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

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.