Offloading Computation to the Cloud

Year : 2025 | Volume : 03 | Issue : 02 | Page : 27 37
    By

    V. Basil Hans,

  1. Research Professor, , Department of Management & Commerce, Srinivas University, Mangalore, Karnataka, India

Abstract

Mobile devices are increasingly relied upon for complex and resource-intensive applications such as real-time video processing, augmented reality, and machine learning. However, their limited computational power, storage capacity, and battery life pose significant challenges. Computation offloading to the cloud has emerged as a promising solution to overcome these limitations by transferring demanding tasks from mobile devices to remote cloud servers. This approach enables improved performance, reduced energy consumption, and enhanced user experience. The study discusses various computation offloading models, including full, partial, and dynamic offloading, and explores the role of edge and fog computing in minimizing latency and improving real-time responsiveness. It also talks about important problems like security, network dependability, cost-effectiveness, and decision-making algorithms for the best offloading. The study concludes that integrating intelligent offloading strategies with 5G and edge computing technologies can pave the way for a new generation of efficient and adaptive mobile applications.

Keywords: Mobile cloud computing, computation offloading, edge computing, energy efficiency, latency reduction, resource management, 5G networks

[This article belongs to International Journal of Mobile Computing Technology ]

How to cite this article:
V. Basil Hans. Offloading Computation to the Cloud. International Journal of Mobile Computing Technology. 2025; 03(02):27-37.
How to cite this URL:
V. Basil Hans. Offloading Computation to the Cloud. International Journal of Mobile Computing Technology. 2025; 03(02):27-37. Available from: https://journals.stmjournals.com/ijmct/article=2025/view=232804


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Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 26/10/2025
Accepted 27/10/2025
Published 01/11/2025
Publication Time 6 Days
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