# Library prosa.results.rta.ideal.elf.bounded_pi

Require Import prosa.util.int.
Require Export prosa.analysis.abstract.ideal.cumulative_bounds.
Require Export prosa.analysis.facts.priority.elf.
Require Export prosa.analysis.facts.interference.
Require Export prosa.analysis.facts.busy_interval.carry_in.
Require Export prosa.analysis.facts.readiness.basic.
Require Export prosa.analysis.facts.priority.jlfp_with_fp.
Require Export prosa.analysis.facts.model.rbf.
Require Export prosa.analysis.facts.busy_interval.pi.
Require Export prosa.analysis.facts.busy_interval.pi_cond.
Require Export prosa.analysis.abstract.ideal.abstract_seq_rta.
Require Export prosa.analysis.facts.model.task_cost.

# Response-Time Analysis for the ELF Scheduling Policy

In the following, we derive a response-time analysis for ELF 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 abstract Response-Time Analysis (aRTA) as provided in the prosa.analysis.abstract module.
Note that this analysis is currently specific to workloads where each task has bounded nonpreemptive segments. This specificity will be further explained by the assumptions which depend on it. The analysis catering to the more general model with bounded priority inversion remains future work.
Section AbstractRTAforELFwithArrivalCurves.

## A. Defining the System Model

Before any formal claims can be stated, an initial setup is needed to define the system model under consideration. To this end, we next introduce and define the following notions using Prosa's standard definitions and behavioral semantics:

- 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

Consider any type of tasks, each characterized by a WCET task_cost, a run-to-completion threshold task_rtct, a maximum non-preemptive segment length task_max_nonpreemptive_segment, an arrival curve max_arrivals, and a relative priority point task_priority_point ...
Context {Task : TaskType} `{TaskCost Task} `{TaskRunToCompletionThreshold Task}
`{TaskMaxNonpreemptiveSegment Task} `{MaxArrivals Task} `{PriorityPoint 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} {Arrival : JobArrival Job}
{Cost : JobCost Job} `{JobPreemptable Job}.

### The Job Arrival Sequence

Consider any valid arrival sequence arr_seq.
Variable arr_seq : arrival_sequence Job.
Hypothesis H_valid_arrival_sequence : valid_arrival_sequence arr_seq.

### Absence of Self-Suspensions and WCET Compliance

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 further require that a job's cost cannot exceed its task's stated WCET.
Hypothesis H_valid_job_cost : arrivals_have_valid_job_costs arr_seq.

### The Task Set

We consider an arbitrary task set ts...
Variable ts : list Task.
Hypothesis H_task_set : uniq ts.
... and assume that all jobs stem from tasks in this task set.
Hypothesis H_all_jobs_from_taskset : all_jobs_from_taskset arr_seq ts.
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 ...
Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.
... (2) a monotonic function that equals 0 for the empty interval delta = 0.
Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.

### The Task Under Analysis

Let tsk be any task in ts that is to be analyzed.
Variable tsk : Task.
Hypothesis H_tsk_in_ts : tsk \in ts.
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

- (1) no larger than tsk's WCET, and
- (2) for any job of task tsk, the job's run-to-completion threshold job_rtct is bounded by task_rtct tsk.

Hypothesis H_valid_run_to_completion_threshold :
valid_task_run_to_completion_threshold arr_seq tsk.

### The Schedule

Finally, we consider any arbitrary, valid ideal uniprocessor schedule of the given arrival sequence arr_seq.
Variable sched : schedule (ideal.processor_state Job).
Hypothesis H_sched_valid : valid_schedule sched arr_seq.
Now, we assume that the fixed-priority policy FP that parameterizes the ELF policy is...
Variable FP : FP_policy Task.
... reflexive, transitive, and total.
Hypothesis H_reflexive_priorities : reflexive_task_priorities FP.
Hypothesis H_transitive_priorities : transitive_task_priorities FP.
Hypothesis H_total_priorities : total_task_priorities FP.
We further assume that the schedule complies with the preemption model ...
Hypothesis H_valid_preemption_model : valid_preemption_model arr_seq sched.
... and finally, that it respects the ELF scheduling policy.
Hypothesis H_respects_policy : respects_JLFP_policy_at_preemption_point arr_seq sched (ELF FP).

