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Augmentation Techniques for Mobile Cloud Computing: A Taxonomy, Survey, and Future Directions

Published:04 January 2018Publication History
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Abstract

Despite the rapid growth of hardware capacity and popularity in mobile devices, limited resources in battery and processing capacity still lack the ability to meet increasing mobile users’ demands. Both conventional techniques and emerging approaches are brought together to fill this gap between user demand and mobile devices’ limited capabilities. Recent research has focused on enhancing the performance of mobile devices via augmentation techniques. Augmentation techniques for mobile cloud computing refer to the computing paradigms and solutions to outsource mobile device computation and storage to more powerful computing resources in order to enhance a mobile device’s computing capability and energy efficiency (e.g., code offloading). Adopting augmentation techniques in the heterogeneous and intermittent mobile cloud computing environment creates new challenges for computation management, energy efficiency, and system reliability. In this article, we aim to provide a comprehensive taxonomy and survey of the existing techniques and frameworks for mobile cloud augmentation regarding both computation and storage. Different from the existing taxonomies in this field, we focus on the techniques aspect, following the idea of realizing a complete mobile cloud computing system. The objective of this survey is to provide a guide on what available augmentation techniques can be adopted in mobile cloud computing systems as well as supporting mechanisms such as decision-making and fault tolerance policies for realizing reliable mobile cloud services. We also present a discussion on the open challenges and future research directions in this field.

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Index Terms

  1. Augmentation Techniques for Mobile Cloud Computing: A Taxonomy, Survey, and Future Directions

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          • Published in

            cover image ACM Computing Surveys
            ACM Computing Surveys  Volume 51, Issue 1
            January 2019
            743 pages
            ISSN:0360-0300
            EISSN:1557-7341
            DOI:10.1145/3177787
            • Editor:
            • Sartaj Sahni
            Issue’s Table of Contents

            Copyright © 2018 ACM

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            Publication History

            • Published: 4 January 2018
            • Revised: 1 October 2017
            • Accepted: 1 October 2017
            • Received: 1 January 2017
            Published in csur Volume 51, Issue 1

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