# Library prosa.results.edf.rta.fully_nonpreemptive

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

Require Import prosa.model.readiness.basic.

Require Export prosa.results.edf.rta.bounded_nps.

Require Export prosa.analysis.facts.preemption.task.nonpreemptive.

Require Export prosa.analysis.facts.preemption.rtc_threshold.nonpreemptive.

Require Export prosa.analysis.facts.readiness.basic.

Require Export prosa.model.task.preemption.fully_nonpreemptive.

Require Import prosa.model.priority.edf.

Require Import prosa.model.readiness.basic.

Require Export prosa.results.edf.rta.bounded_nps.

Require Export prosa.analysis.facts.preemption.task.nonpreemptive.

Require Export prosa.analysis.facts.preemption.rtc_threshold.nonpreemptive.

Require Export prosa.analysis.facts.readiness.basic.

Require Export prosa.model.task.preemption.fully_nonpreemptive.

Require Import prosa.model.priority.edf.

# RTA for Fully Non-Preemptive EDF

In this module we prove the RTA theorem for the fully non-preemptive EDF model.## 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.

#[local] Existing Instance basic_ready_instance.

We assume that jobs and tasks are fully nonpreemptive.

#[local] Existing Instance fully_nonpreemptive_job_model.

#[local] Existing Instance fully_nonpreemptive_task_model.

#[local] Existing Instance fully_nonpreemptive_rtc_threshold.

#[local] Existing Instance fully_nonpreemptive_task_model.

#[local] Existing Instance fully_nonpreemptive_rtc_threshold.

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.

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 non-preemptive uniprocessor schedule of this arrival sequence ...

Variable sched : schedule (ideal.processor_state Job).

Hypothesis H_sched_valid: valid_schedule sched arr_seq.

Hypothesis H_nonpreemptive_sched : nonpreemptive_schedule sched.

Hypothesis H_sched_valid: valid_schedule sched arr_seq.

Hypothesis H_nonpreemptive_sched : nonpreemptive_schedule 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).

We also define a bound for the priority inversion caused by jobs with lower priority.

Let blocking_bound A :=

\max_(tsk_o <- ts | (blocking_relevant tsk_o)

&& (task_deadline tsk_o > task_deadline tsk + A))

(task_cost tsk_o - ε).

\max_(tsk_o <- ts | (blocking_relevant tsk_o)

&& (task_deadline tsk_o > task_deadline tsk + A))

(task_cost tsk_o - ε).

Next, we define an upper bound on interfering workload received from jobs
of other tasks with higher-than-or-equal priority.

Let bound_on_total_hep_workload A Δ :=

\sum_(tsk_o <- ts | tsk_o != tsk)

rbf tsk_o (minn ((A + ε) + task_deadline tsk - task_deadline tsk_o) Δ).

\sum_(tsk_o <- ts | tsk_o != tsk)

rbf tsk_o (minn ((A + ε) + task_deadline tsk - task_deadline tsk_o) Δ).

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: nat.

Hypothesis H_R_is_maximum:

∀ A,

is_in_search_space A →

∃ F,

A + F ≥ blocking_bound A + (task_rbf (A + ε) - (task_cost tsk - ε))

+ bound_on_total_hep_workload A (A + F) ∧

R ≥ F + (task_cost tsk - ε).

Hypothesis H_R_is_maximum:

∀ A,

is_in_search_space A →

∃ F,

A + F ≥ blocking_bound A + (task_rbf (A + ε) - (task_cost tsk - ε))

+ bound_on_total_hep_workload A (A + F) ∧

R ≥ F + (task_cost tsk - ε).

Now, we can leverage the results for the abstract model with bounded nonpreemptive segments
to establish a response-time bound for the more concrete model of fully nonpreemptive scheduling.