云计算平台中多虚拟机内存协同优化策略研究
摘 要
随着云计算技术的快速发展，虚拟化环境下的内存资源管理成为提升系统性能和资源利用率的关键问题。针对多虚拟机环境下内存分配与协同优化的挑战，本文提出了一种基于动态预测与协作调度的内存优化策略。研究旨在通过分析虚拟机内存使用特征，设计一种能够适应负载波动并降低内存碎片化的机制。具体方法包括引入机器学习模型预测内存需求、构建跨虚拟机的内存共享池以及优化内存页置换算法。实验结果表明，该策略可有效减少平均内存等待时间达35%，同时提高整体内存利用率约20%。本研究的主要创新点在于将动态预测与协作调度相结合，实现了更精细的内存管理，并为多租户场景下的资源优化提供了新思路。研究成果对提升云计算平台的运行效率具有重要理论价值和实际应用意义。
关键词：内存优化；动态预测；协作调度
Abstract
With the rapid development of cloud computing technology, memory resource management in virtualized environments has become a critical issue for enhancing system performance and resource utilization. In response to the challenges of memory allocation and collaborative optimization in multi-VM settings, this paper proposes a memory optimization strategy based on dynamic prediction and cooperative scheduling. The study aims to analyze the memory usage characteristics of virtual machines and design a mechanism that adapts to load fluctuations while reducing memory fragmentation. Specific approaches include introducing a machine learning model to predict memory requirements, constructing a cross-VM memory sharing pool, and optimizing the memory page replacement algorithm. Experimental results demonstrate that this strategy can effectively reduce average memory waiting time by up to 35% and increase overall memory utilization by approximately 20%. The primary innovation of this research lies in combining dynamic prediction with cooperative scheduling, achieving finer-grained memory management and providing new insights for resource optimization in multi-tenant scenarios. This research holds significant theoretical value and practical application implications for improving the operational efficiency of cloud computing platforms.
Keywords: Memory Optimization；Dynamic Prediction；Collaborative Scheduling
目 录
引言 1
一、云计算平台内存管理基础 1
（一）云计算平台内存架构分析 1
（二）虚拟机内存分配机制研究 2
（三）内存管理关键技术概述 2
二、多虚拟机内存使用特性分析 2
（一）虚拟机内存负载特征提取 2
（二）内存访问模式与行为建模 3
（三）多虚拟机内存协同需求评估 3
三、内存协同优化策略设计 4
（一）基于共享页的内存优化方法 4
（二）动态内存调整算法研究 4
（三）内存压缩与去重技术应用 5
四、策略实现与性能评估 5
（一）内存协同优化系统架构设计 5
（二）实验环境与测试方案构建 5
（三）性能评估与结果分析 6
结 论 6
致 谢 8
参考文献 9
摘 要
随着云计算技术的快速发展，虚拟化环境下的内存资源管理成为提升系统性能和资源利用率的关键问题。针对多虚拟机环境下内存分配与协同优化的挑战，本文提出了一种基于动态预测与协作调度的内存优化策略。研究旨在通过分析虚拟机内存使用特征，设计一种能够适应负载波动并降低内存碎片化的机制。具体方法包括引入机器学习模型预测内存需求、构建跨虚拟机的内存共享池以及优化内存页置换算法。实验结果表明，该策略可有效减少平均内存等待时间达35%，同时提高整体内存利用率约20%。本研究的主要创新点在于将动态预测与协作调度相结合，实现了更精细的内存管理，并为多租户场景下的资源优化提供了新思路。研究成果对提升云计算平台的运行效率具有重要理论价值和实际应用意义。
关键词：内存优化；动态预测；协作调度
Abstract
With the rapid development of cloud computing technology, memory resource management in virtualized environments has become a critical issue for enhancing system performance and resource utilization. In response to the challenges of memory allocation and collaborative optimization in multi-VM settings, this paper proposes a memory optimization strategy based on dynamic prediction and cooperative scheduling. The study aims to analyze the memory usage characteristics of virtual machines and design a mechanism that adapts to load fluctuations while reducing memory fragmentation. Specific approaches include introducing a machine learning model to predict memory requirements, constructing a cross-VM memory sharing pool, and optimizing the memory page replacement algorithm. Experimental results demonstrate that this strategy can effectively reduce average memory waiting time by up to 35% and increase overall memory utilization by approximately 20%. The primary innovation of this research lies in combining dynamic prediction with cooperative scheduling, achieving finer-grained memory management and providing new insights for resource optimization in multi-tenant scenarios. This research holds significant theoretical value and practical application implications for improving the operational efficiency of cloud computing platforms.
Keywords: Memory Optimization；Dynamic Prediction；Collaborative Scheduling
目 录
引言 1
一、云计算平台内存管理基础 1
（一）云计算平台内存架构分析 1
（二）虚拟机内存分配机制研究 2
（三）内存管理关键技术概述 2
二、多虚拟机内存使用特性分析 2
（一）虚拟机内存负载特征提取 2
（二）内存访问模式与行为建模 3
（三）多虚拟机内存协同需求评估 3
三、内存协同优化策略设计 4
（一）基于共享页的内存优化方法 4
（二）动态内存调整算法研究 4
（三）内存压缩与去重技术应用 5
四、策略实现与性能评估 5
（一）内存协同优化系统架构设计 5
（二）实验环境与测试方案构建 5
（三）性能评估与结果分析 6
结 论 6
致 谢 8
参考文献 9
