oxirs-stream 0.3.1

//! # WASM Edge Computing Runtime Module
//!
//! Resource optimizer, intelligent caching, prefetch prediction, and supporting
//! runtime types for the WASM edge computing subsystem.

use anyhow::Result;
use chrono::{DateTime, Utc};
use std::collections::HashMap;
use tokio::sync::RwLock;
use tracing::debug;

#[cfg(feature = "wasm")]
use wasmtime::{Engine, Module};

use super::{EdgeLocation, WasmPlugin};

// ============================================================
// Supporting data types
// ============================================================

#[derive(Debug, Clone)]
pub struct WorkloadDescription {
    pub id: String,
    pub plugins: Vec<WasmPlugin>,
    pub estimated_complexity: f64,
    pub estimated_memory_mb: u64,
    pub network_operations_per_second: f64,
    pub data_affinity_score: f64,
}

#[derive(Debug, Clone)]
pub struct WorkloadFeatures {
    pub computational_complexity: f64,
    pub memory_requirements: u64,
    pub network_intensity: f64,
    pub data_locality: f64,
    pub temporal_patterns: TemporalPattern,
    pub dependency_graph: DependencyGraph,
}

#[derive(Debug, Clone)]
pub struct TemporalPattern {
    pub peak_hours: Vec<u8>,
    pub seasonality: SeasonalityType,
    pub burst_probability: f64,
    pub sustained_load_factor: f64,
}

#[derive(Debug, Clone)]
pub enum SeasonalityType {
    Daily,
    Weekly,
    Monthly,
    Irregular,
}

#[derive(Debug, Clone)]
pub struct DependencyGraph {
    pub nodes: Vec<String>,
    pub edges: Vec<(String, String)>,
    pub critical_path_length: f64,
    pub parallelization_factor: f64,
}

#[derive(Debug, Clone)]
pub struct ResourcePrediction {
    pub predicted_cpu_usage: f64,
    pub predicted_memory_mb: u64,
    pub predicted_network_mbps: f64,
    pub confidence_interval: (f64, f64),
}

#[derive(Debug, Clone, Default)]
pub struct AllocationPlan {
    pub node_assignments: Vec<NodeAssignment>,
    pub estimated_latency_ms: f64,
    pub estimated_throughput: f64,
    pub cost_estimate: f64,
    pub confidence_score: f64,
}

#[derive(Debug, Clone)]
pub struct NodeAssignment {
    pub node_id: String,
    pub assigned_plugins: Vec<String>,
    pub resource_allocation: ResourceAllocation,
    pub priority_level: PriorityLevel,
}

#[derive(Debug, Clone)]
pub struct ResourceAllocation {
    pub cpu_cores: u32,
    pub memory_mb: u64,
    pub storage_gb: u64,
    pub network_mbps: f64,
}

#[derive(Debug, Clone)]
pub enum PriorityLevel {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone)]
pub struct AllocationConstraints {
    pub max_latency_ms: f64,
    pub min_throughput: f64,
    pub max_cost_per_hour: f64,
    pub max_optimization_iterations: usize,
    pub require_geographic_distribution: bool,
    pub min_reliability_score: f64,
}

#[derive(Debug, Clone)]
pub struct AllocationEvent {
    pub timestamp: DateTime<Utc>,
    pub workload: WorkloadDescription,
    pub allocation: AllocationPlan,
    pub predicted_performance: ResourcePrediction,
}

#[derive(Debug, Clone)]
pub struct ResourceModel {
    pub model_type: ModelType,
    pub parameters: Vec<f64>,
    pub accuracy: f64,
    pub last_trained: DateTime<Utc>,
}

#[derive(Debug, Clone)]
pub enum ModelType {
    LinearRegression,
    RandomForest,
    NeuralNetwork,
    GradientBoosting,
}

#[derive(Debug, Default)]
pub struct OptimizationMetrics {
    pub total_optimizations: u64,
    pub average_improvement_percent: f64,
    pub cost_savings_total: f64,
    pub latency_improvements: Vec<f64>,
}

// ============================================================
// PredictionEngine
// ============================================================

#[derive(Debug)]
pub struct PredictionEngine {
    models: HashMap<String, ResourceModel>,
}

impl Default for PredictionEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl PredictionEngine {
    pub fn new() -> Self {
        Self {
            models: HashMap::new(),
        }
    }

    pub async fn predict_resource_needs(
        &self,
        _features: &WorkloadFeatures,
    ) -> Result<ResourcePrediction> {
        // ML-based resource prediction (simplified)
        Ok(ResourcePrediction {
            predicted_cpu_usage: 2.5,
            predicted_memory_mb: 1024,
            predicted_network_mbps: 50.0,
            confidence_interval: (0.8, 0.95),
        })
    }
}

// ============================================================
// EdgeResourceOptimizer
// ============================================================