## B. Interference and Interfering Workload

With the system model in place, the next step is to encode the scheduling policy and preemption model such that aRTA becomes applicable. To this end, we encode the semantics of the scheduling policy and preemption model by instantiating two functions, called interference and interfering_workload.
We can simply reuse the existing general definitions of interference and interfering workload that apply to job-level fixed-priority (JLFP) policy (as provided in the module analysis.abstract.ideal.iw_instantiation).
#[local] Instance ideal_elf_interference : Interference Job :=
ideal_jlfp_interference arr_seq sched.
#[local] Instance ideal_elf_interfering_workload : InterferingWorkload Job :=
ideal_jlfp_interfering_workload arr_seq sched.

## C. Classic and Abstract Work Conservation

We assume that the schedule is work-conserving in the classic sense.
Hypothesis H_work_conserving : work_conserving.work_conserving arr_seq sched.
This allows us to apply instantiated_i_and_w_are_coherent_with_schedule to conclude that abstract work-conservation also holds.

## D. The Priority Inversion Bound and its Validity

In this file, we break the priority inversion experienced by any job into two categories :

- (1) priority inversion caused by jobs belonging to tasks with lower priority than tsk
- (2) priority inversion caused by jobs belonging to tasks with equal priority as tsk

Note that, by definition of the ELF policy, no job from a task with higher priority than tsk can cause priority inversion.
We define a predicate to identify jobs from lower-priority tasks, or tasks for which tsk has higher priority.
Let is_lower_priority j' := hp_task tsk (job_task j').
We assume there exists a bound on the maximum length of priority inversion from these jobs that is incurred by any job of task tsk.
Variable priority_inversion_lp_tasks_bound : duration.
Hypothesis H_priority_inversion_from_lp_tasks_is_bounded :
priority_inversion_cond_is_bounded_by arr_seq sched tsk
is_lower_priority (constant priority_inversion_lp_tasks_bound).
Similarly, we define a predicate to select jobs whose tasks have equal priority as tsk...
Let is_equal_priority j' := ep_task tsk (job_task j').
... and assume that there exists a bound on the maximum length of priority inversion caused by them to any job of tsk.
Variable priority_inversion_ep_tasks_bound : duration → duration.
Hypothesis H_priority_inversion_from_ep_tasks_is_bounded :
priority_inversion_cond_is_bounded_by arr_seq sched tsk
is_equal_priority priority_inversion_ep_tasks_bound.
We then define the priority inversion bound as a maximum of the bounds on the two categories. Note that this definition is only applicable when all tasks have bounded nonpreemptive segments.
Definition priority_inversion_bound (A : duration) := maxn priority_inversion_lp_tasks_bound
(priority_inversion_ep_tasks_bound A).
Now, we define the following predicate to identify tasks that can release jobs.
Let blocking_relevant (tsk_o : Task) :=
(max_arrivals tsk_o ε > 0) && (task_cost tsk_o > 0).
We also define a predicate to identify equal priority tasks that cannot cause priority inversion for a job j, given that j's busy interval starts that instant t1.
Let is_ep_causing_intf (j: Job) (t1 : instant) (tsk_other:Task) :=
((job_arrival j)%:R - t1%:R + task_priority_point tsk - task_priority_point tsk_other ≥0)%R .
Using the above, we define the condition that an equal-priority task must satisfy for any of its jobs to cause blocking (or priority inversion) to a job j in j's busy interval starting at t1.
Let ep_task_blocking_relevant tsk_other j t1 :=
ep_task tsk_other tsk && ~~ is_ep_causing_intf j t1 tsk_other
&& blocking_relevant tsk_other.
Finally, we assume that the priority_inversion_ep_tasks_bound is bounded by the maximum task_cost of tasks which satisfy the above condition. Note that this assumption is valid only for the model where tasks have bounded nonpreemptive segments.
Hypothesis H_priority_inversion_from_ep_tasks_concrete_bound :
∀ j t1,
job_task j = tsk →
priority_inversion_ep_tasks_bound (job_arrival j - t1)
≤ \max_(i <- ts | ep_task_blocking_relevant i j t1) task_cost i.
Having defined bounds on two separate categories of priority inversion, we now show that the defined priority_inversion_bound upper-bounds the priority inversion faced by any job belonging to tsk, regardless of its cause.
Lemma priority_inversion_is_bounded :
priority_inversion_is_bounded_by arr_seq sched tsk priority_inversion_bound.

