amberjs 1.17.0

// Intelligent Routing System
// AI-powered intelligent routing for distributed edge computing

use crate::edge::{NodeId, NodeStatus, Task, TaskPriority};
use serde::{Deserialize, Serialize};
use std::collections::BTreeMap;
use std::sync::{Arc, Mutex, RwLock};
use tokio::time::{TokioDuration, TokioInstant};
use anyhow::{Result, Error};
use std::time::{Duration, Instant};
use std::collections::HashMap;
use std::time::SystemTime;

/// Request for routing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Request {
    pub id: String,
    pub script: String,
    pub priority: RequestPriority,
    pub timeout_ms: u64,
    pub source_region: String,
}
/// Request priority
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum RequestPriority {
    Low,
    Normal,
    High,
    Critical,
}
/// Intelligent router
#[derive(Debug)]
pub struct IntelligentRouter {
    predictor: Arc<LoadPredictor>,
    optimizer: Arc<RouteOptimizer>,
    model: Arc<MLModel>,
    route_cache: Arc<RwLock<HashMap<String, NodeId>>>,
}
/// Load prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LoadPrediction {
    pub cpu_usage: f64,
    pub memory_usage: f64,
    pub estimated_queue_time_ms: u64,
    pub confidence: f64,
}
/// Route optimization result
#[derive(Debug, Clone)]
pub struct RouteOptimization {
    pub routes: Vec<Route>,
    pub improvement_percent: f64,
    pub optimization_time_ms: u64,
}
/// Network route
#[derive(Debug, Clone)]
pub struct Route {
    pub from: String,
    pub to: String,
    pub latency_ms: u64,
    pub bandwidth_mbps: u64,
}
/// Adaptive scheduler
#[derive(Debug)]
pub struct AdaptiveScheduler {
    scheduler: Arc<TaskScheduler>,
    learning_engine: Arc<LearningEngine>,
}
/// Task scheduling result
#[derive(Debug, Clone)]
pub struct SchedulePlan {
    pub node_id: NodeId,
    pub estimated_start_time_ms: u64,
    pub confidence_score: f64,
}
/// Feedback for learning
#[derive(Debug, Clone)]
pub struct Feedback {
    pub task_id: String,
    pub actual_execution_time_ms: u64,
    pub predicted_execution_time_ms: u64,
    pub success: bool,
    pub node_id: NodeId,
}
/// Load predictor
#[derive(Debug)]
pub struct LoadPredictor {
    history: Arc<RwLock<Vec<LoadSample>>>,
}
/// Load sample
#[derive(Debug, Clone)]
pub struct LoadSample {
    pub timestamp: std::time::SystemTime,
    pub cpu_usage: f64,
    pub memory_usage: f64,
    pub queue_size: u32,
}
/// Route optimizer
#[derive(Debug)]
pub struct RouteOptimizer {
    routes: Arc<RwLock<HashMap<String, Route>>>,
}
/// Machine learning model
#[derive(Debug)]
pub struct MLModel {
    model_name: String,
    weights: Vec<f64>,
}
/// Task scheduler
#[derive(Debug)]
pub struct TaskScheduler {
    nodes: Arc<RwLock<HashMap<NodeId, NodeStatus>>>,
}
/// Learning engine
#[derive(Debug)]
pub struct LearningEngine {
    adaptation_rate: f64,
    history: Arc<RwLock<Vec<Feedback>>>,
}
impl IntelligentRouter {
    /// Create a new intelligent router
    pub async fn new() -> Result<Self> {
        let router: _ = IntelligentRouter {
            predictor: Arc::new(Mutex::new(LoadPredictor::new()),.await?),
            optimizer: Arc::new(Mutex::new(RouteOptimizer::new()),.await?),
            model: Arc::new(Mutex::new(MLModel::new("routing_model_v1".to_string()),.await?),
            route_cache: Arc::new(Mutex::new(HashMap::new()))
        };
        println!("Intelligent router initialized");
        Ok(router)
    }
    /// Get predictor
    pub fn predictor(&self) -> &Arc<LoadPredictor> {
        &self.predictor
    }
    /// Get optimizer
    pub fn optimizer(&self) -> &Arc<RouteOptimizer> {
        &self.optimizer
    }
    /// Route a request to the optimal node
    pub async fn route_request(&self, request: &Request) -> Result<NodeId> {
        let start: _ = Instant::now();
        // Check cache first
        {
            let cache: _ = self.route_cache.read().await;
            if let Some(cached_node) = cache.get(&request.id) {
                println!("Using cached route for request {}", request.id);
                return Ok(cached_node.clone());
            }
        }
        // Get load predictions for all nodes
        let predictions: _ = self.predict_load_for_all_nodes().await?;
        // Select optimal node based on prediction
        let optimal_node: _ = self.select_optimal_node(request, &predictions).await?;
        // Cache the route
        {
            let mut cache = self.route_cache.write().await;
            cache.insert(request.id.clone(), optimal_node.clone());
        }
        let elapsed: _ = start.elapsed();
        println!("Routed request {} to node {} in {}ms",
                 request.id, optimal_node.0, elapsed.as_millis());
