research-article

Leveraging User Mobility and Mobile App Services Behavior for Optimal Edge Resource Utilization

Authors:

Evangelos Psomakelis,

Konstantinos Tserpes,

Theodora VarvarigouAuthors Info & Claims

COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

Pages 7 - 12

https://doi.org/10.1145/3312614.3312620

Published: 05 May 2019 Publication History

Abstract

Edge computing has seen a great progress nowadays eliminating the network latency risks by placing data and functionality in low-end devices closer to the end user is central in application domains such as the support of the backend of mobile apps. When this problem is combined with big data requirements linked to the number of mobile app users, the optimization of edge resource utilization becomes extremely important cost- and quality-wise. This work explores approaches to model resource demand based on user mobility and application characteristics. The Application and User Context Resource Predictor (AUCORP) is introduced that performs performance analysis employing machine learning methods. The evaluation is based on real data from the deployment of a real mobile app for a large music festival.

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Cited By

Taleb TBoudi ARosa LCordeiro LTheodoropoulos TTserpes KDazzi PProtopsaltis ALi R(2023)Toward Supporting XR Services: Architecture and EnablersIEEE Internet of Things Journal10.1109/JIOT.2022.322210310:4(3567-3586)Online publication date: 15-Feb-2023
https://doi.org/10.1109/JIOT.2022.3222103
Santi NGrünblatt RFoubert BHameed AViolos JLeivadeas AMitton NChatzimisios PCoutinho RNotare M(2021)Automated and Reproducible Application Traces Generation for IoT ApplicationsProceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks10.1145/3479242.3487321(17-24)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3479242.3487321
Violos JTsanakas STheodoropoulos TLeivadeas ATserpes KVarvarigou T(2021)Hypertuming GRU Neural Networks for Edge Resource Usage Prediction2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631548(1-8)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631548
Show More Cited By

Index Terms

Leveraging User Mobility and Mobile App Services Behavior for Optimal Edge Resource Utilization
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Software and its engineering
  1. Software creation and management

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cover image ACM Other conferences

COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

May 2019

241 pages

ISBN:9781450366403

DOI:10.1145/3312614

Copyright © 2019 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: 05 May 2019

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Refereed limited

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European Unions Horizon 2020 research and innovation programme under grant agreement no. 723131 and from ICT R&D program of Korean Ministry of Science, ICT and Future Planning

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COINS '19

COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS

May 5 - 7, 2019

Crete, Greece

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Cited By

Taleb TBoudi ARosa LCordeiro LTheodoropoulos TTserpes KDazzi PProtopsaltis ALi R(2023)Toward Supporting XR Services: Architecture and EnablersIEEE Internet of Things Journal10.1109/JIOT.2022.322210310:4(3567-3586)Online publication date: 15-Feb-2023
https://doi.org/10.1109/JIOT.2022.3222103
Santi NGrünblatt RFoubert BHameed AViolos JLeivadeas AMitton NChatzimisios PCoutinho RNotare M(2021)Automated and Reproducible Application Traces Generation for IoT ApplicationsProceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks10.1145/3479242.3487321(17-24)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3479242.3487321
Violos JTsanakas STheodoropoulos TLeivadeas ATserpes KVarvarigou T(2021)Hypertuming GRU Neural Networks for Edge Resource Usage Prediction2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631548(1-8)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631548
Violos JPagoulatou TTsanakas STserpes KVarvarigou T(2021)Predicting Resource Usage in Edge Computing Infrastructures with CNN and a Hybrid Bayesian Particle Swarm Hyper-parameter Optimization ModelIntelligent Computing10.1007/978-3-030-80126-7_40(562-580)Online publication date: 7-Jul-2021
https://doi.org/10.1007/978-3-030-80126-7_40
Violos JPsomakelis EDanopoulos DTsanakas SVarvarigou T(2020)Using LSTM Neural Networks as Resource Utilization Predictors: The Case of Training Deep Learning Models on the EdgeEconomics of Grids, Clouds, Systems, and Services10.1007/978-3-030-63058-4_6(67-74)Online publication date: 5-Dec-2020
https://doi.org/10.1007/978-3-030-63058-4_6

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