/// AI-driven resource allocation optimizer for WASM edge nodes
pub struct EdgeResourceOptimizer {
    resource_models: HashMap<String, ResourceModel>,
    allocation_history: RwLock<Vec<AllocationEvent>>,
    prediction_engine: PredictionEngine,
    optimization_metrics: RwLock<OptimizationMetrics>,
}

impl Default for EdgeResourceOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl EdgeResourceOptimizer {
    pub fn new() -> Self {
        Self {
            resource_models: HashMap::new(),
            allocation_history: RwLock::new(Vec::new()),
            prediction_engine: PredictionEngine::new(),
            optimization_metrics: RwLock::new(OptimizationMetrics::default()),
        }
    }

    /// Optimize resource allocation using machine learning
    pub async fn optimize_allocation(
        &self,
        workload: &WorkloadDescription,
        available_nodes: &[EdgeLocation],
    ) -> Result<AllocationPlan> {
        let features = self.extract_workload_features(workload).await?;
        let predictions = self
            .prediction_engine
            .predict_resource_needs(&features)
            .await?;

        let optimal_allocation = self
            .solve_allocation_problem(
                &predictions,
                available_nodes,
                &self.get_current_constraints().await?,
            )
            .await?;

        // Update allocation history for learning
        {
            let mut history = self.allocation_history.write().await;
            history.push(AllocationEvent {
                timestamp: Utc::now(),
                workload: workload.clone(),
                allocation: optimal_allocation.clone(),
                predicted_performance: predictions.clone(),
            });
        }

        Ok(optimal_allocation)
    }

    async fn extract_workload_features(
        &self,
        workload: &WorkloadDescription,
    ) -> Result<WorkloadFeatures> {
        Ok(WorkloadFeatures {
            computational_complexity: workload.estimated_complexity,
            memory_requirements: workload.estimated_memory_mb,
            network_intensity: workload.network_operations_per_second,
            data_locality: workload.data_affinity_score,
            temporal_patterns: self.analyze_temporal_patterns(workload).await?,
            dependency_graph: self.analyze_dependencies(workload).await?,
        })
    }

    async fn analyze_temporal_patterns(
        &self,
        _workload: &WorkloadDescription,
    ) -> Result<TemporalPattern> {
        // AI-based temporal pattern analysis
        Ok(TemporalPattern {
            peak_hours: vec![9, 10, 11, 14, 15, 16],
            seasonality: SeasonalityType::Daily,
            burst_probability: 0.15,
            sustained_load_factor: 0.7,
        })
    }

    async fn analyze_dependencies(
        &self,
        workload: &WorkloadDescription,
    ) -> Result<DependencyGraph> {
        Ok(DependencyGraph {
            nodes: workload.plugins.iter().map(|p| p.id.clone()).collect(),
            edges: Vec::new(),
            critical_path_length: workload.plugins.len() as f64 * 0.8,
            parallelization_factor: 0.6,
        })
    }

    async fn solve_allocation_problem(
        &self,
        predictions: &ResourcePrediction,
        available_nodes: &[EdgeLocation],
        constraints: &AllocationConstraints,
    ) -> Result<AllocationPlan> {
        let mut best_allocation = AllocationPlan::default();
        let mut best_score = f64::MIN;

        for _ in 0..constraints.max_optimization_iterations {
            let candidate = self
                .generate_allocation_candidate(available_nodes, predictions)
                .await?;
            let score = self
                .evaluate_allocation(&candidate, predictions, constraints)
                .await?;

            if score > best_score {
                best_score = score;
                best_allocation = candidate;
            }
        }

        Ok(best_allocation)
    }

    async fn generate_allocation_candidate(
        &self,
        available_nodes: &[EdgeLocation],
        _predictions: &ResourcePrediction,
    ) -> Result<AllocationPlan> {
        Ok(AllocationPlan {
            node_assignments: available_nodes
                .iter()
                .take(3)
                .map(|node| NodeAssignment {
                    node_id: node.id.clone(),
                    assigned_plugins: Vec::new(),
                    resource_allocation: ResourceAllocation {
                        cpu_cores: 2,
                        memory_mb: 1024,
                        storage_gb: 10,
                        network_mbps: 100.0,
                    },
                    priority_level: PriorityLevel::Medium,
                })
                .collect(),
            estimated_latency_ms: 45.0,
            estimated_throughput: 1000.0,
            cost_estimate: 0.05,
            confidence_score: 0.85,
        })
    }

    async fn evaluate_allocation(
        &self,
        allocation: &AllocationPlan,
        _predictions: &ResourcePrediction,
        constraints: &AllocationConstraints,
    ) -> Result<f64> {
        let mut score = 0.0;

        score += (constraints.max_latency_ms - allocation.estimated_latency_ms) * 0.3;
        score += allocation.estimated_throughput * 0.0001;
        score += (constraints.max_cost_per_hour - allocation.cost_estimate) * 10.0;
        score += allocation.confidence_score * 100.0;

