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Task Allocation in Industrial Edge Networks with Particle Swarm Optimization and Deep Reinforcement Learning

Authors:

Philippe Buschmann,

Mostafa H. M. Shorim,

Georg CarleAuthors Info & Claims

IoT '22: Proceedings of the 12th International Conference on the Internet of Things

Pages 239 - 247

https://doi.org/10.1145/3567445.3571114

Published: 05 January 2023 Publication History

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Abstract

To avoid the disadvantages of a cloud-centric infrastructure, next-generation industrial scenarios focus on using distributed edge networks. Task allocation in distributed edge networks with regards to minimizing the energy consumption is NP-hard and requires considerable computational effort to obtain optimal results with conventional algorithms like Integer Linear Programming (ILP). We extend an existing ILP problem including an ILP heuristic for multi-workflow allocation and propose a Particle Swarm Optimization (PSO) and a Deep Reinforcement Learning (DRL) algorithm. PSO and DRL outperform the ILP heuristic with a median optimality gap of and against . DRL has the lowest upper bound for the optimality gap. It performs better than PSO for problem sizes of more than 25 tasks and PSO fails to find a feasible solution for more than 60 tasks. The execution time of DRL is significantly faster with a maximum of 1 s in comparison to PSO with a maximum of 361 s. In conclusion, our experiments indicate that PSO is more suitable for smaller and DRL for larger sized task allocation problems.

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Cited By

Patsias VAmanatidis PKarampatzakis DLagkas TMichalakopoulou KNikitas A(2023)Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the LiteratureFuture Internet10.3390/fi1508025415:8(254)Online publication date: 28-Jul-2023
https://doi.org/10.3390/fi15080254
Phyo WSasithong PWuttisittikulkij LShah S(2023)Optimizing Funcitional Split in 5G Cloud RAN: A Particle Swarm Optimization ApproachTENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)10.1109/TENCON58879.2023.10322405(103-107)Online publication date: 31-Oct-2023
https://doi.org/10.1109/TENCON58879.2023.10322405
Rodrigues Vda Rosa Righi Rda Costa CZeiser FEskofier BMaier AKim D(2023)Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloudHealth and Technology10.1007/s12553-023-00753-313:3(449-472)Online publication date: 27-Apr-2023
https://doi.org/10.1007/s12553-023-00753-3

Index Terms

Task Allocation in Industrial Edge Networks with Particle Swarm Optimization and Deep Reinforcement Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms

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cover image ACM Other conferences

IoT '22: Proceedings of the 12th International Conference on the Internet of Things

November 2022

259 pages

ISBN:9781450396653

DOI:10.1145/3567445

Copyright © 2022 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 the author(s) 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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Publication History

Published: 05 January 2023

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H2020 Industrial Leadership

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IoT 2022

IoT 2022: The 12th International Conference on the Internet of Things

November 7 - 10, 2022

Delft, Netherlands

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Overall Acceptance Rate 28 of 84 submissions, 33%

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Cited By

Patsias VAmanatidis PKarampatzakis DLagkas TMichalakopoulou KNikitas A(2023)Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the LiteratureFuture Internet10.3390/fi1508025415:8(254)Online publication date: 28-Jul-2023
https://doi.org/10.3390/fi15080254
Phyo WSasithong PWuttisittikulkij LShah S(2023)Optimizing Funcitional Split in 5G Cloud RAN: A Particle Swarm Optimization ApproachTENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)10.1109/TENCON58879.2023.10322405(103-107)Online publication date: 31-Oct-2023
https://doi.org/10.1109/TENCON58879.2023.10322405
Rodrigues Vda Rosa Righi Rda Costa CZeiser FEskofier BMaier AKim D(2023)Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloudHealth and Technology10.1007/s12553-023-00753-313:3(449-472)Online publication date: 27-Apr-2023
https://doi.org/10.1007/s12553-023-00753-3

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