A Calibrated Relaunch Distance Framework for App Eviction in Smartphone Memory Management

Abstract

Abstract

Smartphone operating systems must eventually evict resident apps when memory becomes scarce, yet prior work has focused more on reclaim mechanisms and next app prediction than on the ranking rule that chooses the victim. We study app eviction through relaunch distance and show that generalizing raw relaunch distance prediction is unsafe as a direct policy because small errors among short returns can easily reverse victim ordering, while some resident apps still require fallback handling. Therefore, we propose a calibrated relaunch distance framework that places predicted and fallback candidates on a common scale. In trace-driven fixed capacity app cache simulation on a multi-user smartphone trace, the proposed method remains above LRU from cache capacities C=5 to C=13 on the 279-user evaluation set and improves average hit ratio from 0.8900 to 0.8935. At low cache capacity C=5, it improves hit ratio from 0.7617 to 0.7691, recovering 21.2% of the remaining Oracle–LRU gap, whereas the raw prediction method is below LRU at 0.6283 for the all-user set. The gains are strongest for users with deeper histories, where the margin at C=5 reaches +0.0138 in q4. These results show that calibration is the step that turns relaunch distance prediction into a deployable app eviction policy.