in_abstract_search_space :
search_space.is_in_search_space L total_interference_bound A.
Hypothesis H_A_is_in_abstract_search_space :
search_space.is_in_search_space L total_interference_bound A.
We prove that A is also in the concrete search space.
Then, there exists solution for response-time recurrence (in the abstract sense).
Corollary correct_search_space:
∃ F,
A + F ≥ task_rbf (A + ε) - (task_cost tsk - task_rtct tsk) + task_IBF A (A + F) ∧
R ≥ F + (task_cost tsk - task_rtct tsk).
End SolutionOfResponseTimeReccurenceExists.
End FillingOutHypothesesOfAbstractRTATheorem.
∃ F,
A + F ≥ task_rbf (A + ε) - (task_cost tsk - task_rtct tsk) + task_IBF A (A + F) ∧
R ≥ F + (task_cost tsk - task_rtct tsk).
End SolutionOfResponseTimeReccurenceExists.
End FillingOutHypothesesOfAbstractRTATheorem.