# Library prosa.results.fifo.rta

From mathcomp Require Import ssreflect ssrbool eqtype ssrnat seq path fintype bigop.

Require Import prosa.model.readiness.basic.

Require Import prosa.model.priority.fifo.

Require Import prosa.analysis.facts.priority.fifo.

Require Import prosa.analysis.abstract.ideal_jlfp_rta.

Require Export prosa.analysis.facts.busy_interval.carry_in.

Require Export prosa.analysis.facts.busy_interval.ideal.inequalities.

Require Import prosa.analysis.facts.model.task_cost.

Require Import prosa.model.readiness.basic.

Require Import prosa.model.priority.fifo.

Require Import prosa.analysis.facts.priority.fifo.

Require Import prosa.analysis.abstract.ideal_jlfp_rta.

Require Export prosa.analysis.facts.busy_interval.carry_in.

Require Export prosa.analysis.facts.busy_interval.ideal.inequalities.

Require Import prosa.analysis.facts.model.task_cost.

The formal development and the proofs in this file are described in-depth in
the following paper:
The interested reader is invited to follow along in parallel both in the
paper and here. In particular, the below sections labeled
In the following, we derive a response-time analysis for FIFO schedulers,
assuming a workload of sporadic real-time tasks characterized by arbitrary
arrival curves executing upon an ideal uniprocessor. To this end, we
instantiate the

- Bedarkar et al.,
*"From Intuition to Coq: A Case Study in Verified Response-Time Analysis of FIFO Scheduling"*, RTSS'22.

*A*through*H*correspond directly to the equivalently labeled subsections in Section IV of the paper.# Response-Time Analysis for FIFO Schedulers

*abstract Response-Time Analysis*(aRTA) as provided in the prosa.analysis.abstract module.## A. Defining the System Model

- tasks, jobs, and their parameters,
- the sequence of job arrivals,
- worst-case execution time (WCET) and the absence of self-suspensions,
- the set of tasks under analysis,
- the task under analysis, and, finally,
- an arbitrary schedule of the task set.

### Tasks and Jobs

Context {Task : TaskType}.

Context `{TaskCost Task}.

Context `{MaxArrivals Task}.

Context `{TaskRunToCompletionThreshold Task}.

Context `{TaskCost Task}.

Context `{MaxArrivals Task}.

Context `{TaskRunToCompletionThreshold Task}.

... and any type of jobs associated with these tasks, where each job has
an arrival time job_arrival, a cost job_cost, and an arbitrary
preemption model indicated by job_preemptable.

Context {Job : JobType} `{JobTask Job Task}.

Context `{JobArrival Job}.

Context `{JobCost Job}.

Context `{JobPreemptable Job}.

Context `{JobArrival Job}.

Context `{JobCost Job}.

Context `{JobPreemptable Job}.

### The Job Arrival Sequence

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.

### Absence of Self-Suspensions and WCET Compliance

#[local] Existing Instance basic_ready_instance.

We further require that a job's cost cannot exceed its task's stated
WCET.

... and assume that all jobs stem from tasks in this task set.

Furthermore, we assume that max_arrivals is a family of valid arrival
curves that constrains the arrival sequence arr_seq, i.e., for any task
tsk in ts, max_arrival tsk is (1) an arrival bound of tsk, and ...

... (2) a monotonic function that equals 0 for the empty interval delta = 0.

We assume that tsk is described by a valid task

*run-to-completion threshold*. That is, there exists a task parameter task_rtct such that task_rtct tsk is
Hypothesis H_valid_run_to_completion_threshold :

valid_task_run_to_completion_threshold arr_seq tsk.

valid_task_run_to_completion_threshold arr_seq tsk.

### The Schedule

Variable sched : schedule (ideal.processor_state Job).

Hypothesis H_valid_schedule : valid_schedule sched arr_seq.

Hypothesis H_valid_schedule : valid_schedule sched arr_seq.

We assume that the schedule complies with the preemption model ...

... and, last but not least, that it respects the FIFO scheduling
policy.

