摘 要
随着大数据时代的到来，数据库系统面临着日益增长的数据处理需求，内存管理作为影响系统性能的关键因素，成为研究的热点问题。本研究旨在优化数据库内存管理策略，以提升数据访问效率和资源利用率。通过分析现有内存管理机制的不足，提出了一种基于动态分区和智能缓存淘汰的优化方法。该方法结合工作负载特征和数据访问模式，实现了内存分配的精细化控制，并引入机器学习算法以预测数据访问频率，从而提高缓存命中率。实验结果表明，所提方法在多种典型场景下显著降低了I/O开销，提升了查询响应速度，相较于传统LRU和LFU策略，平均性能提升达25%以上。本研究的创新点在于将智能化技术与内存管理相结合，为数据库系统提供了更高效的资源调度方案，其成果对实际应用具有重要参考价值。关键词：数据库内存管理; 动态分区; 智能缓存淘汰; 机器学习预测; 性能优化
Abstract
With the advent of the big data era, database systems are confronted with increasingly demanding data processing requirements. As a critical factor influencing system performance, memory management has become a focal point of research. This study aims to optimize memory management strategies in databases to enhance data access efficiency and resource utilization. By analyzing the limitations of existing memory management mechanisms, we propose an optimization approach based on dynamic partitioning and intelligent cache eviction. This method integrates workload characteristics and data access patterns to achieve fine-grained control over memory allocation and incorporates machine learning algorithms to predict data access frequencies, thereby improving cache hit rates. Experimental results demonstrate that the proposed method significantly reduces I/O overhead and accelerates query response times across various typical scenarios, achieving an average performance improvement of over 25% compared to traditional LRU and LFU strategies. The innovation of this research lies in its integration of intelligent technologies with memory management, offering a more efficient resource scheduling solution for database systems. The findings hold significant reference value for practical applications.
Key words:Database Memory Management; Dynamic Partitioning; Intelligent Cache Eviction; Machine Learning Prediction; Performance Optimization
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、数据库内存管理基础研究 2
1.1、内存管理的基本概念 2
1.2、数据库内存管理的挑战 2
1.3、当前优化策略综述 3
第2章、内存分配与释放机制优化 4
2.1、分配算法的选择与改进 4
2.2、内存碎片化问题分析 4
2.3、动态调整策略设计 5
第3章、缓存管理的优化策略 6
3.1、缓存替换算法优化 6
3.2、数据预取技术研究 6
3.3、缓存一致性保障方法 7
第4章、并发控制下的内存管理优化 8
4.1、并发环境中的内存冲突分析 8
4.2、锁机制与无锁算法的应用 8
4.3、事务管理对内存的影响 8
结 论 10
参考文献 11
