EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures

Ayeh Mahjoubi; Karl Johan Grinnemo; Javid Taheri

doi:10.1109/NoF55974.2022.9942552

EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures

Ayeh Mahjoubi, Karl Johan Grinnemo, Javid Taheri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Due to cloud computing's limitations, edge computing has emerged to address computation-intensive and time-sensitive applications. In edge computing, users can offload their tasks to edge servers. However, the edge servers' resources are limited, making task scheduling everything but easy. In this paper, we formulate the scheduling of tasks between the user equipment, the edge, and the cloud as a Mixed-Integer Linear Programming (MILP) problem that aims to minimize the total system delay. To solve this MILP problem, we propose an Enhanced Healed Genetic Algorithm solution (EHGA). The results with EHGA are compared with those of CPLEX and a few heuristics previously proposed by us. The results indicate that EHGA is more accurate and reliable than the heuristics and Quicker than CPLEX at solving the MILP problem.

Original language	English
Title of host publication	Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022
Editors	Tim Wautres, Maurice Khabbaz, Federica Paganelli, Filip Idzikowski, Zuqing Zhu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	5
ISBN (Electronic)	9781665472548
ISBN (Print)	9781665472555
DOIs	https://doi.org/10.1109/NoF55974.2022.9942552
Publication status	Published - 14 Nov 2022
Externally published	Yes
Event	13th International Conference on the Network of the Future, NoF 2022 - Ghent, Belgium Duration: 05 Oct 2022 → 07 Oct 2022

Publication series

Name	Proceedings of the International Conference on the Network of the Future
ISSN (Print)	2377-8652
ISSN (Electronic)	2833-0072

Conference

Conference	13th International Conference on the Network of the Future, NoF 2022
Country/Territory	Belgium
City	Ghent
Period	05/10/2022 → 07/10/2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

edge/cloud computing
genetic algorithm
problem solving time
system delay
task offloading

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1109/NoF55974.2022.9942552Licence: Unspecified

Cite this

Mahjoubi, A., Grinnemo, K. J., & Taheri, J. (2022). EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures. In T. Wautres, M. Khabbaz, F. Paganelli, F. Idzikowski, & Z. Zhu (Eds.), Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022 (Proceedings of the International Conference on the Network of the Future). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NoF55974.2022.9942552

Mahjoubi, Ayeh ; Grinnemo, Karl Johan ; Taheri, Javid. / EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures. Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022. editor / Tim Wautres ; Maurice Khabbaz ; Federica Paganelli ; Filip Idzikowski ; Zuqing Zhu. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the International Conference on the Network of the Future).

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abstract = "Due to cloud computing's limitations, edge computing has emerged to address computation-intensive and time-sensitive applications. In edge computing, users can offload their tasks to edge servers. However, the edge servers' resources are limited, making task scheduling everything but easy. In this paper, we formulate the scheduling of tasks between the user equipment, the edge, and the cloud as a Mixed-Integer Linear Programming (MILP) problem that aims to minimize the total system delay. To solve this MILP problem, we propose an Enhanced Healed Genetic Algorithm solution (EHGA). The results with EHGA are compared with those of CPLEX and a few heuristics previously proposed by us. The results indicate that EHGA is more accurate and reliable than the heuristics and Quicker than CPLEX at solving the MILP problem.",

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Mahjoubi, A, Grinnemo, KJ & Taheri, J 2022, EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures. in T Wautres, M Khabbaz, F Paganelli, F Idzikowski & Z Zhu (eds), Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022. Proceedings of the International Conference on the Network of the Future, Institute of Electrical and Electronics Engineers Inc., 13th International Conference on the Network of the Future, NoF 2022, Ghent, Belgium, 05/10/2022. https://doi.org/10.1109/NoF55974.2022.9942552

EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures. / Mahjoubi, Ayeh; Grinnemo, Karl Johan; Taheri, Javid.
Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022. ed. / Tim Wautres; Maurice Khabbaz; Federica Paganelli; Filip Idzikowski; Zuqing Zhu. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the International Conference on the Network of the Future).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures

AU - Mahjoubi, Ayeh

AU - Grinnemo, Karl Johan

AU - Taheri, Javid

PY - 2022/11/14

Y1 - 2022/11/14

N2 - Due to cloud computing's limitations, edge computing has emerged to address computation-intensive and time-sensitive applications. In edge computing, users can offload their tasks to edge servers. However, the edge servers' resources are limited, making task scheduling everything but easy. In this paper, we formulate the scheduling of tasks between the user equipment, the edge, and the cloud as a Mixed-Integer Linear Programming (MILP) problem that aims to minimize the total system delay. To solve this MILP problem, we propose an Enhanced Healed Genetic Algorithm solution (EHGA). The results with EHGA are compared with those of CPLEX and a few heuristics previously proposed by us. The results indicate that EHGA is more accurate and reliable than the heuristics and Quicker than CPLEX at solving the MILP problem.

AB - Due to cloud computing's limitations, edge computing has emerged to address computation-intensive and time-sensitive applications. In edge computing, users can offload their tasks to edge servers. However, the edge servers' resources are limited, making task scheduling everything but easy. In this paper, we formulate the scheduling of tasks between the user equipment, the edge, and the cloud as a Mixed-Integer Linear Programming (MILP) problem that aims to minimize the total system delay. To solve this MILP problem, we propose an Enhanced Healed Genetic Algorithm solution (EHGA). The results with EHGA are compared with those of CPLEX and a few heuristics previously proposed by us. The results indicate that EHGA is more accurate and reliable than the heuristics and Quicker than CPLEX at solving the MILP problem.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 5 October 2022 through 7 October 2022

ER -

Mahjoubi A, Grinnemo KJ, Taheri J. EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures. In Wautres T, Khabbaz M, Paganelli F, Idzikowski F, Zhu Z, editors, Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the International Conference on the Network of the Future). doi: 10.1109/NoF55974.2022.9942552

EHGA: a genetic algorithm based approach for scheduling tasks on distributed edge-cloud infrastructures

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this