        Ok(optimal_node)
    }
    /// Predict load for a specific node
    pub async fn predict_load(&self, node_id: &NodeId) -> Result<LoadPrediction> {
        let prediction: _ = self.predictor.predict(node_id).await?;
        Ok(prediction)
    }
    /// Predict load for all nodes
    async fn predict_load_for_all_nodes(&self) -> Result<HashMap<NodeId, LoadPrediction> {
        let mut predictions = HashMap::new();
        // In real implementation, would query all active nodes
        let dummy_nodes: _ = vec![
            NodeId("node-us-west-1".to_string()),
            NodeId("node-us-east-1".to_string()),
            NodeId("node-eu-west-1".to_string()),
        ];
        for node_id in dummy_nodes {
            let pred: _ = self.predict_load(&node_id).await?;
            predictions.insert(node_id, pred);
        }
        Ok(predictions)
    }
    /// Select optimal node based on request and predictions
    async fn select_optimal_node(&self, request: &Request, predictions: &HashMap<NodeId, LoadPrediction>) -> Result<NodeId> {
        let mut best_node = None;
        let mut best_score = f64::MIN;
        for (node_id, prediction) in predictions {
            // Calculate score based on multiple factors
            let load_score: _ = 100.0 - prediction.cpu_usage;
            let queue_score: _ = 100.0 - (prediction.estimated_queue_time_ms as f64 / 100.0);
            let priority_score: _ = match request.priority {
                RequestPriority::Critical => 100.0,
                RequestPriority::High => 80.0,
                RequestPriority::Normal => 60.0,
                RequestPriority::Low => 40.0,
            };
            let total_score: _ = (load_score * 0.4) + (queue_score * 0.4) + (priority_score * 0.2);
            if total_score > best_score {
                best_score = total_score;
                best_node = Some(node_id.clone());
            }
        }
        best_node.ok_or_else(|| anyhow::anyhow!("No suitable node found"))
    }
    /// Optimize routing strategy
    pub async fn optimize_routes(&self) -> Result<RouteOptimization> {
        let start: _ = Instant::now();
        let optimization: _ = self.optimizer.optimize().await?;
        let elapsed: _ = start.elapsed();
        println!("Route optimization completed in {}ms", elapsed.as_millis());
        Ok(RouteOptimization {
            routes: optimization.routes,
            improvement_percent: optimization.improvement_percent,
            optimization_time_ms: elapsed.as_millis() as u64,
        })
    }
    /// Clear route cache
    pub async fn clear_cache(&self) {
        let mut cache = self.route_cache.write().await;
        cache.clear();
        println!("Route cache cleared");
    }
}
impl AdaptiveScheduler {
    /// Create a new adaptive scheduler
    pub async fn new() -> Result<Self> {
        let scheduler: _ = AdaptiveScheduler {
            scheduler: Arc::new(Mutex::new(TaskScheduler::new()),.await?),
            learning_engine: Arc::new(Mutex::new(LearningEngine::new()),.await?),
        };
        println!("Adaptive scheduler initialized");
        Ok(scheduler)
    }
    /// Get scheduler
    pub fn scheduler(&self) -> &Arc<TaskScheduler> {
        &self.scheduler
    }
    /// Get learning engine
    pub fn learning_engine(&self) -> &Arc<LearningEngine> {
        &self.learning_engine
    }
    /// Schedule a task
    pub async fn schedule_task(&self, task: &Task) -> Result<SchedulePlan> {
        let plan: _ = self.scheduler.schedule(task).await?;
        // Learn from the scheduling decision
        self.learning_engine.record_scheduling(&plan).await?;
        Ok(plan)
    }
    /// Adapt strategy based on feedback
    pub async fn adapt_strategy(&self, feedback: &Feedback) -> Result<()> {
        self.learning_engine.update_model(feedback).await?;
        Ok(())
    }
}
impl LoadPredictor {
    /// Create a new load predictor
    pub async fn new() -> Result<Self> {
        let predictor: _ = LoadPredictor {
            history: Arc::new(Mutex::new(Vec::new()))
        };
        println!("Load predictor initialized");
        Ok(predictor)
    }
    /// Predict load for a node
    pub async fn predict(&self, node_id: &NodeId) -> Result<LoadPrediction> {
        // Simulate load prediction
        tokio::time::sleep(Duration::from_millis(5)).await;
        Ok(LoadPrediction {
            cpu_usage: 45.0 + (node_id.0.len() as f64 % 30.0),
            memory_usage: 50.0 + (node_id.0.len() as f64 % 25.0),
            estimated_queue_time_ms: (node_id.0.len() as u64 * 10) % 500,
            confidence: 0.85,
        })
    }
    /// Predict next load based on history
    pub async fn predict_next_load(&self, history: &[LoadSample]) -> Result<LoadPrediction> {