## E. Maximum Busy-Window Length

The next step is to establish a bound on the maximum busy-window length, which aRTA requires to be given.
Using the sum of individual request bound functions, we define the request bound function of all tasks with higher-or-equal priority FP (with respect to tsk).
Let total_hep_rbf := total_hep_request_bound_function_FP ts tsk.
To this end, we assume that we are given a positive value L ...
Variable L : duration.
Hypothesis H_L_positive : L > 0.
... that is a fixed point of the following equation.
Hypothesis H_fixed_point : L = priority_inversion_lp_tasks_bound + total_hep_rbf L.
Given this definition of L, we prove that L bounds the length of the busy window.
Lemma instantiated_busy_intervals_are_bounded :
busy_intervals_are_bounded_by arr_seq sched tsk L.

## F. The Interference-Bound Function

Next, we define the interference task_IBF and prove that task_IBF bounds the interference incurred by any job of tsk. Note that task_IBF only takes the interference from jobs of other tasks into account i.e., self-interference is not included.
We first consider the interference incurred due to strictly higher-priority tasks i.e., those which have strictly higher-priority according to the FP policy.
Definition total_hp_rbf := total_hp_request_bound_function_FP ts tsk.
We define the following parameterized end point of the interval during which interfering jobs of equal-priority tasks must arrive. The implicit beginning of the interval is the start of the busy window, i.e., at time t1.
Definition ep_task_intf_interval (tsk_o : Task) (A : instant) :=
((A + ε)%:R + task_priority_point tsk - task_priority_point tsk_o)%R.
Using this interval end point, we define the bound on the total equal-priority (EP) workload produced during the interval Δ as the sum of the RBFs of all tasks (with equal priority as tsk) in the task set ts (excluding tsk) over the minimum of Δ and ep_task_intf_interval.
Definition bound_on_total_ep_workload (A Δ : duration) :=
\sum_(tsk_o <- ts | ep_task tsk tsk_o && (tsk_o != tsk))
task_request_bound_function tsk_o (minn `|Num.max 0%R (ep_task_intf_interval tsk_o A)| Δ).
Finally, task_IBF for an interval Δ is defined as the sum of priority_inversion_bound, bound_on_total_ep_workload, and total_hp_rbf on Δ.
Definition task_IBF (A Δ : duration) :=
priority_inversion_bound A + bound_on_total_ep_workload A Δ + total_hp_rbf Δ.
In this section, we prove the soundness of task_IBF.
Section BoundingIBF.
Consider any job j of task tsk that has a positive job cost and is in the arrival sequence.
Variable j : Job.
Hypothesis H_job_of_task : job_of_task tsk j.
Hypothesis H_job_cost_positive : job_cost_positive j.
Hypothesis H_j_in_arr_seq : arrives_in arr_seq j.
Assume the busy interval of j (in the abstract sense) is given by `[t1,t2)`.
Variable t1 t2 : instant.
Hypothesis H_busy_window : definitions.busy_interval sched j t1 t2.
Consider any arbitrary length Δ interval `[t1, t1+ Δ)` within the busy window.
Variable Δ : duration.
Hypothesis H_Δ_in_busy : t1 + Δ ≤ t2.
We define the service needed by jobs belonging to other equal-priority tasks, that have higher-or-equal priority than j ...
