amberjs 1.17.0

// AI 性能引擎
// 基于机器学习的智能性能优化系统
// 提供性能预测、自动调优和自适应调度功能

use crate::ai::performance_predictor::PerformancePredictor;
use crate::ai::tensor_optimizer::TensorOptimizer;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::collections::VecDeque;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use tokio::sync::RwLock as AsyncRwLock;
/// 性能指标
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// CPU 使用率 (0-100)
    pub cpu_usage: f64,
    /// 内存使用量 (MB)
    pub memory_usage: f64,
    /// 堆大小 (MB)
    pub heap_size: f64,
    /// GC 时间 (ms)
    pub gc_time: f64,
    /// 执行时间 (μs)
    pub execution_time: u64,
    /// 吞吐量 (ops/sec)
    pub throughput: f64,
    /// 延迟 (μs)
    pub latency: f64,
    /// 并发任务数
    pub concurrent_tasks: u32,
    /// 时间戳
    pub timestamp: u64,
}
/// 性能预测结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePrediction {
    /// 预测的执行时间
    pub predicted_execution_time: f64,
    /// 预测的内存使用
    pub predicted_memory: f64,
    /// 预测的吞吐量
    pub predicted_throughput: f64,
    /// 预测置信度 (0-1)
    pub confidence: f64,
    /// 建议的优化参数
    pub optimization_suggestions: Vec<OptimizationSuggestion>,
}
/// 优化建议
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSuggestion {
    /// 参数名称
    pub parameter: String,
    /// 当前值
    pub current_value: f64,
    /// 建议值
    pub suggested_value: f64,
    /// 预期性能提升 (%)
    pub expected_improvement: f64,
    /// 优化类型
    pub optimization_type: OptimizationType,
}
/// 优化类型
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
    /// 内存优化
    Memory,
    /// CPU 优化
    Cpu,
    /// 并发优化
    Concurrency,
    /// JIT 优化
    Jit,
    /// 缓存优化
    Cache,
}
/// AI 性能引擎配置
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AiPerformanceEngineConfig {
    /// 预测窗口大小 (样本数)
    pub prediction_window: usize,
    /// 训练批次大小
    pub batch_size: usize,
    /// 学习率
    pub learning_rate: f64,
    /// 预测间隔 (ms)
    pub prediction_interval_ms: u64,
    /// 自动调优间隔 (ms)
    pub auto_tune_interval_ms: u64,
    /// 最小置信度阈值
    pub min_confidence: f64,
    /// 是否启用在线学习
    pub enable_online_learning: bool,
}
impl Default for AiPerformanceEngineConfig {
    fn default() -> Self {
        Self {
            prediction_window: 1000,
            batch_size: 32,
            learning_rate: 0.001,
            prediction_interval_ms: 100,
            auto_tune_interval_ms: 1000,
            min_confidence: 0.8,
            enable_online_learning: true,
        }
    }
}
/// AI 性能引擎
pub struct AiPerformanceEngine {
    /// 配置
    pub config: AiPerformanceEngineConfig,
    /// 性能指标历史
    pub metrics_history: Arc<RwLock<VecDeque<PerformanceMetrics>>>,
    /// 性能预测器
    pub predictor: Arc<Mutex<PerformancePredictor>>,
    /// 张量优化器
    pub tensor_optimizer: Arc<Mutex<TensorOptimizer>>,
    /// 预测缓存
    pub prediction_cache: Arc<Mutex<HashMap<String, PerformancePrediction>>>,
    /// 是否正在训练
    is_training: Arc<Mutex<bool>>,
    /// 训练进度
    training_progress: Arc<Mutex<f64>>,
}
impl AiPerformanceEngine {
    /// 创建新的 AI 性能引擎
    pub fn new(config: AiPerformanceEngineConfig) -> Self {
        Self {
            config: config.clone(),
            metrics_history: Arc::new(RwLock::new(VecDeque::with_capacity(
                config.prediction_window,
            ))),
            predictor: Arc::new(Mutex::new(PerformancePredictor::new(config.clone()))),
            tensor_optimizer: Arc::new(Mutex::new(TensorOptimizer::new())),
            prediction_cache: Arc::new(Mutex::new(HashMap::new())),
            is_training: Arc::new(Mutex::new(false)),
            training_progress: Arc::new(Mutex::new(0.0)),
        }
    }
    /// 记录性能指标
    pub async fn record_metrics(&self, metrics: PerformanceMetrics) {
        let mut history = self.metrics_history.write().await;
        history.push_back(metrics);
        // 保持窗口大小
        if history.len() > self.config.prediction_window {
            history.pop_front();
        }
        // 在线学习
        if self.config.enable_online_learning {
            self.train_online().await;
        }
    }
    /// 预测性能
    pub async fn predict_performance(
        &self,
    ) -> Result<PerformancePrediction, Box<dyn std::error::Error>> {
        let history: _ = self.metrics_history.read().await;
        if history.len() < 10 {
            return Err("历史数据不足，无法进行预测".into());
        }
        // 检查缓存
        let cache_key: _ = self.generate_cache_key(&history);
        let cache: _ = self.prediction_cache.lock().unwrap();