Hypothesis H_respects_policy_at_preemption_point :

respects_JLFP_policy_at_preemption_point arr_seq sched (FIFO Job).

respects_JLFP_policy_at_preemption_point arr_seq sched (FIFO Job).

## B. Encoding the Scheduling Policy and Preemption Model

*interference*and

*interfering workload*that apply to any job-level fixed-priority (JLFP) policy (as provided in the module ideal_jlfp_rta).

Let interference (j : Job) (t : instant) :=

ideal_jlfp_rta.interference arr_seq sched j t.

Let interfering_workload (j : Job) (t : instant) :=

ideal_jlfp_rta.interfering_workload arr_seq sched j t.

ideal_jlfp_rta.interference arr_seq sched j t.

Let interfering_workload (j : Job) (t : instant) :=

ideal_jlfp_rta.interfering_workload arr_seq sched j t.

Please refer to the general definitions (by clicking on the links above)
to see how they correspond to the definitions provided in Listing 3 of the
paper (they are identical).
The next step is to connect the classic notion of work conservation with
the abstract notion assumed by aRTA. First, let us recall the abstract and
classic notations of work conservation as work_conserving_ab and
work_conserving_cl, respectively.

## C. Classic and Abstract Work Conservation

Let work_conserving_ab := abstract.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.

In the following, we make the standard assumption that the schedule is
work-conserving in the classic sense.

As explained in much detail in the paper, a general proof obligation of
aRTA is to show that its abstract notion of work conservation is also
satisfied. That is, the classic, policy-specific notion assumed in
H_work_conserving needs to be "translated" into the abstract notion
understood by aRTA. Fortunately, in our case the proof is trivial: as a
benefit of reusing the general definitions of interference and
interfering_workload for JLFP policies, we can reuse the existing
general lemma instantiated_i_and_w_are_coherent_with_schedule. This
lemma immediately allows us to conclude that the schedule is
work-conserving in the abstract sense with respect to interference and
interfering_workload.

The preceding fact abstractly_work_conserving corresponds to Lemma 1 in
the paper. To see the correspondence, refer to the definition of
definitions.work_conserving (by clicking the link in the above
definition).
The next step is to establish a bound on the maximum busy-window length,
which aRTA requires to be given.
To this end, we assume that we are given a positive value L ...

## D. Bounding the Maximum Busy-Window Length

... that is a fixed point of the following equation.

Given this definition of L, it is our proof obligation to show that all
busy windows (in the

*abstract*sense) are indeed bounded by L. To this end, let us first recall the notion of a bound on the maximum busy-window length (or, interchangeably, busy-interval length) as understood by aRTA.
Let busy_windows_are_bounded_by L :=

busy_intervals_are_bounded_by arr_seq sched tsk interference interfering_workload L.

busy_intervals_are_bounded_by arr_seq sched tsk interference interfering_workload L.

We observe that the length of any (abstract) busy window in sched is
indeed bounded by L. Again, the proof is trivial because we can reuse a
general lemma, namely instantiated_busy_intervals_are_bounded in this
case, due to the choice to reuse the existing JLFP definitions of
interference and interfering_workload.

The preceding fact busy_windows_are_bounded correspond to Lemma 2 in the
paper. To clearly see the correspondence, refer to the definition of
busy_intervals_are_bounded_by (by clicking on the link in the definition
above).
Finally, we define the

## E. Defining the Interference Bound Function (IBF)

*interference bound function*(IBF). IBF bounds the cumulative interference incurred by a job in its busy window. In general, aRTA expects to reason about an IBF parametric in two parameters, a*relative arrival offset*A and an*interval length*Δ, as described in the paper. In our specific case, for FIFO scheduling, only A is actually relevant. We therefore define IBF as the sum, across all tasks, of the per-task request-bound functions (RBFs) in the interval A + ε minus the WCET of the task under analysis tsk.
Let IBF tsk (A Δ : duration) := (\sum_(tsko <- ts) task_request_bound_function tsko (A + ε))

- task_cost tsk.

- task_cost tsk.

