amberjs 1.17.0

// Stage 92 Phase 2: 智能内存预取系统
//
// 实现基于 AI 的预测性内存预取，根据访问模式自动预测并预取数据
// 支持顺序访问、随机访问、循环访问等多种模式

use crate::memory::{
    zero_copy_enhanced::{AccessPattern, EnhancedZeroCopy},
    SendPtr,
};
use rand::Rng;
use std::collections::HashMap;
use std::ptr::NonNull;
use std::sync::atomic::{AtomicBool, AtomicUsize, Ordering};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::{Mutex, RwLock};

/// 访问历史条目
#[derive(Debug, Clone)]
pub struct AccessHistoryEntry {
    /// 访问地址
    pub address: usize,
    /// 访问时间
    pub timestamp: Instant,
    /// 访问大小
    pub size: usize,
}
/// 访问模式识别器
#[derive(Debug, Clone)]
pub struct PatternRecognizer {
    /// 历史访问记录
    pub history: Vec<AccessHistoryEntry>,
    /// 当前识别的模式
    pub current_pattern: Option<AccessPattern>,
    /// 模式置信度 (0.0 - 1.0)
    pub confidence: f64,
    /// 模式预测准确性
    pub accuracy: f64,
}
impl PatternRecognizer {
    pub fn new() -> Self {
        Self {
            history: Vec::with_capacity(1024),
            current_pattern: None,
            confidence: 0.0,
            accuracy: 0.0,
        }
    }
    /// 记录访问
    pub fn record_access(&mut self, address: usize, size: usize) {
        let entry: _ = AccessHistoryEntry {
            address,
            timestamp: Instant::now(),
            size,
        };
        self.history.push(entry);
        // 限制历史记录大小
        if self.history.len() > 1024 {
            self.history.remove(0);
        }
        // 重新识别模式
        self.recognize_pattern();
    }
    /// 识别访问模式
    fn recognize_pattern(&mut self) {
        if self.history.len() < 10 {
            return;
        }
        let recent_accesses: _ = &self.history[self.history.len().saturating_sub(100)..];
        // 检查顺序访问模式
        let sequential_score: _ = self.calculate_sequential_score(recent_accesses);
        // 检查循环访问模式
        let cyclic_score: _ = self.calculate_cyclic_score(recent_accesses);
        // 检查随机访问模式
        let random_score: _ = self.calculate_random_score(recent_accesses);
        // 选择得分最高的模式
        let (pattern, confidence) = if sequential_score > 0.8 {
            (Some(AccessPattern::Sequential), sequential_score)
        } else if cyclic_score > 0.7 {
            (Some(AccessPattern::Sequential), cyclic_score)
        } else if random_score > 0.5 {
            (Some(AccessPattern::Random), random_score)
        } else {
            (Some(AccessPattern::Random), 0.5)
        };
        self.current_pattern = pattern;
        self.confidence = confidence;
    }
    /// 计算顺序访问得分
    fn calculate_sequential_score(&self, accesses: &[AccessHistoryEntry]) -> f64 {
        if accesses.len() < 3 {
            return 0.0;
        }
        let mut sequential_count = 0;
        let mut total_transitions = 0;
        for i in 1..accesses.len() {
            let prev: _ = &accesses[i - 1];
            let curr: _ = &accesses[i];
            // 检查是否为顺序访问 (地址递增且接近)
            let diff: _ = curr.address as isize - prev.address as isize;
            let expected_diff: _ = prev.size as isize;
            if (diff - expected_diff).abs() < (prev.size / 4) as isize {
                sequential_count += 1;
            }
            total_transitions += 1;
        }
        if total_transitions > 0 {
            sequential_count as f64 / total_transitions as f64
        } else {
            0.0
        }
    }
    /// 计算循环访问得分
    fn calculate_cyclic_score(&self, accesses: &[AccessHistoryEntry]) -> f64 {
        if accesses.len() < 5 {
            return 0.0;
        }
        // 查找重复的地址序列
        let mut sequence_counts: HashMap<Vec<usize>, usize> = HashMap::new();
        let sequence_length = 3.min(accesses.len());
        for i in 0..=accesses.len() - sequence_length {
            let sequence: Vec<usize> = accesses[i..i + sequence_length]
                .iter()
                .map(|a| a.address)
                .collect();
            *sequence_counts.entry(sequence).or_insert(0) += 1;
        }
        // 计算最高重复次数
        let max_count: _ = sequence_counts.values().max().unwrap_or(&0);
        let total_sequences: _ = accesses.len() - sequence_length + 1;
        if total_sequences > 0 {
            *max_count as f64 / total_sequences as f64
        } else {
            0.0
        }
    }
    /// 计算随机访问得分
    fn calculate_random_score(&self, accesses: &[AccessHistoryEntry]) -> f64 {
        if accesses.len() < 5 {
            return 0.0;
        }
        // 计算地址分布的方差
        let addresses: Vec<usize> = accesses.iter().map(|a| a.address).collect();
        let mean: _ = addresses.iter().sum::<usize>() as f64 / addresses.len() as f64;
