# Library prosa.results.transfer_schedulability.paper_model

# Connecting the General Result to the Model in the Paper

In the following, we instantiate the general transfer schedulability result for a system model that more closely resembles the system model defined in the paper by Willemsen et al. (EMSOFT
2025, DOI: 10.1145/3763236). In particular, we explicitly introduce the notion of system evolutions, which are usually left implicit in Prosa.
Require Export prosa.util.all.
Require Export prosa.analysis.definitions.finish_time.
Require Export prosa.results.transfer_schedulability.criterion.

# Elements of the EMSOFT'25 System Model

We first define the key notions that refine the model in the paper relative to the more abstract one in the main proof of transfer schedulability:

- job precedence and delay constraints,
- system evolutions, and
- a notion of schedulers.

## Precedence and Delay

We introduce a readiness model that matches the model assumed in the paper: jobs have predecessors and may be scheduled (i.e., become ready) only after a predecessor-dependent delay has passed.

### Job Parameters

We introduce two new job parameters that model, for each job, the set of predecessors and the associated delays.
The list of a job j's predecessors is given by job_predecessors j.
Class JobPredecessors (Job : JobType) := { job_predecessors : Job → seq Job }.
The delay that must pass between when a job j is first scheduled and the completion of a predecessor j' is given by job_delay j j'.
Class JobDelay (Job : JobType) := { job_delay : Job → Job → duration }.

### Definition of Job Readiness

Based on the above two job parameters, we can precisely define the assumed precedence constraints in terms of a readiness model, which is a standard Prosa interface.
Section DelayedPrecedenceReadiness.
Consider any kind of jobs...
Context {Job : JobType}.
... and any kind of processor state.
Context {PState : ProcessorState Job}.
Suppose jobs have an arrival time, a cost, a set of predecessors, and predecessor-specific release delays.
Context `{JobArrival Job} `{JobCost Job} `{JobPredecessors Job} `{JobDelay Job}.
For a given predecessor j_pred of a job j, we say the delay has passed at time t if j_pred completed at least job_delay j j_pred time units earlier.
Let delay_has_passed sched j t j_pred :=
let
delta := job_delay j j_pred
in
(t ≥ delta) && completed_by sched j_pred (t - delta).
We say that a job j is released at a time t after its arrival if the delay has passed w.r.t. each predecessor.
Let is_released sched j t :=
(job_arrival j ≤ t)
&& all (delay_has_passed sched j t) (job_predecessors j).
A job j is hence ready to be scheduled at time t if and only if it is released and not yet complete.
#[local,program] Instance delayed_precedence_ready_instance : JobReady Job PState :=
{
job_ready sched j t := is_released sched j t && ~~ completed_by sched j t
}.
End DelayedPrecedenceReadiness.

## System Evolutions

Next, we introduce the notion of system evolutions that determine each job's cost and delay parameters.
Given an arbitrary type of evolutions Omega, ...
Class SystemEvolutions (Omega : Type) (Job : JobType) := {
... the job costs in an evolution omega : Omega are given by evo_costs omega and ...
evo_costs : Omega → JobCost Job;
... the delays are given by evo_delays omega.
evo_delays : Omega → JobDelay Job;
}.

## Scheduler Interface

Finally, we introduce the notion of a scheduling algorithm.
Section SchedulerDef.
Allowing for any type of system evolutions Omega, ...
Variable Omega : Type.
... any kind of jobs, ...
Variable Job : JobType.
... and any kind of processor model, ...
Context {PState : ProcessorState Job}.
... a scheduler is a function that, given each job's arrival time, predecessors, and an evolution omega, yields a schedule of the jobs.
Definition Scheduler
`{JobArrival Job} `{JobPredecessors} `{SystemEvolutions Omega}
:= Omega → schedule PState.
End SchedulerDef.

# Instantiation of the EMSOFT'25 System Model

With all basic elements defined, we can state the transfer schedulability results in terms of the system model assumed in the paper.
Section EMSOFTModel.

## The Set of Jobs

We consider jobs described by evolution-independent arrival times and predecessor sets, ...
Context {Job : JobType} `{JobArrival Job} `{JobPredecessors Job}.
... which arrive according to the arrival sequence arr_seq.
Variable arr_seq : arrival_sequence Job.
Hypothesis H_valid_arrivals : valid_arrival_sequence arr_seq.

## Ideal Uniprocessor Assumption

We consider ideal uniprocessor schedules.
#[local] Existing Instance ideal.processor_state.
Let PState := ideal.processor_state Job.

## System Evolutions

Let Omega denote the set of all system evolutions.
Variable Omega : Type.
Context `{SystemEvolutions Omega Job}.
For clarity, we introduce the following abbreviations:

- job_cost omega j denotes the cost of job j in evolution omega.

Let job_cost omega := @job_cost _ (evo_costs omega).

- job_delay omega j j' denotes the delay between jobs j and its predecessor j' in evolution omega.