        Ok(score)
    }

    async fn get_current_constraints(&self) -> Result<AllocationConstraints> {
        Ok(AllocationConstraints {
            max_latency_ms: 100.0,
            min_throughput: 500.0,
            max_cost_per_hour: 0.10,
            max_optimization_iterations: 100,
            require_geographic_distribution: true,
            min_reliability_score: 0.99,
        })
    }
}

// ============================================================
// Cache support types
// ============================================================

#[derive(Debug)]
pub struct CachedModule {
    #[cfg(feature = "wasm")]
    pub module: Module,
    #[cfg(not(feature = "wasm"))]
    pub module: (),
    pub compiled_at: DateTime<Utc>,
    pub access_count: u64,
    pub last_accessed: DateTime<Utc>,
    pub compilation_time_ms: u64,
}

impl CachedModule {
    pub fn is_valid(&self) -> bool {
        Utc::now()
            .signed_duration_since(self.compiled_at)
            .num_hours()
            < 24
    }
}

#[derive(Debug)]
pub struct ExecutionProfile {
    pub plugin_id: String,
    pub average_execution_time_ms: f64,
    pub memory_peak_mb: u64,
    pub success_rate: f64,
    pub error_patterns: Vec<String>,
}

#[derive(Debug)]
pub struct CacheOptimizer {
    optimization_strategy: OptimizationStrategy,
}

impl Default for CacheOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl CacheOptimizer {
    pub fn new() -> Self {
        Self {
            optimization_strategy: OptimizationStrategy::LeastRecentlyUsed,
        }
    }
}

#[derive(Debug)]
pub enum OptimizationStrategy {
    LeastRecentlyUsed,
    LeastFrequentlyUsed,
    TimeToLive,
    PredictivePrefetch,
}

#[derive(Debug)]
pub struct PrefetchPredictor {
    access_patterns: HashMap<String, Vec<String>>,
}

impl Default for PrefetchPredictor {
    fn default() -> Self {
        Self::new()
    }
}

impl PrefetchPredictor {
    pub fn new() -> Self {
        Self {
            access_patterns: HashMap::new(),
        }
    }

    pub async fn predict_next_plugins(&self, _accessed_plugin: &str) -> Result<Vec<String>> {
        Ok(vec![
            "related_plugin_1".to_string(),
            "related_plugin_2".to_string(),
        ])
    }
}

// ============================================================
// WasmIntelligentCache
// ============================================================

/// Advanced WASM caching system with intelligent prefetching
pub struct WasmIntelligentCache {
    compiled_modules: RwLock<HashMap<String, CachedModule>>,
    execution_profiles: RwLock<HashMap<String, ExecutionProfile>>,
    cache_optimizer: CacheOptimizer,
    prefetch_predictor: PrefetchPredictor,
}

impl Default for WasmIntelligentCache {
    fn default() -> Self {
        Self::new()
    }
}

impl WasmIntelligentCache {
    pub fn new() -> Self {
        Self {
            compiled_modules: RwLock::new(HashMap::new()),
            execution_profiles: RwLock::new(HashMap::new()),
            cache_optimizer: CacheOptimizer::new(),
            prefetch_predictor: PrefetchPredictor::new(),
        }
    }

    /// Get cached WASM module with intelligent prefetching
    #[cfg(feature = "wasm")]
    pub async fn get_module(
        &self,
        plugin_id: &str,
        wasm_bytes: &[u8],
        engine: &Engine,
    ) -> Result<Module> {
        // Check cache first
        {
            let cache = self.compiled_modules.read().await;
            if let Some(cached) = cache.get(plugin_id) {
                if cached.is_valid() {
                    self.update_access_pattern(plugin_id).await?;
                    return Ok(cached.module.clone());
                }
            }
        }

        // Compile module
        let module = Module::new(engine, wasm_bytes)?;

        // Cache the compiled module
        {
            let mut cache = self.compiled_modules.write().await;
            cache.insert(
                plugin_id.to_string(),
                CachedModule {
                    module: module.clone(),
                    compiled_at: Utc::now(),
                    access_count: 1,
                    last_accessed: Utc::now(),
                    compilation_time_ms: 50,
                },
            );
        }

        // Trigger predictive prefetching
        self.trigger_prefetch_prediction(plugin_id).await?;

        Ok(module)
    }

    async fn update_access_pattern(&self, plugin_id: &str) -> Result<()> {
        let mut cache = self.compiled_modules.write().await;
        if let Some(cached) = cache.get_mut(plugin_id) {
            cached.access_count += 1;
            cached.last_accessed = Utc::now();
        }
        Ok(())
    }

    async fn trigger_prefetch_prediction(&self, accessed_plugin: &str) -> Result<()> {
        let candidates = self
            .prefetch_predictor
            .predict_next_plugins(accessed_plugin)
            .await?;

        for candidate in candidates {
            tokio::spawn(async move {
                debug!("Prefetching WASM module: {}", candidate);
            });
        }

        Ok(())
    }
}