        if let Some(prediction) = cache.get(&cache_key) {
            return Ok(prediction.clone());
        }
        drop(cache);
        // 使用预测器进行预测
        let predictor: _ = self.predictor.lock().unwrap();
        let history_vec: Vec<PerformanceMetrics> = history.iter().cloned().collect();
        let prediction: _ = predictor.predict(&history_vec)?;
        // 缓存预测结果
        let mut cache = self.prediction_cache.lock().unwrap();
        cache.insert(cache_key, prediction.clone());
        Ok(prediction)
    }
    /// 自动调优
    pub async fn auto_tune(
        &self,
    ) -> Result<Vec<OptimizationSuggestion>, Box<dyn std::error::Error>> {
        let prediction: _ = self.predict_performance().await?;
        // 根据预测结果生成优化建议
        let mut suggestions = prediction.optimization_suggestions;
        // 基于历史数据进行额外优化
        let history: _ = self.metrics_history.read().await;
        let additional_suggestions: _ = self.generate_suggestions_from_history(&history)?;
        suggestions.extend(additional_suggestions);
        // 应用优化建议
        if !suggestions.is_empty() {
            self.apply_optimizations(&suggestions).await?;
        }
        Ok(suggestions)
    }
    /// 获取当前性能指标
    pub async fn get_current_metrics(&self) -> Option<PerformanceMetrics> {
        let history: _ = self.metrics_history.read().await;
        history.back().cloned()
    }
    /// 获取性能趋势
    pub async fn get_performance_trend(&self, duration: Duration) -> Vec<PerformanceMetrics> {
        let history: _ = self.metrics_history.read().await;
        let cutoff: _ = chrono::Utc::now().timestamp() as u64 - duration.as_secs();
        history
            .iter()
            .filter(|m| m.timestamp >= cutoff)
            .cloned()
            .collect()
    }
    /// 检查性能回归
    pub async fn check_performance_regression(&self, baseline: &[PerformanceMetrics]) -> bool {
        let current: _ = self.get_performance_trend(Duration::from_secs(60)).await;
        if current.is_empty() || baseline.is_empty() {
            return false;
        }
        let current_avg_latency: f64 =
            current.iter().map(|m| m.latency).sum::<f64>() / current.len() as f64;
        let baseline_avg_latency: f64 =
            baseline.iter().map(|m| m.latency).sum::<f64>() / baseline.len() as f64;
        // 如果当前延迟比基线高 20%，则认为发生回归
        current_avg_latency > baseline_avg_latency * 1.2
    }
    /// 训练模型（在线学习）
    async fn train_online(&self) {
        if *self.is_training.lock().unwrap() {
            return;
        }
        let history: _ = self.metrics_history.read().await;
        if history.len() < self.config.batch_size {
            return;
        }
        *self.is_training.lock().unwrap() = true;
        // TODO: 修复异步训练的 Send 问题
        // 异步训练
        // let predictor: _ = Arc::clone(&self.predictor);
        // let tensor_optimizer: _ = Arc::clone(&self.tensor_optimizer);
        // let progress: _ = Arc::clone(&self.training_progress);
        // let is_training: _ = Arc::clone(&self.is_training);
        // let history_data: _ = history.iter().cloned().collect::<Vec<_>();
        // tokio::spawn(async move {
        //     // 训练预测器
        //     {
        //         let mut predictor = predictor.lock().unwrap();
        //         predictor.train(&history_data).await;
        //     }
        //     // 训练张量优化器
        //     {
        //         let _optimizer_guard: _ = tensor_optimizer.lock().unwrap();
        //         // 简化的训练过程（实际实现中会使用真实数据）
        //         // TODO: 使用真实的历史数据进行训练
        //     }
        //     *progress.lock().unwrap() = 1.0;
        //     *is_training.lock().unwrap() = false;
        // });
    }
    /// 生成缓存键
    fn generate_cache_key(&self, history: &VecDeque<PerformanceMetrics>) -> String {
        // 简化的缓存键生成：使用最近 10 个指标的特征
        let recent_metrics: Vec<_> = history.iter().rev().take(10).collect();
        format!("{:?}", recent_metrics)
    }
    /// 从历史数据生成优化建议
    fn generate_suggestions_from_history(
        &self,
        history: &VecDeque<PerformanceMetrics>,
    ) -> Result<Vec<OptimizationSuggestion>, Box<dyn std::error::Error>> {
        let mut suggestions = Vec::new();
        if history.len() < 10 {
            return Ok(suggestions);
        }
        // 计算平均指标
        let avg_cpu: _ = history.iter().map(|m| m.cpu_usage).sum::<f64>() / history.len() as f64;
        let avg_memory: _ =
            history.iter().map(|m| m.memory_usage).sum::<f64>() / history.len() as f64;
