time_since_access = time_now - last_access

    # 根据访问模式选择目标层次
    if pattern_type == 'hot':
        # 热数据保持在GPU高速缓存
        return StorageTier.GPU_LRAM
    elif pattern_type == 'warm':
        # 温数据在GPU显存
        return StorageTier.GPU_VRAM
    elif pattern_type == 'lukewarm':
        # 微温数据在系统内存
        return StorageTier.SYSTEM_DRAM
    elif pattern_type == 'periodic':
        # 规律访问的数据保持在系统内存
        return StorageTier.SYSTEM_DRAM
    elif pattern_type == 'cold':
        # 冷数据移动到RAM缓存
        if time_since_access > self.migration_thresholds['cold_to_nvme']:
            return StorageTier.RAM_CACHE
        else:
            return StorageTier.SYSTEM_DRAM
    elif pattern_type == 'irregular':
        # 不规律访问的数据移动到RAM缓存
        return StorageTier.RAM_CACHE
    else:
        # 默认情况保持在当前层次
        return current_tier

def proactive_migration(self):
    """主动迁移策略"""
    blocks = list(self.storage_manager.memory_blocks.values())
    
    for block in blocks:
        block_id = block.block_id
        pattern = self.access_analyzer.get_access_pattern(block_id)
        current_tier = block.storage_tier
        
        # 检查是否需要提前迁移
        if pattern['prediction'] and pattern['prediction'] < time.time() + 300:  # 5分钟内会被访问
            if current_tier in [StorageTier.RAM_CACHE, StorageTier.NVME_SSD]:
                # 提前迁移到更高层次
                target_tier = StorageTier.SYSTEM_DRAM if pattern['pattern_type'] == 'warm' else StorageTier.GPU_VRAM
                asyncio.create_task(
                    self.storage_manager.move_to_tier(block_id, target_tier)
                )
                print(f"Proactive migration of {block_id} to {target_tier}")

def optimize_storage_usage(self):
    """优化存储使用"""
    # 分析存储使用情况
    stats = self.storage_manager.get_storage_stats()
    
    # 如果GPU显存使用率过高，进行垃圾回收
    gpu_vram_usage = stats['tier_usage'].get('GPU_VRAM', 0)
    if gpu_vram_usage > 40:  # 超过40GB使用
        # 找到最少使用的冷数据块
        cold_blocks = [
            block for block in stats['memory_blocks']
            if block['tier'] == 'GPU_VRAM'
        ]
        
        # 按访问频率排序
        cold_blocks.sort(key=lambda x: x['access_count'])
        
        # 回收最不活跃的块
        for block in cold_blocks[:3]:  # 回收3个最不活跃的块
            asyncio.create_task(
                self.storage_manager.move_to_tier(block['id'], StorageTier.SYSTEM_DRAM)
            )
​

使用示例

async def demo_tiered_storage():
"""演示分级存储功能"""
# 创建管理器和分析器
storage_manager = NVMeOffloadManager()
access_analyzer = AccessPatternAnalyzer()

# 创建调度器
scheduler = TieredStorageScheduler(storage_manager, access_analyzer)

try:
    # 分配一些内存块
    block1 = await storage_manager.allocate_memory_block("hot_block", 512**3)  # 512MB
    block2 = await storage_manager.allocate_memory_block("warm_block", 1024**3)  # 1GB
    block3 = await storage_manager.allocate_memory_block("cold_block", 2048**3)  # 2GB
    
    # 模拟访问模式
    for i in range(10):
        # 频繁访问block1（热数据）
        storage_manager.access_memory_block("hot_block")
        access_analyzer.record_access("hot_block")
        
        # 中等频率访问block2（温数据）
        if i % 2 == 0:
            storage_manager.access_memory_block("warm_block")
            access_analyzer.record_access("warm_block")
        
        # 偶尔访问block3（冷数据）
        if i % 5 == 0:
            storage_manager.access_memory_block("cold_block")
            access_analyzer.record_access("cold_block")
        
        await asyncio.sleep(1)
    
    # 添加调度请求
    scheduler.add_scheduling_request("hot_block", 1)
    scheduler.add_scheduling_request("warm_block", 2)
    scheduler.add_scheduling_request("cold_block", 3)
    
    # 处理调度
    scheduler.process_scheduling_queue()
    
    # 主动迁移
    scheduler.proactive_migration()
    
    # 优化存储
    scheduler.optimize_storage_usage()
    
    # 查看结果
    stats = storage_manager.get_storage_stats()
    print("\nFinal storage statistics:")
    for tier, usage in stats['tier_usage'].items():
        print(f"  {tier}: {usage:.2f}GB")
    
finally:
    await storage_manager.shutdown()
​

运行演示

asyncio.run(demo_tiered_storage())

### 2.2 缓存一致性管理

**多级缓存同步机制**

```python
class CacheConsistencyManager:
    """缓存一致性管理器"""
    
    def __init__(self):
        self.cache_versions = {}
        self.lock_manager = {}
        self.dirty_blocks = set()
    
    def lock_block(self, block_id: str, holder: str):
        """锁定数据块"""
        if block_id not in self.lock_manager:
            self.lock_manager[block_id] = {}
        
        if holder not in self.lock_manager[block_id]:
            self.lock_manager[block_id][holder] = True
            return True
        else:
            return False
    
    def unlock_block(self, block_id: str, holder: str):
        """解锁数据块"""
        if block_id in self.lock_manager and holder in self.lock_manager[block_id]:
            del self.lock_manager[block_id][holder]
            
            # 如果没有锁持有者，清理
            if not self.lock_manager[block_id]:
                del self.lock_manager[block_id]
    
    def mark_dirty(self, block_id: str):
        """标记数据块为
​