Authors

Karthick DM

Independent Researcher, USA

Jessy Thomas

Independent Researcher, USA

Abstract

Efficient task scheduling and memory management are critical factors influencing the performance, responsiveness, and energy efficiency of modern mobile operating systems. Among the dominant platforms, Android and iOS adopt fundamentally different architectural approaches to process lifecycle control, background task execution, and resource allocation. This research presents a comprehensive cross-platform analysis of task and memory management mechanisms in Android and iOS, focusing on their impact on system performance, resource utilization, and user experience. The study examines kernel-level scheduling policies, application lifecycle states, garbage collection strategies, and memory reclamation techniques such as Android’s Low Memory Killer and iOS’s aggressive background process suspension model. Experimental evaluations are conducted using benchmark applications to measure CPU utilization, memory footprint, task switching latency, and energy consumption under varying workload conditions. The analysis highlights the trade-offs between openness and strict system control, demonstrating how Android’s flexible multitasking model contrasts with iOS’s tightly regulated background execution for optimized stability and battery efficiency. Results indicate that while Android offers greater multitasking flexibility, iOS demonstrates superior memory stability and power optimization under constrained conditions. The findings provide valuable insights for developers, system architects, and researchers aiming to design performance-aware mobile applications and next-generation mobile operating systems.

Keywords

Android Operating System; iOS Architecture; Process Management; Mobile Operating Systems; Task Scheduling; Memory Management; Resource Allocation

Citation of this Article

Karthick DM, & Jessy Thomas. (2025). Cross-Platform Analysis of Task and Memory Management in Android and iOS. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 2(9), 18-24. Article DOI: https://doi.org/10.47001/JAIET/2025.209003

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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