摘 要
随着大数据时代的到来，数据库系统面临日益增长的并发访问需求，负载均衡成为提升并行处理性能的关键问题。本研究旨在优化数据库并行处理中的负载均衡策略，以提高系统的吞吐量和响应效率。为此，提出了一种基于动态任务分配与资源预测的负载均衡算法，该算法通过实时监测节点负载状态并结合机器学习模型预测未来任务需求，实现任务在各节点间的合理分配。实验结果表明，相较于传统静态分配方法，所提算法能够显著降低系统平均响应时间，并有效减少节点间负载差异。此外，该算法在高并发场景下表现出更强的适应性和稳定性。本研究的主要贡献在于将动态调整机制与智能预测技术相结合，为复杂环境下的负载均衡提供了新的解决方案，同时为后续相关研究奠定了理论和技术基础。关键词：负载均衡; 动态任务分配; 资源预测; 机器学习; 高并发处理
Abstract
With the advent of the big data era, database systems are facing increasingly demanding requirements for concurrent access, making load balancing a critical issue for enhancing parallel processing performance. This study focuses on optimizing load balancing strategies in database parallel processing to improve system throughput and response efficiency. To achieve this, a load balancing algorithm based on dynamic task allocation and resource prediction is proposed. The algorithm monitors node load status in real-time and integrates machine learning models to predict future task demands, thereby enabling rational task distribution across nodes. Experimental results demonstrate that, compared with traditional static allocation methods, the proposed algorithm significantly reduces the average system response time and effectively minimizes load disparities among nodes. Furthermore, the algorithm exhibits superior adaptability and stability under high-concurrency scenarios. The primary contribution of this research lies in combining dynamic adjustment mechanisms with intelligent prediction technologies, offering a novel solution for load balancing in complex environments while establishing a theoretical and technical foundation for subsequent related studies.
Key words:Load Balancing; Dynamic Task Allocation; Resource Prediction; Machine Learning; High Concurrency Processing
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、数据库并行处理基础 2
1.1、并行处理的基本概念 2
1.2、负载均衡的核心意义 2
1.3、当前研究的主要挑战 3
第2章、负载均衡优化的关键技术 4
2.1、分布式任务分配策略 4
2.2、动态负载调整机制 4
2.3、性能评估与反馈优化 5
第3章、优化算法的设计与实现 6
3.1、算法设计的目标与原则 6
3.2、基于预测的负载分配模型 6
3.3、实验环境与算法验证 7
第4章、实际应用与效果分析 8
4.1、典型场景的应用案例 8
4.2、负载均衡优化的实际收益 8
4.3、未来改进方向探讨 8
结 论 10
参考文献 11