Let job_delay omega := @job_delay _ (evo_delays omega).

- job_ready omega denotes the readiness model resulting from job_delay omega and the evolution-invariant predecessor sets.

Let job_ready (omega : Omega) : JobReady Job PState :=
@delayed_precedence_ready_instance _ _ _ (evo_costs omega) _ (evo_delays omega).

## Worst-Case Reference Evolution

Let omega_0 denote the system evolution with maximal parameters, ...
Variable omega_0 : Omega.
... that is, the job costs in omega_0 upper-bound the costs in any other evolution, ...
Hypothesis H_max_cost :
∀ omega j,
job_cost omega j ≤ job_cost omega_0 j.
... and the delays in omega_0 similarly upper-bound the delays in any other evolution.
Hypothesis H_max_delay :
∀ omega j j',
job_delay omega j j' ≤ job_delay omega_0 j j'.

## The Schedulers

Suppose we are given two scheduling algorithms algA and algB.
Variable algA algB : Scheduler Omega Job.
As discussed in the paper, we restrict our attention to schedulers that always produce well-formed schedules.
Hypothesis H_well_formed_A :
@valid_schedule _ _ _ (algA omega_0) (job_cost omega_0) (job_ready omega_0) arr_seq.
Hypothesis H_well_formed_B :
∀ omega, @valid_schedule _ _ _ (algB omega) (job_cost omega) (job_ready omega) arr_seq.
For brevity, recall the notion of schedulability_transferred. We specialize the definition such that algA omega_0 is the reference schedule and algB omega the online schedule.
Definition schedulability_transferred_AB omega :=
schedulability_transferred
(algA omega_0) (algB omega) (job_cost omega_0) (job_cost omega).

## Clairvoyant Results

We are now ready to state the results pertaining to clairvoyant scheduling algorithms.
First, recall the transfer_schedulability_criterion as applied to clairvoyant schedulers.
Definition clairvoyant_criterion :=
For any evolution omega, the criterion is assumed to hold ...
(∀ omega, transfer_schedulability_criterion
... for the reference schedule algA omega_0 and the online schedule algB omega ...
(algA omega_0) (algB omega)
... w.r.t. the respective job costs ...
(job_cost omega_0) (job_cost omega)
... for the given, fixed job arrival times ...
arr_seq
with regard to online job costs. This last parameter is the "clairvoyant" part.
(job_cost omega)).
The above clairvoyant criterion ensures that schedulability is transferred in every evolution.
Theorem clairvoyant_sufficiency :
clairvoyant_criterion →
∀ omega, schedulability_transferred_AB omega.
Conversely, the fact that schedulability is transferred in every evolution implies that the above criterion holds.
Theorem clairvoyant_necessity :
(∀ omega, schedulability_transferred_AB omega) →
clairvoyant_criterion.

## Non-Clairvoyant Result

Finally, we state the sufficiency result for non-clairvoyant schedulers.
As above, we recall the transfer schedulability criterion, this time instantiated for reference costs job_cost omega_0 (again, note the last parameter).
Definition nonclairvoyant_criterion :=
∀ omega,
transfer_schedulability_criterion
(algA omega_0) (algB omega)
(job_cost omega_0) (job_cost omega)
arr_seq
(job_cost omega_0).
Assuming the above criterion holds, schedulability is transferred in all evolutions.
Theorem nonclairvoyant_sufficiency :
nonclairvoyant_criterion →
∀ omega, schedulability_transferred_AB omega.

# Results w.r.t. Finish Times

The results stated above are stated w.r.t. Prosa's standard completed_by predicate, which tests whether or not a job is finished at a given time (and in a given schedule) by comparing the total amount of service received so far with the job's cost.
The advantage of Prosa's completed_by predicate is that it is always applicable, even if some jobs never finish (i.e., are starved). It does not, however, yield the job's finish time, which is the earliest time at which the completed_by predicate holds, and which consequently is defined only in starvation-free schedules.
In the following, we re-state the above theorems in terms of finish times under the strengthened assumption that we are indeed looking at starvation-free schedules.
We begin with the two sufficiency theorems.

## Non-Starvation Assumption

For convenience, to match Prosa's existing definitions, we state the non-starvation assumption in terms of the existence of some finite response-time bound for every job in the arrival sequence.
First, we assume non-starvation only for the reference schedule
algA omega_0. The following notation means that there exists a bound R for each job j in the arrival sequence such that j completes within R time units of its arrival (in the reference schedule).
Hypothesis H_non_starvation :
∀ j,
arrives_in arr_seq j →
{ R : duration | @job_response_time_bound
_ _ (algA omega_0) (job_cost omega_0) _ j R }.

