research-article

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Authors:

Zdenek BecvarAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 19, Issue 3

Pages 1628 - 1656

https://doi.org/10.1109/COMST.2017.2682318

Published: 01 July 2017 Publication History

Abstract

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

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Mobile Edge Computing: A Survey on Architecture and Computation Offloading

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cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 19, Issue 3

thirdquarter 2017

647 pages

ISSN:1553-877X

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1553-877X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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Published: 01 July 2017

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Nujhat NHaque Shanta FSarker SRoy PRazzaque MMamun-Or-Rashid MHassan MFortino G(2024)Task offloading exploiting grey wolf optimization in collaborative edge computingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00570-z13:1Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1186/s13677-023-00570-z
Rahmath P HSrivastava VChaurasia KPacheco RCouto R(2024)Early-Exit Deep Neural Network - A Comprehensive SurveyACM Computing Surveys10.1145/369876757:3(1-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3698767
Zhao YHe SLi QPei YMa Y(2024)MobileEdgeSim: A Tool for Simulating Microservice-Oriented Mobile Edge ComputingProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671407(491-494)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671407
Vali AAzizi SShojafar M(2024)RESP: A Recursive Clustering Approach for Edge Server Placement in Mobile Edge ComputingACM Transactions on Internet Technology10.1145/366609124:3(1-25)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1145/3666091
Golpayegani FChen NAfraz NGyamfi EMalekjafarian ASchäfer DKrupitzer C(2024)Adaptation in Edge Computing: A Review on Design Principles and Research ChallengesACM Transactions on Autonomous and Adaptive Systems10.1145/366420019:3(1-43)Online publication date: 30-Sep-2024
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Keizer NAscigil OKról MKutscher DPavlou G(2024)A Survey on Content Retrieval on the Decentralised WebACM Computing Surveys10.1145/364913256:8(1-39)Online publication date: 26-Apr-2024
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Al-Atat GDatta PMoharir SChampati JPapavassiliou SSchmid S(2024)Regret Bounds for Online Learning for Hierarchical InferenceProceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3641512.3686392(281-290)Online publication date: 14-Oct-2024
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Wang RLin FGanesan DLane NShi W(2024)Turbocharge Speech Understanding with Pilot InferenceProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690694(1299-1313)Online publication date: 4-Dec-2024
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Cang YChen MHuang K(2024)Joint Batching and Scheduling for High-Throughput Multiuser Edge AI With Asynchronous Task ArrivalsIEEE Transactions on Wireless Communications10.1109/TWC.2024.340481123:10_Part_2(13782-13795)Online publication date: 1-Oct-2024
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