        let variance: _ = addresses
            .iter()
            .map(|&addr| (addr as f64 - mean).powi(2))
            .sum::<f64>()
            / addresses.len() as f64;
        // 方差越大，随机性越强
        let normalized_variance: _ = (variance / 1_000_000_000.0).min(1.0);
        normalized_variance
    }
    /// 获取预测的下一个访问地址
    pub fn predict_next_address(&self) -> Option<usize> {
        if self.history.is_empty() {
            return None;
        }
        let last_access: _ = &self.history[self.history.len() - 1];
        match self.current_pattern {
            Some(AccessPattern::Sequential) => {
                // 顺序访问：预测下一个连续地址
                Some(last_access.address + last_access.size)
            }
            Some(AccessPattern::Random) => {
                // 随机访问：使用历史分布预测
                self.predict_random_address()
            }
            None => None,
        }
    }
    /// 预测随机地址
    fn predict_random_address(&self) -> Option<usize> {
        if self.history.len() < 5 {
            return None;
        }
        // 使用历史地址的统计特性进行预测
        let addresses: Vec<usize> = self.history.iter().map(|a| a.address).collect();
        let mean: _ = addresses.iter().sum::<usize>() as f64 / addresses.len() as f64;
        let variance: _ = addresses
            .iter()
            .map(|&addr| (addr as f64 - mean).powi(2))
            .sum::<f64>()
            / addresses.len() as f64;
        let std_dev: _ = variance.sqrt();
        // 生成基于正态分布的预测地址
        let mut rng = rand::rng();
        let prediction: _ = mean + rng.random_range(-std_dev..std_dev);
        Some(prediction as usize)
    }
}
/// 预取策略
#[derive(Debug, Clone)]
pub struct PrefetchStrategy {
    /// 预取窗口大小
    pub window_size: usize,
    /// 预取深度
    pub prefetch_depth: usize,
    /// 最小置信度阈值
    pub min_confidence: f64,
    /// 预取延迟
    pub prefetch_delay: Duration,
}
impl Default for PrefetchStrategy {
    fn default() -> Self {
        Self {
            window_size: 4096,
            prefetch_depth: 4,
            min_confidence: 0.7,
            prefetch_delay: Duration::from_micros(100),
        }
    }
}
/// 智能预取器
#[derive(Debug)]
pub struct SmartPrefetcher {
    /// 模式识别器
    recognizer: Arc<RwLock<PatternRecognizer>>,
    /// 预取策略
    strategy: PrefetchStrategy,
    /// 零拷贝系统引用
    zero_copy: Arc<EnhancedZeroCopy>,
    /// 预取队列
    prefetch_queue: Arc<Mutex<Vec<PrefetchTask>>>,
    /// 预取统计
    stats: Arc<PrefetchStats>,
    /// 是否启用预取
    enabled: Arc<AtomicBool>,
}
/// 预取任务
#[derive(Debug)]
struct PrefetchTask {
    /// 预取地址
    pub address: SendPtr,
    /// 预取大小
    pub size: usize,
    /// 优先级 (0-100, 越高越优先)
    pub priority: usize,
    /// 创建时间
    pub created_at: Instant,
}
unsafe impl Send for PrefetchTask {}
unsafe impl Sync for PrefetchTask {}
/// 预取统计
#[derive(Debug, Default)]
pub struct PrefetchStats {
    pub total_prefetch_requests: AtomicUsize,
    pub successful_prefetches: AtomicUsize,
    pub wasted_prefetches: AtomicUsize,
    pub average_confidence: AtomicUsize,
    pub pattern_accuracies: HashMap<String, AtomicUsize>,
}
impl SmartPrefetcher {
    /// 创建智能预取器
    pub fn new(zero_copy: Arc<EnhancedZeroCopy>, strategy: PrefetchStrategy) -> Self {
        Self {
            recognizer: Arc::new(RwLock::new(PatternRecognizer::new())),
            strategy,
            zero_copy,
            prefetch_queue: Arc::new(Mutex::new(Vec::new())),
            stats: Arc::new(PrefetchStats::default()),
            enabled: Arc::new(AtomicBool::new(true)),
        }
    }
    /// 记录内存访问
    pub async fn record_access(&self, address: usize, size: usize) {
        {
            let mut recognizer = self.recognizer.write().await;
            recognizer.record_access(address, size);
        }
        // 更新统计
        self.stats
            .total_prefetch_requests
            .fetch_add(1, Ordering::Relaxed);
        // 如果启用了预测性预取，触发预取
        if self.enabled.load(Ordering::Relaxed) {
            self.trigger_predictive_prefetch().await;
        }
    }
    /// 触发预测性预取
    async fn trigger_predictive_prefetch(&self) {
        let recognizer: _ = self.recognizer.read().await;
        // 检查置信度
        if recognizer.confidence < self.strategy.min_confidence {
            return;
        }
        // 获取预测的下一个访问地址
        if let Some(predicted_addr) = recognizer.predict_next_address() {
            let Some(addr) = NonNull::new(predicted_addr as *mut u8) else {
                self.stats.wasted_prefetches.fetch_add(1, Ordering::Relaxed);
                return;
            };
            // 创建预取任务
            let task: _ = PrefetchTask {