## Definition of Finish Times

With the non-starvation assumption in place, we can start reasoning about finish times. Let us recall the relevant definitions from Prosa.
Section FinishTimes.
In the context of a given evolution omega ...
Variable omega : Omega.
... and for a given, certainly finishing job jf that is part of the arrival sequence arr_seq, ...
Variable jf : Job.
Hypothesis H_arrives : arrives_in arr_seq jf.
... we exploit the non-starvation assumption ...
Let R := proj1_sig (H_non_starvation jf H_arrives).
Let ref_response_time_bound := proj2_sig (H_non_starvation jf H_arrives).
... to define jf's finish time in the reference schedule algA omega_0.
Definition ref_finish_time : instant :=
finish_time _ _ _ ref_response_time_bound.
To define the finish time in the online schedule, we need to put in a little more legwork, since the non-starvation assumption above applies only to the reference schedule, but not to the online schedule. To this end, assume that schedulability is transferred from algA omega_0 to algB omega.
Hypothesis H_trans : schedulability_transferred_AB omega.
Under this assumption, R is of course also a response-time bound w.r.t. the online schedule.
Lemma online_response_time_bound :
@job_response_time_bound _ _ (algB omega) (job_cost omega) _ jf R.
Having established that the online schedule is starvation-free, too, we can recall the finish time in algB omega, too.
Definition online_finish_time : instant :=
finish_time _ _ _ online_response_time_bound.
With the finish times in place for both schedules, we can state the property that we're after: the finish time in the online schedule is no later than in the reference schedule.
Definition online_finish_time_bounded :=
online_finish_time ≤ ref_finish_time.
End FinishTimes.

## Clairvoyant Sufficiency Result for Finish Times

With the above setup in place, we can easily re-state the clairvoyant_sufficiency theorem in terms of finish times.
Section SufficientFinishTimesClairvoyant.
Under the assumption that the schedulability transfer criterion holds w.r.t. online job costs, ...
Hypothesis H_criterion : clairvoyant_criterion.
... the finish time of any job (that is part of the arrival sequence) in any online schedule is no later than in the reference schedule.
Theorem clairvoyant_sufficiency' :
∀ omega j (IN : arrives_in arr_seq j),
online_finish_time_bounded omega j IN
(clairvoyant_sufficiency H_criterion omega).
End SufficientFinishTimesClairvoyant.
Before moving on to the necessity argument for the clairvoyant case, which will require further assumptions and legwork, we first quickly dispatch the sufficiency argument in the case of non-clairvoyant schedulers.

## Non-Clairvoyant Sufficiency Result for Finish Times

The sufficiency claim for non-clairvoyant schedulers follows virtually identically to the above theorem since the underlying argument is the same.
Section SufficientFinishTimesNonclairvoyant.
Under the assumption that the schedulability transfer criterion holds w.r.t. reference job costs, ...
Hypothesis H_criterion : nonclairvoyant_criterion.
... the finish time of any job (that is part of the arrival sequence) in any online schedule is no later than in the reference schedule.
Theorem nonclairvoyant_sufficiency' :
∀ omega j (IN : arrives_in arr_seq j),
online_finish_time_bounded omega j IN
(nonclairvoyant_sufficiency H_criterion omega).
End SufficientFinishTimesNonclairvoyant.

## Clairvoyant Necessity Result for Finish Times

For the necessity case, we need to strengthen our non-starvation assumption to also cover any online schedule. We again express this assumption in terms of the existence of a finite response-time bound for every job in all online schedules.
Hypothesis H_non_starvation' :
∀ j,
arrives_in arr_seq j →
∀ omega,
{ R | @job_response_time_bound _ _ (algB omega) (job_cost omega) _ j R }.

### Online Finish Times, Revisited

Based on the above non-starvation assumption, we can refer to the completion time in the online schedule directly.
Section FinishTimes.
In the context of a given evolution omega ...
Variable omega : Omega.
... and for a given, certainly finishing job jf that is part of the arrival sequence arr_seq, ...
Variable jf : Job.
Hypothesis H_arrives : arrives_in arr_seq jf.
... we exploit the non-starvation assumption ...
Let online_response_time_bound :=
proj2_sig (H_non_starvation' jf H_arrives omega).
... to define j's finish time in the online schedule algB omega.
Definition online_finish_time' : instant :=
finish_time _ _ _ online_response_time_bound.
Using this notion of j's online finish time, we can re-state that it finishes no later in the online schedule than in the reference schedule.
Definition online_finish_time_bounded' :=
online_finish_time' ≤ ref_finish_time jf H_arrives.
End FinishTimes.

### Clairvoyant Necessity Theorem

Finally, we are in a position to state the necessity result w.r.t. finish times.
Section NecessaryFinishTimesClairvoyant.
Under the assumption that, for any job that arrives in the arrival sequence, the finish time in any online schedule is no later than in the reference schedule, ...
Hypothesis H_bounded :
∀ omega j in_arrival_sequence,
online_finish_time_bounded' omega j in_arrival_sequence.
... we can show that the transfer schedulability criterion w.r.t. online job costs holds.
Theorem clairvoyant_necessity' : clairvoyant_criterion.
End NecessaryFinishTimesClairvoyant.
End EMSOFTModel.