As discussed in the paper, our proof obligation now is to show that the
stated IBF is indeed correct. To this end, we first establish two
auxiliary lemmas.
Because we reuse the general JLFP notions of interference and
interfering_workload, which allowed us to save much proof effort in the
preceding sections, we must reason about priority inversion. While
priority inversion is

### Absence of Priority Inversion

*conceptually*not relevant under FIFO scheduling, it clearly is a factor in the general JLFP case, and hence shows up in the definitions of interference and interfering_workload. We therefore next show it to be*actually*impossible, too, by proving that, under FIFO scheduling, the cumulative priority inversion experienced by a job j in any interval within its busy window is always 0.
Variable j : Job.

Hypothesis H_j_arrives : arrives_in arr_seq j.

Hypothesis H_job_of_tsk : job_of_task tsk j.

Hypothesis H_j_arrives : arrives_in arr_seq j.

Hypothesis H_job_of_tsk : job_of_task tsk j.

Assume that the job has a positive cost (as we later do not need to
reason about zero-cost jobs).

Assume the busy interval of the job j is given by

`[t1,t2)`

.
Variable t1 t2 : duration.

Hypothesis H_busy_interval :

definitions.busy_interval sched interference interfering_workload j t1 t2.

Hypothesis H_busy_interval :

definitions.busy_interval sched interference interfering_workload j t1 t2.

Consider any sub-interval

`[t1, t1 + Δ)`

of the busy interval of j.
We prove that the cumulative priority inversion in the interval

`[t1, t1 + Δ)`

is indeed 0.
Lemma no_priority_inversion:

cumulative_priority_inversion arr_seq sched j t1 (t1 + Δ) = 0.

End AbsenceOfPriorityInversion.

cumulative_priority_inversion arr_seq sched j t1 (t1 + Δ) = 0.

End AbsenceOfPriorityInversion.

### Higher- and Equal-Priority Interference

Variable j : Job.

Hypothesis H_job_of_task : job_of_task tsk j.

Hypothesis H_j_in_arrivals : arrives_in arr_seq j.

Hypothesis H_job_cost_positive : job_cost_positive j.

Hypothesis H_job_of_task : job_of_task tsk j.

Hypothesis H_j_in_arrivals : arrives_in arr_seq j.

Hypothesis H_job_cost_positive : job_cost_positive j.

Consider the (abstract) busy window of j and denote it as

`[t1, t2)`

.
Variable t1 t2 : instant.

Hypothesis H_busy_window :

definitions.busy_interval sched interference interfering_workload j t1 t2.

Hypothesis H_busy_window :

definitions.busy_interval sched interference interfering_workload j t1 t2.

Consider any arbitrary sub-interval

`[t1, Δ)`

within the busy window
of j.
The cumulative interference from higher- and equal-priority jobs during

`[t1, Δ)`

is bounded as follows.
Lemma bound_on_hep_workload :

\sum_(t1 ≤ t < t1 + Δ) is_interference_from_another_hep_job sched j t ≤

\sum_(tsko <- ts) task_request_bound_function tsko (job_arrival j - t1 + ε) - task_cost tsk.

End BoundOnHEPWorkload.

\sum_(t1 ≤ t < t1 + Δ) is_interference_from_another_hep_job sched j t ≤

\sum_(tsko <- ts) task_request_bound_function tsko (job_arrival j - t1 + ε) - task_cost tsk.

End BoundOnHEPWorkload.

### Correctness of IBF

Lemma IBF_correct :

job_interference_is_bounded_by arr_seq sched tsk interference interfering_workload IBF.

job_interference_is_bounded_by arr_seq sched tsk interference interfering_workload IBF.

The preceding lemma IBF_correct corresponds to Lemma 3 in the paper. To
see the correspondence more clearly, refer to the definition of
job_interference_is_bounded_by in the above lemma.
In this section, we define the concrete search space for FIFO and relate
it to the abstract search space of aRTA. In the case of FIFO, the concrete
search space is the set of offsets less than L such that there exists a
task tsk' in ts such that r bf tsk' (A) ≠ rbf tsk' (A + ε).