Definition service_of_hp_jobs_from_other_ep_tasks (j : Job) (t1 t2 : instant) :=
service_of_jobs sched (fun jhp ⇒ other_ep_task_hep_job jhp j)
(arrivals_between arr_seq t1 t2) t1 t2.
... and show that it is equivalent to the cumulative interference incurred by j due to these jobs.
Lemma cumulative_intf_ep_task_service_equiv :
cumulative_interference_from_hep_jobs_from_other_ep_tasks arr_seq sched j t1 (t1 + Δ)
= service_of_hp_jobs_from_other_ep_tasks j t1 (t1 + Δ).
Similarly, we define the service required by jobs belonging to other strictly higher-priority tasks, that have higher-or-equal priority than j, ...
Definition service_of_hp_jobs_from_other_hp_tasks (j : Job) (t1 t2 : instant) :=
service_of_jobs sched (fun jhp ⇒ hp_task_hep_job jhp j)
(arrivals_between arr_seq t1 t2) t1 t2.
... and show that it is equivalent to the cumulative interference incurred by j due to these jobs.
Lemma cumulative_intf_hp_task_service_equiv :
cumulative_interference_from_hep_jobs_from_hp_tasks arr_seq sched j t1 (t1 + Δ)
= service_of_hp_jobs_from_other_hp_tasks j t1 (t1 + Δ).
Assume the arrival time of j relative to the busy window start is given by A.
Let A := job_arrival j - t1.
First, consider the case where ep_task_intf_interval ≤ Δ.
Section ShortenRange.
Consider any equal-priority task tsk_o distinct from tsk. Assume that tsk_o is in ts.
Variable tsk_o : Task.
Hypothesis H_tsko_in_ts : tsk_o \in ts.
Hypothesis H_neq : tsk_o != tsk.
Hypothesis H_task_priority : ep_task tsk tsk_o.
We define a predicate to identify higher-or-equal-priority jobs coming from the task tsk.
Let hep_jobs_from (tsk : Task) :=
fun (jo : Job) ⇒
hep_job jo j
&& ep_task (job_task jo) (job_task j)
&& (job_task jo == tsk).
If Δ is greater than ep_task_intf_interval for tsk_o and A, ...
Hypothesis H_Δ_ge : (ep_task_intf_interval tsk_o A ≤ Δ%:R)%R.
... then the workload of jobs satisfying the predicate hp_jobs_from in the interval `[t1,t1 + Δ)` is bounded by the workload in the interval `[t1, t1 + ep_task_intf_interval [tsk_o] [A])`.
Lemma total_workload_shorten_range :
workload_of_jobs [eta hep_jobs_from tsk_o]
(arrivals_between arr_seq t1 (t1 + Δ))
≤ workload_of_jobs [eta hep_jobs_from tsk_o]
(arrivals_between arr_seq t1
`|Num.max 0%R (t1%:R + ep_task_intf_interval tsk_o A)%R|).
End ShortenRange.
Then, we establish that the cumulative interference incurred by j due to all higher-or-equal-priority jobs from equal-priority tasks is bounded by the bound_on_ep_workload, ...
Lemma bound_on_ep_workload :
cumulative_interference_from_hep_jobs_from_other_ep_tasks arr_seq sched j t1 (t1 + Δ)
≤ bound_on_total_ep_workload (job_arrival j - t1) Δ.
... and that the cumulative interference incurred by j due to all higher-or-equal priority jobs from higher-priority tasks is bounded by the total_hp_rbf.
Lemma bound_on_hp_workload :
cumulative_interference_from_hep_jobs_from_hp_tasks arr_seq sched j t1 (t1 + Δ)
≤ total_hp_rbf Δ.
End BoundingIBF.
Finally, we use the above two lemmas to prove that task_IBF bounds the interference incurred by tsk.
Lemma instantiated_task_interference_is_bounded :
task_interference_is_bounded_by arr_seq sched tsk task_IBF.

