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research-article

Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control

Authors:

Tiago Gama Rodrigues,

Hiroki Nishiyama,

Nei KatoAuthors Info & Claims

IEEE Transactions on Computers, Volume 66, Issue 5

Pages 810 - 819

https://doi.org/10.1109/TC.2016.2620469

Published: 01 May 2017 Publication History

Abstract

Due to physical limitations, mobile devices are restricted in memory, battery, processing, among other characteristics. This results in many applications that cannot be run in such devices. This problem is fixed by Edge Cloud Computing, where the users offload tasks they cannot run to cloudlet servers in the edge of the network. The main requirement of such a system is having a low Service Delay, which would correspond to a high Quality of Service. This paper presents a method for minimizing Service Delay in a scenario with two cloudlet servers. The method has a dual focus on computation and communication elements, controlling Processing Delay through virtual machine migration and improving Transmission Delay with Transmission Power Control. The foundation of the proposal is a mathematical model of the scenario, whose analysis is used on a comparison between the proposed approach and two other conventional methods; these methods have single focus and only make an effort to improve either Transmission Delay or Processing Delay, but not both. As expected, the proposal presents the lowest Service Delay in all study cases, corroborating our conclusion that a dual focus approach is the best way to tackle the Service Delay problem in Edge Cloud Computing.

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Cited By

Zhou YTong FHe S(2024)Bi-Objective Incentive Mechanism for Mobile Crowdsensing With Budget/Cost ConstraintIEEE Transactions on Mobile Computing10.1109/TMC.2022.322947023:1(223-237)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMC.2022.3229470
Qian LDong XWu MWu YZhao L(2024)Long-Term Energy Consumption Minimization in NOMA-Enabled Vehicular Edge Computing NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340499125:10(13717-13728)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3404991
Chen ZPang ZHou WWen HWen MZhao RTang T(2024)Cross-Device Radio Frequency Fingerprinting Identification Based on Domain AdaptationIEEE Transactions on Consumer Electronics10.1109/TCE.2024.335784470:1(2391-2400)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3357844
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Index Terms

Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Client-server architectures
2. Information systems
  1. Data management systems
    1. Middleware for databases
      1. Application servers
      2. Database web servers

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Reviews

Reviewer: Xiaokun Yang

One of the most important requirements for an edge/cloud computing system is task latency, especially for some specific applications such as security alarm services and actuator feedback control in the Industrial Internet of Things (IIoT). In this paper, the authors present a method to minimize service delay, which results in two physical cloudlet servers. Different from traditional methods that focus on either communication or computation delay, this paper exploits a way to improve both elements with an integration technique. Several mathematical models are further presented to evaluate the system delay as the sum of transmission delay and processing delay. Analysis results show the proposed "dual focus approach is the best way to tackle the service delay problem in edge cloud computing." Online Computing Reviews Service

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cover image IEEE Transactions on Computers

IEEE Transactions on Computers Volume 66, Issue 5

May 2017

182 pages

ISSN:0018-9340

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Copyright © 2017.

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IEEE Computer Society

United States

Publication History

Published: 01 May 2017

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Cited By

Zhou YTong FHe S(2024)Bi-Objective Incentive Mechanism for Mobile Crowdsensing With Budget/Cost ConstraintIEEE Transactions on Mobile Computing10.1109/TMC.2022.322947023:1(223-237)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMC.2022.3229470
Qian LDong XWu MWu YZhao L(2024)Long-Term Energy Consumption Minimization in NOMA-Enabled Vehicular Edge Computing NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340499125:10(13717-13728)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3404991
Chen ZPang ZHou WWen HWen MZhao RTang T(2024)Cross-Device Radio Frequency Fingerprinting Identification Based on Domain AdaptationIEEE Transactions on Consumer Electronics10.1109/TCE.2024.335784470:1(2391-2400)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3357844
Volnes EPlagemann TGoebel V(2024)To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream ProcessingIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333095326:1(670-705)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2023.3330953
Saheed YAbdulganiyu OTchakoucht T(2024)Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilitiesApplied Soft Computing10.1016/j.asoc.2024.111434155:COnline publication date: 1-Apr-2024
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Zhu MLiang MLi HLu YPang M(2023)Intelligent acceptance systems for distribution automation terminals: an overview of edge computing technologies and applicationsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00529-012:1Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1186/s13677-023-00529-0
Wu YZhang WGuan NMa Y(2023)TDTA: Topology-Based Real-Time DAG Task Allocation on Identical Multiprocessor PlatformsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.331029434:11(2895-2909)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TPDS.2023.3310294
Huang XHuang TZhang WYeo CZhao SZhang G(2023)Pricing Optimization in MEC Systems: Maximizing Resource Utilization Through Joint Server Configuration and Dynamic OperationIEEE Transactions on Mobile Computing10.1109/TMC.2023.331533423:5(5863-5879)Online publication date: 14-Sep-2023
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Zhang QYu H(2023)Monte Carlo‐based service migration under multiple constraints in mobile edge computingIET Communications10.1049/cmu2.1270518:1(28-39)Online publication date: 17-Dec-2023
https://dl.acm.org/doi/10.1049/cmu2.12705
Li CZhang QLuo Y(2023)A jointly non-cooperative game-based offloading and dynamic service migration approach in mobile edge computingKnowledge and Information Systems10.1007/s10115-022-01822-165:5(2187-2223)Online publication date: 16-Jan-2023
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