Open Access

ARTICLE

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Jaehwan Lee¹, Sangoh Park^2,*

1 Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, Korea
2 School of Computer Science and Engineering, Chung-Ang University, Seoul, 06974, Korea

* Corresponding Author: Sangoh Park. Email:

Computer Systems Science and Engineering 2023, 47(1), 171-186. https://doi.org/10.32604/csse.2023.038139

Received 28 November 2022; Accepted 24 February 2023; Issue published 26 May 2023

Abstract

Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch and address the limitations of the current least recently used (LRU) approach. However, existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again. In this paper, we present a new approach for predicting future app launches by utilizing the relaunch distance. We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction (M2ARP). M2ARP utilizes past app usage patterns to predict the relaunch distance. It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

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Keywords

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