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    基于热点文件页交换和压缩预测的移动设备内存优化方法

    Memory Optimization for Mobile Devices Based on Hot File Page Swapping and Compression Prediction

    • 摘要: 摘要 移动设备应用生态的发展导致内存压力加剧,传统物理扩容方案受限,压缩内存成为主流内存扩容技术。然而,当前压缩内存系统仍存在文件页性能低下、压缩开销较高及存储资源利用率方面不足的问题,对系统性能产生了负面影响。针对上述问题,提出EC-MemOpt与APC-MSO两种优化框架。EC-MemOpt通过迁移高频访问的文件页至压缩内存,并结合压缩性预测技术,有效提高了文件页I/O性能和存储资源利用率。APC-MSO针对匿名页特性,优化了压缩交换策略,从而减少无效压缩计算开销。最后,研究构建面向混合内存页的协同管理架构(EC-MemOpt+APC-MSO),实现差异化分区管理。与OnePlus维护的QCOM 8350R内核相比,该架构在应用切换速率和空间利用率方面分别提升10.5%和9.8%。实验结果表明,提出的优化方案能显著改善移动端内存压缩系统的综合性能。

       

      Abstract: Abstract The development of the mobile application ecosystem has intensified memory pressure, while traditional physical expansion solutions are limited. As a result, compressed memory has become the mainstream technology for memory expansion. However, existing compressed memory systems still suffer from low file page performance, high compression overhead, and suboptimal storage resource utilization, negatively impacting overall system performance. To address these issues, this paper proposes two optimization frameworks: EC-MemOpt and APC-MSO. EC-MemOpt improves file page I/O performance and storage resource utilization by migrating frequently accessed file pages to compressed memory and leveraging compression predictability techniques. APC-MSO optimizes the compression swap strategy for anonymous pages, reducing unnecessary compression computation overhead. Furthermore, a collaborative management architecture for hybrid memory pages (EC-MemOpt + APC-MSO) is designed to achieve differentiated partition management. Compared with the QCOM 8350R kernel maintained by OnePlus, the proposed architecture improves application switching speed and space utilization by 10.5% and 9.8%, respectively. Experimental results demonstrate that the proposed optimization solutions significantly enhance the overall performance of compressed memory systems in mobile environments.

       

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