## G. Defining the Search Space

In this section, we define the concrete search space for ELF. Then, we prove that, if a given A is in the abstract search space, then it is also included in the concrete search space.
For tsk, the total interference bound is defined as the sum of the interference due to

- (1) jobs belonging to the same task (self interference) and
- (2) jobs belonging to other tasks task_IBF.

Let total_interference_bound (A Δ : duration) :=
task_request_bound_function tsk (A + ε) - task_cost tsk + task_IBF A Δ.
In the case of ELF, of the four terms that constitute the total interference bound, only the priority_inversion_bound, task RBF and the bound on total equal-priority workload are dependent on the offset A.
Therefore, in order to define the concrete search space, we define predicates that capture when these values change for successive values of the offset A.
First, we define a predicate that captures whether the RBF of the task under analysis changes, ...
Definition task_rbf_changes_at (A : duration) :=
task_request_bound_function tsk A != task_request_bound_function tsk (A + ε).
... second, whether the total equal-priority workload bound changes, and ...
Definition bound_on_total_ep_workload_changes_at A :=
let any_task_bound_changes tsk_o := ep_task tsk tsk_o
&& (tsk_o != tsk)
&& (ep_task_intf_interval tsk_o (A - ε)
!= ep_task_intf_interval tsk_o A)
in has (any_task_bound_changes) ts.
... third, whether the priority-inversion bound changes.
Definition priority_inversion_changes_at (A : duration) :=
priority_inversion_bound (A - ε) != priority_inversion_bound A.
Finally, we define the concrete search space as the set containing all points less than L at which any of the bounds on priority inversion, task RBF, or total equal-priority workload changes.
Definition is_in_search_space (A : duration) :=
(A < L) && (priority_inversion_changes_at A
|| task_rbf_changes_at A
|| bound_on_total_ep_workload_changes_at A).
In this section, we prove that, for any job j of task tsk, if A is in the abstract search space, then it is also in the concrete search space.
Section ConcreteSearchSpace.
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.
Any point A in the abstract search space...
Variable A : duration.
Hypothesis H_A_is_in_abstract_search_space :
search_space.is_in_search_space L total_interference_bound A.
... is also in the concrete search space.
Lemma A_is_in_concrete_search_space :
is_in_search_space A.
End ConcreteSearchSpace.

## H. The Response-Time Bound R

Finally, we define the response-time bound R as the maximum offset by which any job j of task tsk has completed.
Variable R : duration.
Hypothesis H_R_is_maximum :
∀ (A : duration),
is_in_search_space A →
∃ (F : duration),
A + F ≥ priority_inversion_bound A
+ bound_on_total_ep_workload A (A + F)
+ total_hp_rbf (A + F)
+ (task_request_bound_function tsk (A + ε)
- (task_cost tsk - task_rtct tsk))
∧ R ≥ F + (task_cost tsk - task_rtct tsk).
Section ResponseTimeReccurence.
To rule out pathological cases with the H_R_is_maximum equation (such as task_cost tsk being greater than task_rbf (A + ε)), we assume that 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.
We have established that if A is in the abstract search then it is in the concrete search space, too. We also know by assumption that, if A is in the concrete search space, then there exists an R that satisfies H_R_is_maximum.
Using these facts, here we prove that if A is in the abstract search space, ...
Let is_in_search_space := search_space.is_in_search_space L total_interference_bound.
... then there exists a solution to the response-time equation as stated in the aRTA.
Corollary response_time_recurrence_solution_exists :
∀ A,
is_in_search_space A →
∃ F,
A + F ≥ task_request_bound_function tsk (A + ε)
- (task_cost tsk - task_rtct tsk)
+ task_IBF A (A + F)
∧ R ≥ F + (task_cost tsk - task_rtct tsk).
End ResponseTimeReccurence.

## I. Soundness of the Response-Time Bound

Finally, we prove that R is a bound on the response time of the task tsk.
Theorem uniprocessor_response_time_bound_elf :
task_response_time_bound arr_seq sched tsk R.
End AbstractRTAforELFwithArrivalCurves.