## F. Defining the Search Space

Definition is_in_concrete_search_space (A : duration) :=

(A < L) && has (fun tsk' ⇒ task_request_bound_function tsk' (A) !=

task_request_bound_function tsk' ( A + ε )) ts.

(A < L) && has (fun tsk' ⇒ task_request_bound_function tsk' (A) !=

task_request_bound_function tsk' ( A + ε )) ts.

To enable the use of aRTA, we must now show that any offset A included
in the abstract search space is also included in the concrete search
space. That is, we must show that the concrete search space is a
refinement of the abstract search space assumed by aRTA.
To this end, first recall the notion of the abstract search space in aRTA.

Let is_in_abstract_search_space A := abstract.search_space.is_in_search_space tsk L IBF A.

Section SearchSpaceRefinement.

Section SearchSpaceRefinement.

Suppose we are given a job j of the task under analysis tsk with
positive cost. We use the existence of such a job in the subsequent
proof, even if it does not feature in the claim directly.

Variable j : Job.

Hypothesis H_j_arrives : arrives_in arr_seq j.

Hypothesis H_job_of_tsk : job_of_task tsk j.

Hypothesis H_positive_cost : 0 < task_cost tsk.

Hypothesis H_j_arrives : arrives_in arr_seq j.

Hypothesis H_job_of_tsk : job_of_task tsk j.

Hypothesis H_positive_cost : 0 < task_cost tsk.

Under this assumption, given any A from the

*abstract*search space, ...
... we prove that A is also in the concrete search space. In other
words, we prove that the abstract search space is a subset of the
concrete search space.

The preceding lemma search_space_refinement corresponds to Lemma 4 in
the paper, which is apparent after consulting the definitions of the
abstract and concrete search spaces.

## G. Stating the Response-Time Bound R

Variable R : duration.

Hypothesis H_R_max:

∀ (A : duration),

is_in_concrete_search_space A →

∃ (F : nat),

A + F ≥ \sum_(tsko <- ts) task_request_bound_function tsko (A + ε) ∧

F ≤ R.

Hypothesis H_R_max:

∀ (A : duration),

is_in_concrete_search_space A →

∃ (F : nat),

A + F ≥ \sum_(tsko <- ts) task_request_bound_function tsko (A + ε) ∧

F ≤ R.

Ultimately, we seek to apply aRTA to prove the correctness of this R.
However, in order to connect the concrete definition of R with aRTA, we
must first restate the bound in the shape of the abstract response-time
bound equation that aRTA expects, which we do next.

Suppose again we are given a job j of the task under analysis tsk
with positive cost. We use the existence of such a job in the subsequent
proof, even if it does not feature in the claim directly.

Variable j : Job.

Hypothesis H_j_arrives : arrives_in arr_seq j.

Hypothesis H_job_of_tsk : job_of_task tsk j.

Hypothesis H_job_cost_positive : job_cost_positive j.

Hypothesis H_j_arrives : arrives_in arr_seq j.

Hypothesis H_job_of_tsk : job_of_task tsk j.

Hypothesis H_job_cost_positive : job_cost_positive j.

We know that:

- if A is in the abstract search space, then it is also in the concrete search space; and
- if A is in the concrete search space, then there exists a solution that satisfies the inequalities stated in H_R_is_maximum.

Lemma soln_abstract_response_time_recurrence :

∀ A,

is_in_abstract_search_space A →

∃ (F : nat),

A + F ≥ task_rtct tsk + IBF tsk A (A + F) ∧

F + (task_cost tsk - task_rtct tsk) ≤ R.

∀ A,

is_in_abstract_search_space A →

∃ (F : nat),

A + F ≥ task_rtct tsk + IBF tsk A (A + F) ∧

F + (task_cost tsk - task_rtct tsk) ≤ R.

Lemma soln_abstract_response_time_recurrence is shown in Listing 3 in
the paper.

## H. Soundness of the Response-Time Bound

The preceding theorem uniprocessor_response_time_bound_FIFO corresponds
to Theorem 2 in the paper. The correspondence becomes clearer when referring
to the definition of task_response_time_bound, and then in turn to the
definitions of job_of_task and job_response_time_bound.