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AutoMEC: LSTM-based User Mobility Prediction for Service Management in Distributed MEC Resources

Authors:

Umberto Fattore,

Adlen KsentiniAuthors Info & Claims

MSWiM '20: Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

Pages 155 - 159

https://doi.org/10.1145/3416010.3423246

Published: 16 November 2020 Publication History

Abstract

The 5th generation of the cellular mobile communication system (5G) is in the meantime stepwise being deployed in mobile carriers' infrastructure. Various standardization tracks as well as research activity are investigating the exploitation of the very flexible 5G system architecture for customized deployments, meeting requirements of the vertical industry, such as for automotive, factory, or smart city. A very common base is a cloud-native development and decentralized deployment of the 5G system along with services in distributed resources per the Multi-Access Edge Computing (MEC) architecture to locate services topologically close to (mobile) users, e.g. along public roads, and to enable low-latency communication with local services. Automated management of such a distributed deployment in an agile environment is a prerequisite. This paper investigates the use of Recurrent Neural Networks (RNN) for accurate user mobility prediction in an automotive scenario. By the use of simulated vehicular traffic, a suitable RNN configuration using Long Short-Term Memory (LSTM) has been found, which provides accurate prediction results. Proof of value has been accomplished by an experimental decision algorithm, which balances the use of available distributed resources through service scale, migration or replication decisions while meeting mobile users' expectation on the experienced service quality.

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Cited By

Lyu XLi YHe YRen CNi WLiu RZhu PCui Q(2024)Objective-Driven Differentiable Optimization of Traffic Prediction and Resource Allocation for Split AI Inference Edge NetworksIEEE Transactions on Machine Learning in Communications and Networking10.1109/TMLCN.2024.34498312(1178-1192)Online publication date: 2024
https://doi.org/10.1109/TMLCN.2024.3449831
Wu CChiu TWang CPang A(2024)Mobility-Aware Deep Reinforcement Learning With Seq2seq Mobility Prediction for Offloading and Allocation in Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2023.332899623:6(6803-6819)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3328996
Dinani MHolzer ANguyen HMarsan MRizzo G(2024)A Gossip Learning Approach to Urban Trajectory Nowcasting for Anticipatory RAN ManagementIEEE Transactions on Mobile Computing10.1109/TMC.2023.3320551(1-17)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3320551
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Index Terms

AutoMEC: LSTM-based User Mobility Prediction for Service Management in Distributed MEC Resources
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Networks

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Published In

cover image ACM Conferences

MSWiM '20: Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2020

278 pages

ISBN:9781450381178

DOI:10.1145/3416010

General Chairs:
Monica Aguilar Igartua
Universitat Politecnica de Catalunya, Spain
,
Paolo Bellavista
University of Bologna, Italy
,
Program Chairs:
Antonio A.F. Loureiro
Federal University of Minas Gerais, Brazil
,
Carlo Giannelli
University of Ferrara, Italy

Copyright © 2020 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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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

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Short-paper

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H2020 Marie Sk?odowska-Curie Actions
H2020 ICT 2018

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MSWiM '20

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SIGSIM

MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 16 - 20, 2020

Alicante, Spain

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Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

Lyu XLi YHe YRen CNi WLiu RZhu PCui Q(2024)Objective-Driven Differentiable Optimization of Traffic Prediction and Resource Allocation for Split AI Inference Edge NetworksIEEE Transactions on Machine Learning in Communications and Networking10.1109/TMLCN.2024.34498312(1178-1192)Online publication date: 2024
https://doi.org/10.1109/TMLCN.2024.3449831
Wu CChiu TWang CPang A(2024)Mobility-Aware Deep Reinforcement Learning With Seq2seq Mobility Prediction for Offloading and Allocation in Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2023.332899623:6(6803-6819)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3328996
Dinani MHolzer ANguyen HMarsan MRizzo G(2024)A Gossip Learning Approach to Urban Trajectory Nowcasting for Anticipatory RAN ManagementIEEE Transactions on Mobile Computing10.1109/TMC.2023.3320551(1-17)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3320551
Wang CPeng JCai LPeng HLiu WGu XHuang Z(2024)AI-Enabled Spatial-Temporal Mobility Awareness Service Migration for Connected VehiclesIEEE Transactions on Mobile Computing10.1109/TMC.2023.327165523:4(3274-3290)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3271655
Filianin IBogatyrev VLesonen M(2024)Development of a Query Delay Injection System for the MEC Simulator of the LWMECPS Platform2024 International Russian Smart Industry Conference (SmartIndustryCon)10.1109/SmartIndustryCon61328.2024.10515395(384-390)Online publication date: 25-Mar-2024
https://doi.org/10.1109/SmartIndustryCon61328.2024.10515395
Belhadj AAkilal KBouchelaghem SOmar MAissani S(2024)Next-cell prediction with LSTM based on vehicle mobility for 5G mc-IoT slicesTelecommunication Systems10.1007/s11235-024-01214-6Online publication date: 16-Sep-2024
https://doi.org/10.1007/s11235-024-01214-6
Nasr Azadani MBoukerche ACoutinho RBani Younes M(2023)A Social-Aware Vehicle Path Forecasting Method using Graph Neural NetworksProceedings of the Int'l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3616392.3623419(61-68)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616392.3623419
Filianin IBogatyrev VLesonen M(2023)Develop a Lightweight MEC Platform Simulator2023 International Russian Automation Conference (RusAutoCon)10.1109/RusAutoCon58002.2023.10272771(537-542)Online publication date: 10-Sep-2023
https://doi.org/10.1109/RusAutoCon58002.2023.10272771
Araújo PLópez HFaria JSantos A(2023)Towards Mobility Management in MEC Simulation2023 IEEE 9th International Conference on Network Softwarization (NetSoft)10.1109/NetSoft57336.2023.10175403(207-211)Online publication date: 19-Jun-2023
https://doi.org/10.1109/NetSoft57336.2023.10175403
Toumi NBagaa MKsentini A(2023)Machine Learning for Service Migration: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.327312125:3(1991-2020)Online publication date: Nov-2024
https://doi.org/10.1109/COMST.2023.3273121
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