Для минимальной реализации планировщика задач с использоанием ML необходимо следующее:
Tokio для полноценной асинхронной поддержки.
Таймер Tokio для неблокирующих задержек.
Безблокировочная архитектура с асинхронными мьютексами.
Выделенный канал для передачи обучающих данных.
Фоновый поток для обучения модели.
Неблокирующее прогнозирование во время планирования.
Фьючерсы.
Система Waker - обрабатывается средой выполнения Tokio
use std::collections::{HashMap, BinaryHeap};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use tokio::sync::{mpsc, Mutex as AsyncMutex};
use ndarray::Array1;
use rand::Rng;
// Simplified task metadata
#[derive(Debug, Clone)]
struct TaskMetadata {
id: usize,
priority: f64,
dependencies: Vec<usize>,
start_time: Instant,
}
// ML Model (simplified)
struct MLModel {
weights: Array1<f64>,
}
impl MLModel {
fn new(num_features: usize) -> Self {
MLModel {
weights: Array1::ones(num_features),
}
}
fn predict(&self, features: &[f64]) -> f64 {
self.weights.dot(&Array1::from_vec(features.to_vec()))
}
async fn train_loop(receiver: mpsc::Receiver<(Array1<f64>, f64)>) {
// Batch training happens in background
while let Some((features, target)) = receiver.recv().await {
// Actual training logic here
println!("Training on data: {:?} -> {}", features, target);
}
}
}
// Async task scheduler
#[derive(Clone)]
struct Scheduler {
task_queue: Arc<AsyncMutex<BinaryHeap<PriorityTask>>>,
ml_sender: mpsc::Sender<(Array1<f64>, f64)>,
task_counter: Arc<Mutex<usize>>,
completed_tasks: Arc<Mutex<HashMap<usize, Duration>>>,
}
#[derive(Debug)]
struct PriorityTask {
metadata: TaskMetadata,
future: Pin<Box<dyn std::future::Future<Output = ()> + Send>>,
}
impl Eq for PriorityTask {}
impl PartialEq for PriorityTask {
fn eq(&self, other: &Self) -> bool {
self.metadata.id == other.metadata.id
}
}
impl PartialOrd for PriorityTask {
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
other.metadata.priority.partial_cmp(&self.metadata.priority)
}
}
impl Ord for PriorityTask {
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
self.partial_cmp(other).unwrap()
}
}
impl Scheduler {
fn new() -> Self {
let (ml_sender, ml_receiver) = mpsc::channel(100);
// Start ML training thread
tokio::spawn(async move {
MLModel::train_loop(ml_receiver).await
});
Scheduler {
task_queue: Arc::new(AsyncMutex::new(BinaryHeap::new())),
ml_sender,
task_counter: Arc::new(Mutex::new(0)),
completed_tasks: Arc::new(Mutex::new(HashMap::new())),
}
}
async fn spawn<F>(&self, future: F, dependencies: Vec<usize>)
where
F: Future<Output = ()> + Send + 'static,
{
let id = {
let mut counter = self.task_counter.lock().unwrap();
*counter += 1;
*counter
};
let metadata = TaskMetadata {
id,
priority: rand::thread_rng().gen_range(0.0..1.0), // Initial random priority
dependencies,
start_time: Instant::now(),
};
let task = PriorityTask {
metadata: metadata.clone(),
future: Box::pin(future),
};
self.task_queue.lock().await.push(task);
}
async fn run(&self) {
let model = MLModel::new(3); // Simplified model
while let Some(mut task) = self.task_queue.lock().await.pop() {
let features = self.calculate_features(&task.metadata).await;
let new_priority = model.predict(&features);
// Update task priority
task.metadata.priority = new_priority;
// Re-insert with new priority
self.task_queue.lock().await.push(task);
// Process highest priority task
if let Some(mut task) = self.task_queue.lock().await.pop() {
let start_time = Instant::now();
// Run the future to completion
task.future.await;
// Record completion time
let duration = start_time.elapsed();
self.completed_tasks.lock().unwrap()
.insert(task.metadata.id, duration);
// Send training data to ML thread
let target = if duration < Duration::from_millis(100) {
1.0
} else {
0.0
};
let _ = self.ml_sender.send((Array1::from_vec(features), target)).await;
}
}
}
async fn calculate_features(&self, metadata: &TaskMetadata) -> Vec<f64> {
let completed = self.completed_tasks.lock().unwrap();
let success_rate = metadata.dependencies.iter()
.filter(|id| completed.contains_key(id))
.count() as f64 / metadata.dependencies.len().max(1) as f64;
vec![
metadata.priority,
metadata.dependencies.len() as f64,
success_rate,
]
}
}
// Custom async sleep using Tokio's timers
async fn async_sleep(duration: Duration) {
tokio::time::sleep(duration).await;
}
#[tokio::main]
async fn main() {
let scheduler = Scheduler::new();
// Spawn tasks with dependencies
let sched_clone = scheduler.clone();
tokio::spawn(async move {
sched_clone.spawn(async {
println!("Task 1 started");
async_sleep(Duration::from_secs(1)).await;
println!("Task 1 completed");
}, vec![]).await;
});
// More tasks would be added here...
scheduler.run().await;
}