                address: SendPtr(addr),
                size: self.strategy.window_size,
                priority: (recognizer.confidence * 100.0) as usize,
                created_at: Instant::now(),
            };
            // 添加到预取队列
            {
                let mut queue = self.prefetch_queue.lock().await;
                queue.push(task);
                // 按优先级排序
                queue.sort_by(|a, b| b.priority.cmp(&a.priority));
            }
            // 异步执行预取
            self.execute_prefetch_queue().await;
        }
    }
    /// 执行预取队列
    async fn execute_prefetch_queue(&self) {
        let mut queue = self.prefetch_queue.lock().await;
        while let Some(task) = queue.pop() {
            // 检查任务是否过期
            if task.created_at.elapsed() > Duration::from_secs(1) {
                continue;
            }
            // 执行预取
            if self.execute_prefetch(&task).await {
                self.stats
                    .successful_prefetches
                    .fetch_add(1, Ordering::Relaxed);
            } else {
                self.stats.wasted_prefetches.fetch_add(1, Ordering::Relaxed);
            }
        }
    }
    /// 执行单个预取任务
    async fn execute_prefetch(&self, task: &PrefetchTask) -> bool {
        // 使用零拷贝系统进行预取
        let result: _ = self.zero_copy.smart_prefetch(task.address, task.size).await;
        match result {
            Ok(_) => true,
            Err(_) => false,
        }
    }
    /// 启用预取
    pub fn enable(&self) {
        self.enabled.store(true, Ordering::Relaxed);
    }
    /// 禁用预取
    pub fn disable(&self) {
        self.enabled.store(false, Ordering::Relaxed);
    }
    /// 获取当前访问模式
    pub async fn get_current_pattern(&self) -> Option<(AccessPattern, f64)> {
        let recognizer: _ = self.recognizer.read().await;
        recognizer
            .current_pattern
            .map(|p| (p, recognizer.confidence))
    }
    /// 获取预取统计
    pub async fn get_stats(&self) -> SmartPrefetchStatsSnapshot {
        let recognizer: _ = self.recognizer.read().await;
        SmartPrefetchStatsSnapshot {
            total_prefetch_requests: self.stats.total_prefetch_requests.load(Ordering::Relaxed),
            successful_prefetches: self.stats.successful_prefetches.load(Ordering::Relaxed),
            wasted_prefetches: self.stats.wasted_prefetches.load(Ordering::Relaxed),
            current_pattern: recognizer.current_pattern,
            confidence: recognizer.confidence,
            queue_size: self.prefetch_queue.lock().await.len(),
        }
    }
    /// 清理过期任务
    pub async fn cleanup_expired_tasks(&self) {
        let mut queue = self.prefetch_queue.lock().await;
        let now: _ = Instant::now();
        queue.retain(|task| now.duration_since(task.created_at) < Duration::from_secs(1));
    }
}
/// 智能预取统计快照
#[derive(Debug, Clone)]
pub struct SmartPrefetchStatsSnapshot {
    pub total_prefetch_requests: usize,
    pub successful_prefetches: usize,
    pub wasted_prefetches: usize,
    pub current_pattern: Option<AccessPattern>,
    pub confidence: f64,
    pub queue_size: usize,
}
impl SmartPrefetchStatsSnapshot {
    pub fn success_rate(&self) -> f64 {
        if self.total_prefetch_requests == 0 {
            0.0
        } else {
            self.successful_prefetches as f64 / self.total_prefetch_requests as f64
        }
    }
    pub fn waste_rate(&self) -> f64 {
        if self.total_prefetch_requests == 0 {
            0.0
        } else {
            self.wasted_prefetches as f64 / self.total_prefetch_requests as f64
        }
    }
}
#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_pattern_recognizer() {
        let mut recognizer = PatternRecognizer::new();
        // 测试顺序访问模式
        for i in 0..10 {
            recognizer.record_access(i * 1024, 1024);
        }
        assert!(recognizer.current_pattern.is_some());
        assert!(recognizer.confidence > 0.0);
    }
    #[tokio::test]
    async fn test_smart_prefetcher() {
        let zero_copy = Arc::new(EnhancedZeroCopy::default());
        let prefetcher: _ = SmartPrefetcher::new(zero_copy, PrefetchStrategy::default());
        // 记录一些访问
        for i in 0..5 {
            prefetcher.record_access(i * 1024, 1024).await;
        }
        let stats: _ = prefetcher.get_stats().await;
        assert!(stats.total_prefetch_requests > 0);
    }
    #[test]
    fn test_prefetch_strategy() {
        let strategy: _ = PrefetchStrategy::default();
        assert_eq!(strategy.window_size, 4096);
        assert_eq!(strategy.prefetch_depth, 4);
        assert!(strategy.min_confidence > 0.0);
    }
}