research-article

A Dynamic Service Migration Mechanism in Edge Cognitive Computing

Authors:

Giancarlo Fortino,

Iztok HumarAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 19, Issue 2

Article No.: 30, Pages 1 - 15

https://doi.org/10.1145/3239565

Published: 03 April 2019 Publication History

Abstract

Driven by the vision of edge computing and the success of rich cognitive services based on artificial intelligence, a new computing paradigm, edge cognitive computing (ECC), is a promising approach that applies cognitive computing at the edge of the network. ECC has the potential to provide the cognition of users and network environmental information, and further to provide elastic cognitive computing services to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing. This article first introduces our architecture of the ECC and then describes its design issues in detail. Moreover, we propose an ECC-based dynamic service migration mechanism to provide insight into how cognitive computing is combined with edge computing. In order to evaluate the proposed mechanism, a practical platform for dynamic service migration is built up, where the services are migrated based on the behavioral cognition of a mobile user. The experimental results show that the proposed ECC architecture has ultra-low latency and a high user experience, while providing better service to the user, saving computing resources, and achieving a high energy efficiency.

References

[1]

C. A. Sarros, S. Diamantopoulos, S. Rene, I. Psaras, A. Lertsinsrubtavee, C. Molina-Jimenez, P. Mendes, R. Sofia, A. Sathiaseelan, G. Pavlou, J. Crowcroft, and V. Tsaoussidis. 2018. Connecting the edges: A universal, mobile-centric, and opportunistic communications architecture. IEEE Communications Magazine 56, 2 (2018), 136--143.

Digital Library

[2]

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge computing: Vision and challenges. IEEE Internet of Things Journal 3, 5 (2016), 637--646.

[3]

O. Salman, I. Elhajj, A. Kayssi, and A. Chehab. 2016. Edge computing enabling the Internet of Things. In Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT'16). 603--608.

Digital Library

[4]

M. Chen, J. Yang, X. Zhu, X. Wang, M. Liu, and J. Song. 2017. Smart home 2.0: Innovative smart home system powered by botanical IoT and emotion detection. Mobile Networks and Applications 22 (2017), 1159--1169.

Digital Library

[5]

G. Fortino, R. Gravina, W. Russo, and C. Savaglio. 2017. Modeling and simulating internet-of-things systems: A hybrid agent-oriented approach. Computing in Science and Engineering 19, 5 (2017), 68--76.

Digital Library

[6]

M. Chen, Y. Miao, Y. Hao, and K. Hwang. 2017. Narrow band Internet of things. IEEE Access 5 (2017), 20557--20577.

[7]

M. Chen and Y. Hao. 2018. Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal on Selected Areas in Communications 36, 3 (2018), 587--597.

[8]

L. Petri, O. F. Rana, J. Bignell, S. Nepal, and N. Auluck. 2017. Incentivising resource sharing in edge computing applications. In Economics of Grids, Clouds, Systems, and Services (GECON'17), C. Pham, J. Altmann, and J. Bañares (Eds.). Lecture Notes in Computer Science, Vol. 10537. Springer, 204--215.

[9]

Y. Qian, M. Chen, J. Chen, M. Hossain, and A. Alamri. 2018. Secure enforcement in cognitive internet of vehicles. IEEE IoT Journal 5, 2 (2018), 1242--1250.

[10]

M. Villari, M. Fazio, S. Dustdar, O. Rana, L. Chen, and R. Ranjan. 2017. Software defined membrane: Policy-driven edge and internet of things security. IEEE Cloud Computing 4, 4 (2017), 92--99.

[11]

L. Zhou, D. Wu, Z. Dong, and X. Li. 2017. When collaboration hugs intelligence: Content delivery over ultra-dense networks. IEEE Communications Magazine 55, 12 (2017), 91--95.

Digital Library

[12]

L. Zhou, D. Wu, J. Chen, and Z. Dong. 2018. Greening the smart cities: Energy-efficient massive content delivery via D2D communications. IEEE Transactions on Industrial Informatics 14, 4 (2018), 1626--1634.

[13]

H. Habibzadeh, A. Boggio-Dandry, Z. Qin, T. Soyata, B. Kantarci, and H. T. Mouftah. 2018. Soft sensing in smart cities: Handling 3Vs using recommender systems, machine intelligence, and data analytics. IEEE Communications Magazine 56, 2 (2018), 78--86.

Digital Library

[14]

M. Mohammadi and A. Al-Fuqaha. 2018. Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Communications Magazine 56, 2 (2018), 94--101.

Digital Library

[15]

A. Abeshu and N. Chilamkurti. 2018. Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine 56, 2 (2018), 169--175.

Digital Library

[16]

M. Chen and V. Leung. 2018. From cloud-based communications to cognition-based communications: A computing perspective. Computer Communications 128 (2018), 74--79.