        let avg_gc_time: _ = history.iter().map(|m| m.gc_time).sum::<f64>() / history.len() as f64;
        // 基于 CPU 使用率生成建议
        if avg_cpu > 80.0 {
            suggestions.push(OptimizationSuggestion {
                parameter: "max_concurrent_tasks".to_string(),
                current_value: 100.0,
                suggested_value: 80.0,
                expected_improvement: 15.0,
                optimization_type: OptimizationType::Concurrency,
            });
        } else if avg_cpu < 50.0 {
            suggestions.push(OptimizationSuggestion {
                parameter: "max_concurrent_tasks".to_string(),
                current_value: 100.0,
                suggested_value: 150.0,
                expected_improvement: 25.0,
                optimization_type: OptimizationType::Concurrency,
            });
        }
        // 基于内存使用生成建议
        if avg_memory > 1000.0 {
            suggestions.push(OptimizationSuggestion {
                parameter: "heap_size_mb".to_string(),
                current_value: 256.0,
                suggested_value: 512.0,
                expected_improvement: 20.0,
                optimization_type: OptimizationType::Memory,
            });
        }
        // 基于 GC 时间生成建议
        if avg_gc_time > 10.0 {
            suggestions.push(OptimizationSuggestion {
                parameter: "gc_interval_ms".to_string(),
                current_value: 1000.0,
                suggested_value: 500.0,
                expected_improvement: 30.0,
                optimization_type: OptimizationType::Memory,
            });
        }
        Ok(suggestions)
    }
    /// 应用优化建议
    async fn apply_optimizations(
        &self,
        suggestions: &[OptimizationSuggestion],
    ) -> Result<(), Box<dyn std::error::Error>> {
        for suggestion in suggestions {
            tracing::info!(
                "应用优化建议: {} = {} (当前: {}, 预期提升: {}%)",
                suggestion.parameter,
                suggestion.suggested_value,
                suggestion.current_value,
                suggestion.expected_improvement
            );
            // TODO: 实际应用优化参数
            // 这里应该调用配置管理器的更新方法
        }
        Ok(())
    }
    /// 获取训练进度
    pub fn get_training_progress(&self) -> f64 {
        *self.training_progress.lock().unwrap()
    }
    /// 重置历史数据
    pub async fn reset_history(&self) {
        let mut history = self.metrics_history.write().await;
        history.clear();
    }
}
#[cfg(test)]
mod tests {
    use super::*;
    use std::collections::BTreeMap;
    use std::sync::{Arc, Mutex};
    use std::time::{Duration, Instant};
    use tracing::{debug, error, info, warn};
    #[tokio::test]
    async fn test_record_and_predict() {
        let config: _ = AiPerformanceEngineConfig::default();
        let engine: _ = AiPerformanceEngine::new(config);
        // 记录一些指标
        for i in 0..20 {
            let metrics: _ = PerformanceMetrics {
                cpu_usage: 50.0 + i as f64,
                memory_usage: 100.0 + i as f64 * 2.0,
                heap_size: 200.0 + i as f64,
                gc_time: 5.0 + i as f64 * 0.5,
                execution_time: 1000 + i * 10,
                throughput: 10000.0 - i as f64 * 100.0,
                latency: 100.0 + i as f64,
                concurrent_tasks: 100,
                timestamp: chrono::Utc::now().timestamp() as u64,
            };
            engine.record_metrics(metrics).await;
        }
        // 预测性能
        let prediction: _ = engine.predict_performance().await.unwrap();
        println!("预测结果: {:?}", prediction);
        assert!(prediction.confidence > 0.0);
    }
    #[tokio::test]
    async fn test_auto_tune() {
        let config: _ = AiPerformanceEngineConfig::default();
        let engine: _ = AiPerformanceEngine::new(config);
        // 记录高 CPU 使用率的指标
        for _ in 0..20 {
            let metrics: _ = PerformanceMetrics {
                cpu_usage: 90.0,
                memory_usage: 1500.0,
                heap_size: 300.0,
                gc_time: 15.0,
                execution_time: 1500,
                throughput: 8000.0,
                latency: 150.0,
                concurrent_tasks: 100,
                timestamp: chrono::Utc::now().timestamp() as u64,
            };
            engine.record_metrics(metrics).await;
        }
        // 自动调优
        let suggestions: _ = engine.auto_tune().await.unwrap();
        println!("优化建议: {:?}", suggestions);
        assert!(!suggestions.is_empty());
    }
}