An Adaptive Android Memory Management Based on a Lightweight PSO-LSTM Model

Shupeng Zhao, Junbo Wang, Songcan Yu, Wanbin Wang

Abstract: Nowadays, most edge devices running Android still adopt the memory reclaim scheme designed for the server, which easily leads to a large number of page re-faults in the kernel, making mobile device workloads inefficient. We hold the opinion that the amount of reclaimable pages is sequential and predictable. Therefore, we propose an adaptive lightweight memory management scheme based on prediction, which consists of two parts: a lightweight and optimal prediction model generation module (LOPMG) and an adaptive prediction-based reclaim scheme (APRS). LOPMG adopts the Particle Swarm Optimization (PSO) algorithm to generate and quantify Long-Short Term Memory (LSTM) prediction model. By APRS, the PSO-LSTM model works in the kernel and can predict allocation workloads to tune reclaim parameters dynamically. Experiments show that our PSO-LSTM model can improve the accuracy of prediction, and the reclaim scheme can significantly reduce the number of page re-faults and the time of application relaunch.

External IDs:dblp:conf/wcnc/Zhao0YW24