[17]

Y. He, N. Zhao, and H. Yin. 2018. Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology 67, 1 (2018), 44--55.

[18]

M. Chen, Y. Hao, M. Qiu, J. Song, D. Wu, and I. Humar. 2016. Mobility-aware caching and computation offloading in 5G ultradense cellular networks. Sensors 16, 7 (2016), 974--987.

[19]

S. Zhang, S. Zhang, T. Huang, and W. Gao. 2017. Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching. IEEE Transactions on Multimedia 20, 6 (2017), 1576--1590.

[20]

M. Chen, X. Shi, Y. Zhang, D. Wu, and M. Guizani. 2017. Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data 1 (2017), 1--1.

[21]

A. Machen, S. Wang, K. Leung, B. J. Ko, and T. Salonidis. 2017. Live service migration in mobile edge clouds. IEEE Wireless Communications 25, 1 (2017), 140--147.

Digital Library

[22]

V. Medina and J. Garcia. 2014. A survey of migration mechanisms of virtual machines. ACM Computing Surveys 46, 3 (2014), 30--62.

Digital Library

[23]

K. Hwang and M. Chen. 2017. Big Data Analytics for Cloud/IoT and Cognitive Computing. Wiley, UK., 2017. ISBN: 9781119247029.

Digital Library

Cited By

Šatkauskas NVenčkauskas A(2024)Multi-Agent Dynamic Fog Service Placement ApproachFuture Internet10.3390/fi1607024816:7(248)Online publication date: 13-Jul-2024
https://doi.org/10.3390/fi16070248
Sfaxi HLahyani IYangui STorjmen M(2024)Latency-Aware and Proactive Service Placement for Edge ComputingIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337597021:4(4243-4254)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3375970
Raza IHussain SLatif S(2024)EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare2024 International Conference on Computing, Internet of Things and Microwave Systems (ICCIMS)10.1109/ICCIMS61672.2024.10690352(1-6)Online publication date: 29-Jul-2024
https://doi.org/10.1109/ICCIMS61672.2024.10690352
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A Dynamic Service Migration Mechanism in Edge Cognitive Computing

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Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 19, Issue 2

Special Issue on Fog, Edge, and Cloud Integration

May 2019

288 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3322882

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 April 2019

Accepted: 01 July 2018

Revised: 01 June 2018

Received: 01 December 2017

Published in TOIT Volume 19, Issue 2

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National Key R8DProgram of China
China National Natural Science Foundation
Slovenian Research Agency
National Natural Science Foundation of China
Director Fund of WNLO

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Cited By

Šatkauskas NVenčkauskas A(2024)Multi-Agent Dynamic Fog Service Placement ApproachFuture Internet10.3390/fi1607024816:7(248)Online publication date: 13-Jul-2024
https://doi.org/10.3390/fi16070248
Sfaxi HLahyani IYangui STorjmen M(2024)Latency-Aware and Proactive Service Placement for Edge ComputingIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337597021:4(4243-4254)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3375970
Raza IHussain SLatif S(2024)EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare2024 International Conference on Computing, Internet of Things and Microwave Systems (ICCIMS)10.1109/ICCIMS61672.2024.10690352(1-6)Online publication date: 29-Jul-2024
https://doi.org/10.1109/ICCIMS61672.2024.10690352
Walia GKumar MGill S(2024)AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future PerspectivesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333801526:1(619-669)Online publication date: Sep-2025
https://doi.org/10.1109/COMST.2023.3338015
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
Tang XFeng JHuang JXiang QXue B(2024)A lightweight and continuous dimensional emotion analysis system of facial expression recognition under complex backgroundJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.104260(104260)Online publication date: Aug-2024
https://doi.org/10.1016/j.jvcir.2024.104260
Trindade SBittencourt LFonseca N(2024)Resource management at the network edge for federated learningDigital Communications and Networks10.1016/j.dcan.2022.10.01510:3(765-782)Online publication date: Jun-2024
https://doi.org/10.1016/j.dcan.2022.10.015
Jha VTripathi P(2024)Relational regression: a cognitively-inspired method for prediction system in cognitive IoTProgress in Artificial Intelligence10.1007/s13748-024-00333-013:3(247-262)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s13748-024-00333-0
Liu ZXu X(2024)Latency-aware service migration with decision theory for Internet of Vehicles in mobile edge computingWireless Networks10.1007/s11276-022-02978-y30:5(4261-4273)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11276-022-02978-y
Ayouni SMaddeh MAl-Otaibi SAlazzam MAlturki NHajjej F(2023)Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient ConditionsJournal of Disability Research10.57197/JDR-2023-00172:2Online publication date: 2023
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