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

Authors:

Rajkumar BuyyaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 1

Article No.: 13, Pages 1 - 38

https://doi.org/10.1145/3152397

Published: 04 January 2018 Publication History

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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Goswami SGoswami RVerma G(2024)Artificial Intelligence-Based Internet of Things for Industry 5.0The Role of Cybersecurity in the Industry 5.0 Era [Working Title]10.5772/intechopen.115116Online publication date: 16-Jul-2024
https://doi.org/10.5772/intechopen.115116
Yasmin SMishra SIslam AHussain R(2024)mBAMCA : A Mobile Business Architecture for M-Commerce Application's Performance in Cloud Computing Environment2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)10.1109/ICIPTM59628.2024.10563403(1-6)Online publication date: 21-Feb-2024
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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
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL

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Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 January 2018

Accepted: 01 October 2017

Revised: 01 October 2017

Received: 01 January 2017

Published in CSUR Volume 51, Issue 1

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Cited By

Goswami SGoswami RVerma G(2024)Artificial Intelligence-Based Internet of Things for Industry 5.0The Role of Cybersecurity in the Industry 5.0 Era [Working Title]10.5772/intechopen.115116Online publication date: 16-Jul-2024
https://doi.org/10.5772/intechopen.115116
Yasmin SMishra SIslam AHussain R(2024)mBAMCA : A Mobile Business Architecture for M-Commerce Application's Performance in Cloud Computing Environment2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)10.1109/ICIPTM59628.2024.10563403(1-6)Online publication date: 21-Feb-2024
https://doi.org/10.1109/ICIPTM59628.2024.10563403
Aishwarya RMathivanan G(2024)COCAME: A Computational Offloading in Cloud Assisted Mobile Environments Structure to Enhance Performance and Energy2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence60223.2024.10463339(264-271)Online publication date: 18-Jan-2024
https://doi.org/10.1109/Confluence60223.2024.10463339
Malviya ALora CKannagi A(2024)Shaping the Future of Mobile Edge Computing with Innovative Architectures and Designs for Web3.0 over 5G/6G Networks2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT)10.1109/CSNT60213.2024.10545956(168-175)Online publication date: 6-Apr-2024
https://doi.org/10.1109/CSNT60213.2024.10545956
Grosu GNistor SCiobanu RKolodziej JPop F(2024)Computation Augmentation Techniques for Computing Continuum2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)10.1109/CCGridW63211.2024.00023(136-143)Online publication date: 6-May-2024
https://doi.org/10.1109/CCGridW63211.2024.00023
Ma PGarg SBarika M(2024)Research allocation in mobile volunteer computing systemFuture Generation Computer Systems10.1016/j.future.2024.01.015154:C(251-265)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2024.01.015
Nisa U(2024)Image augmentation approaches for small and tiny object detection in aerial images: a reviewMultimedia Tools and Applications10.1007/s11042-024-19768-7Online publication date: 1-Aug-2024
https://doi.org/10.1007/s11042-024-19768-7
Pal S(2024)Artificial Intelligence-Based IoT-Edge Environment for Industry 5.0IoT Edge Intelligence10.1007/978-3-031-58388-9_4(111-148)Online publication date: 4-Jun-2024
https://doi.org/10.1007/978-3-031-58388-9_4
Manju Bargavi STejashvar KTejashwini K(2024)Artificial Intelligence–Driven Cobots for Innovative Industry 5.0 WorkforceIntelligent Robots and Cobots10.1002/9781394198252.ch8(159-175)Online publication date: 5-Dec-2024
https://doi.org/10.1002/9781394198252.ch8
Mirusmonov MZhang GAli JLee SKim DKim J(2023)Effects of Users’ Motivation on Their Usage of Mobile Cloud ComputingInternational Journal of Human–Computer Interaction10.1080/10447318.2023.224761240:20(6167-6184)Online publication date: 27-Aug-2023
https://doi.org/10.1080/10447318.2023.2247612
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