### 连续存储分配管理实验的 Python 实现 连续存储分配是一种常见的内存管理方式，其中操作系统会将物理内存划分为多个区域来满足不同进程的需求。以下是基于最佳适配（Best Fit, BF）、首次适配（First Fit, FF）以及最差适配（Worst Fit, WF）三种经典算法的 Python 实现。 #### 数据结构设计 为了模拟内存分区的状态变化，通常使用列表作为主要的数据结构。每个分区可以用字典表示，包含起始地址、大小和状态等字段。 ```python class MemoryManager: def __init__(self, total_memory_size): self.memory = [{'start': 0, 'size': total_memory_size, 'status': 'free'}] def allocate(self, process_name, size, algorithm='FF'): allocated_partition = None if algorithm == 'FF': allocated_partition = self.first_fit(size) elif algorithm == 'BF': allocated_partition = self.best_fit(size) elif algorithm == 'WF': allocated_partition = self.worst_fit(size) if allocated_partition is not None: partition_index = self.memory.index(allocated_partition) new_partition = { 'start': allocated_partition['start'], 'size': size, 'status': f'occupied by {process_name}' } # 更新剩余部分为新分区或保持为空闲区 remaining_space = allocated_partition['size'] - size if remaining_space > 0: self.memory.insert(partition_index + 1, {'start': allocated_partition['start'] + size, 'size': remaining_space, 'status': 'free'}) self.memory[partition_index] = new_partition return True else: return False def deallocate(self, process_name): for i, part in enumerate(self.memory): if part.get('status', '').startswith(f'occupied by {process_name}'): self.memory[i]['status'] = 'free' break # 合并相邻空闲分区 free_parts = [] for idx, part in enumerate(self.memory[:]): if part['status'] == 'free': free_parts.append(idx) merged_memory = [] start_idx = 0 while start_idx < len(self.memory): if start_idx in free_parts and (start_idx + 1) in free_parts: combined_part = { 'start': self.memory[start_idx]['start'], 'size': sum([part['size'] for part in self.memory[start_idx:start_idx+2]]), 'status': 'free'} merged_memory.append(combined_part) start_idx += 2 else: merged_memory.append(self.memory[start_idx]) start_idx += 1 self.memory = merged_memory def first_fit(self, required_size): """ 首次适配 """ for part in self.memory: if part['status'] == 'free' and part['size'] >= required_size: return part return None def best_fit(self, required_size): """ 最佳适配 """ candidates = [p for p in self.memory if p['status'] == 'free' and p['size'] >= required_size] if candidates: return min(candidates, key=lambda x: x['size']) return None def worst_fit(self, required_size): """ 最差适配 """ candidates = [p for p in self.memory if p['status'] == 'free' and p['size'] >= required_size] if candidates: return max(candidates, key=lambda x: x['size']) return None def show_memory_status(self): print("\n当前内存状态:") for part in self.memory: status_str = part.get('status') or '未占用' print(f"Start Address={part['start']} Size={part['size']} Status={status_str}") ``` 此代码定义了一个 `MemoryManager` 类用于管理内存分配与释放操作[^1]。通过调整参数可以选择不同的分配策略，如首次适配 (`FF`)、最佳适配 (`BF`) 和最差适配 (`WF`)。 在具体实现中，当分配内存时如果存在多余的空间，则将其拆分成两个独立的部分；而当释放某个已占有的分区时，程序还会尝试自动合并相邻的空闲区块以提高利用率[^2]。 #### 使用示例 下面是一个简单的测试案例展示如何利用该类完成基本功能： ```python if __name__ == "__main__": manager = MemoryManager(total_memory_size=100) success = manager.allocate(process_name="P1", size=10, algorithm='FF') if success: print("成功分配给 P1") success = manager.allocate(process_name="P2", size=30, algorithm='BF') if success: print("成功分配给 P2") manager.show_memory_status() manager.deallocate("P1") print("释放 P1 的资源后...") manager.show_memory_status() ``` 运行以上脚本将会打印出每次变动后的内存布局情况，便于观察整个过程中的细节变化。 ---