Library prosa.results.edf.rta.bounded_nps
Require Export prosa.util.tactics.
Require Import prosa.model.readiness.basic.
Require Export prosa.analysis.facts.busy_interval.pi_bound.
Require Export prosa.analysis.facts.busy_interval.arrival.
Require Export prosa.results.edf.rta.bounded_pi.
Require Export prosa.model.schedule.work_conserving.
Require Export prosa.analysis.definitions.busy_interval.classical.
Require Export prosa.analysis.facts.blocking_bound.edf.
Require Export prosa.analysis.facts.workload.edf_athep_bound.
Require Import prosa.model.readiness.basic.
Require Export prosa.analysis.facts.busy_interval.pi_bound.
Require Export prosa.analysis.facts.busy_interval.arrival.
Require Export prosa.results.edf.rta.bounded_pi.
Require Export prosa.model.schedule.work_conserving.
Require Export prosa.analysis.definitions.busy_interval.classical.
Require Export prosa.analysis.facts.blocking_bound.edf.
Require Export prosa.analysis.facts.workload.edf_athep_bound.
RTA for EDF with Bounded Non-Preemptive Segments
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 `{JobTask Job Task}.
Context `{Arrival : JobArrival Job}.
Context `{Cost : JobCost 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 ...
Variable sched : schedule (ideal.processor_state Job).
Hypothesis H_sched_valid : valid_schedule sched arr_seq.
Hypothesis H_sched_valid : valid_schedule sched arr_seq.
In addition, we assume the existence of a function mapping jobs
to their preemption points ...
... and assume that it defines a valid preemption model with
bounded non-preemptive segments.
Hypothesis H_valid_model_with_bounded_nonpreemptive_segments:
valid_model_with_bounded_nonpreemptive_segments arr_seq sched.
valid_model_with_bounded_nonpreemptive_segments arr_seq sched.
Next, we assume that the schedule is a work-conserving schedule...
... and the schedule respects the scheduling policy at every preemption point.
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.
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 as an abbreviation rbf for the task request bound function,
which is defined as task_cost(T) × max_arrivals(T,Δ) for a task T.
Using the sum of individual request bound functions, we define the request bound
function of all tasks (total request bound function).
Let's define some local names for clarity.
Search Space
Definition is_in_search_space (L A : duration) :=
(A < L) && (task_rbf_changes_at tsk A
|| bound_on_total_hep_workload_changes_at ts tsk A).
(A < L) && (task_rbf_changes_at tsk A
|| bound_on_total_hep_workload_changes_at ts tsk A).
For the following proof, we exploit the fact that the blocking bound is
monotonically decreasing in A, which we note here.
Fact blocking_bound_decreasing :
∀ A1 A2,
A1 ≤ A2 →
blocking_bound ts tsk A1 ≥ blocking_bound ts tsk A2.
∀ A1 A2,
A1 ≤ A2 →
blocking_bound ts tsk A1 ≥ blocking_bound ts tsk A2.
To use the refined search space with the abstract theorem, we must show
that it still includes all relevant points. To this end, we first observe
that a step in the blocking bound implies the existence of a task that
could release a job with an absolute deadline equal to the absolute
deadline of the job under analysis.
Lemma task_with_equal_deadline_exists :
∀ {A},
priority_inversion_changes_at (blocking_bound ts tsk) A →
∃ tsk_o, (tsk_o \in ts)
&& (blocking_relevant tsk_o)
&& (tsk_o != tsk)
&& (D tsk_o == D tsk + A).
∀ {A},
priority_inversion_changes_at (blocking_bound ts tsk) A →
∃ tsk_o, (tsk_o \in ts)
&& (blocking_relevant tsk_o)
&& (tsk_o != tsk)
&& (D tsk_o == D tsk + A).
With the above setup in place, we can show that the search space defined
above by is_in_search_space covers the the more abstract search space
defined by bounded_pi.is_in_search_space.
Lemma search_space_inclusion :
∀ {A L},
bounded_pi.is_in_search_space ts tsk (blocking_bound ts tsk) L A →
is_in_search_space L A.
∀ {A L},
bounded_pi.is_in_search_space ts tsk (blocking_bound ts tsk) L A →
is_in_search_space L A.
Response-Time Bound
In this section, we prove that the maximum among the solutions of the response-time bound recurrence is a response-time bound for tsk.
Let L be any positive fixed point of the busy interval recurrence.
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 L A →
∃ (F : duration),
A + F ≥ blocking_bound ts tsk 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 L A →
∃ (F : duration),
A + F ≥ blocking_bound ts tsk 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).
Then, using the results for the general RTA for EDF-schedulers, we establish a
response-time bound for the more concrete model of bounded nonpreemptive segments.
Note that in case of the general RTA for EDF-schedulers, we just assume that
the priority inversion is bounded. In this module we provide the preemption model
with bounded nonpreemptive segments and prove that the priority inversion is
bounded.