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EHGA: A Genetic Algorithm Based Approach for Scheduling Tasks on Distributed Edge-Cloud Infrastructures
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0009-0007-3773-5130
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0003-4147-9487
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
2022 (English)In: Proceedings of the 2022 13th International Conference on the Network of the Future / [ed] Wautres T., Khabbaz M., Paganelli F., Idzikowski F., Zhu Z., Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022.
Keywords [en]
computation offloading, Integer programming, Scheduling, Algorithm solution, Cloud-computing, Edge clouds, Edge computing, Edge/cloud computing, Mixed integer linear programming problems, Problem solving time, Problem-solving, System delay, Task offloading, Genetic algorithms
National Category
Telecommunications
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-92698DOI: 10.1109/NoF55974.2022.9942552Scopus ID: 2-s2.0-85142931071ISBN: 978-1-6654-7254-8 (electronic)OAI: oai:DiVA.org:kau-92698DiVA, id: diva2:1717841
Conference
13th International Conference on the Network of the Future,Ghent, Belgium, October 5-7, 2022.
Available from: 2022-12-09 Created: 2022-12-09 Last updated: 2023-06-20Bibliographically approved

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Mahjoubi, AyehGrinnemo, Karl-JohanTaheri, Javid

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