        if history.is_empty() {
            return Ok(LoadPrediction {
                cpu_usage: 50.0,
                memory_usage: 50.0,
                estimated_queue_time_ms: 100,
                confidence: 0.5,
            });
        }
        // Simple linear prediction
        let last: _ = history.last().unwrap();
        let cpu_trend: _ = if history.len() > 1 {
            let prev: _ = &history[history.len() - 2];
            last.cpu_usage - prev.cpu_usage
        } else {
            0.0
        };
        let predicted_cpu: _ = (last.cpu_usage + cpu_trend).clamp(0.0, 100.0);
        Ok(LoadPrediction {
            cpu_usage: predicted_cpu,
            memory_usage: last.memory_usage,
            estimated_queue_time_ms: last.queue_size as u64 * 20,
            confidence: 0.8,
        })
    }
}
impl RouteOptimizer {
    /// Create a new route optimizer
    pub async fn new() -> Result<Self> {
        let optimizer: _ = RouteOptimizer {
            routes: Arc::new(Mutex::new(HashMap::new()))
        };
        println!("Route optimizer initialized");
        Ok(optimizer)
    }
    /// Find optimal route
    pub async fn find_optimal_route(&self, routes: &[Route], destination: &str) -> Result<Route> {
        let mut best_route = None;
        let mut best_latency = u64::MAX;
        for route in routes {
            if route.to == destination && route.latency_ms < best_latency {
                best_latency = route.latency_ms;
                best_route = Some(route.clone());
            }
        }
        best_route.ok_or_else(|| anyhow::anyhow!("No route found to {}", destination))
    }
    /// Optimize routes
    pub async fn optimize(&self) -> Result<RouteOptimization> {
        // Simulate route optimization
        tokio::time::sleep(Duration::from_millis(20)).await;
        Ok(RouteOptimization {
            routes: Vec::new(),
            improvement_percent: 15.5,
            optimization_time_ms: 20,
        })
    }
}
impl MLModel {
    /// Create a new ML model
    pub async fn new(model_name: String) -> Result<Self> {
        let model: _ = MLModel {
            model_name,
            weights: vec![0.3, 0.25, 0.2, 0.15, 0.1],
        };
        println!("ML model '{}' initialized", model.model_name);
        Ok(model)
    }
    /// Make a prediction
    pub async fn predict(&self, features: &[f64]) -> Result<Vec<f64> {
        if features.is_empty() {
            return Ok(vec![0.5]);
        }
        // Simple linear model
        let mut prediction = 0.0;
        for (i, feature) in features.iter().enumerate() {
            if i < self.weights.len() {
                prediction += feature * self.weights[i];
            }
        }
        // Normalize to [0, 1]
        prediction = prediction.clamp(0.0, 1.0);
        Ok(vec![prediction])
    }
}
impl TaskScheduler {
    /// Create a new task scheduler
    pub async fn new() -> Result<Self> {
        let scheduler: _ = TaskScheduler {
            nodes: Arc::new(Mutex::new(HashMap::new()))
        };
        println!("Task scheduler initialized");
        Ok(scheduler)
    }
    /// Schedule a task
    pub async fn schedule(&self, task: &Task) -> Result<SchedulePlan> {
        // Simulate scheduling
        tokio::time::sleep(Duration::from_millis(5)).await;
        let node_id: _ = NodeId(format!("scheduled-node-{}", task.id));
        let estimated_start: _ = 50;
        let confidence: _ = 0.9;
        Ok(SchedulePlan {
            node_id,
            estimated_start_time_ms: estimated_start,
            confidence_score: confidence,
        })
    }
}
impl LearningEngine {
    /// Create a new learning engine
    pub async fn new() -> Result<Self> {
        let engine: _ = LearningEngine {
            adaptation_rate: 0.1,
            history: Arc::new(Mutex::new(Vec::new()))
        };
        println!("Learning engine initialized");
        Ok(engine)
    }
    /// Record scheduling decision
    pub async fn record_scheduling(&self, plan: &SchedulePlan) -> Result<()> {
        // In real implementation, would record the scheduling decision
        Ok(())
    }
    /// Update model based on feedback
    pub async fn update_model(&self, feedback: &Feedback) -> Result<()> {
        // In real implementation, would update ML model weights
        tokio::time::sleep(Duration::from_millis(2)).await;
        let mut history = self.history.write().await;
        history.push(feedback.clone());
        // Keep only last 1000 feedback items
        if history.len() > 1000 {
            history.drain(0..history.len() - 1000);
        }
        println!("Updated model with feedback for task {}", feedback.task_id);
        Ok(())
    }
}