Authors

Nikhil Varghese

Electronics and Communication Engineering Department, IES College of Engineering, Thrissur-Kerala, India

Abstract

Process management is a fundamental component of mobile operating systems, directly influencing application responsiveness, memory utilization, power efficiency, and overall system stability. With billions of devices operating on Android and iOS platforms worldwide, understanding their process lifecycle models and resource allocation strategies is essential for improving user experience and device performance. This paper presents a comprehensive comparative analysis of process management mechanisms implemented in Android and iOS. The study examines core architectural components including task scheduling, memory management, inter-process communication, background execution policies, and energy optimization strategies. Android, built upon the Linux kernel, employs priority-based process classification, dynamic memory reclamation, and flexible multitasking to accommodate diverse hardware ecosystems. In contrast, iOS utilizes a tightly controlled and sandboxed execution model with deterministic lifecycle management to enhance stability, security, and energy efficiency. The similarities and differences between the two platforms are evaluated in terms of computational efficiency, multitasking capability, and system reliability. Furthermore, emerging challenges such as increasing system complexity, AI-driven workloads, and battery optimization are discussed. The findings provide valuable insights for researchers, developers, and system architects seeking to enhance mobile operating system performance and optimize resource management strategies.

Keywords

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

Citation of this Article

Nikhil Varghese. (2025). Comparative Analysis of Process Management Mechanisms in Android and iOS for Performance and Resource Optimization. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 2(2), 16-21. Article DOI: https://doi.org/10.47001/JAIET/2025.202004

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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