Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum

Kefan  Wu; Abdorasoul Ghasemi; Melanie  Schranz

doi:10.5220/0013140800003890

Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum

Kefan Wu, Abdorasoul Ghasemi, Melanie Schranz

Research Centre for Computational Science and Mathematical Modelling

Lakeside Labs

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Abstract

This paper addresses the workload placement problem in the edge-fog-cloud continuum. We model the edge fog-cloud computing continuum as a multi-agent framework consisting of networked resource supply and demand agents. Inspired by the swarm intelligence behavior of the ant colony optimization, we propose a workload scheduler for the arriving demand agents to increase local resource utilization and reduce communication costs without relying on a centralized scheduler. Like the ants, the demand agents will release pheromones on the resource agent to indicate the available resources. The next arriving demand agent will most probably choose a neighbor, following the pheromone value and communication cost. The framework’s performance is evaluated in terms of local resource utilization, dependency on fog and cloud, and communication cost. We compare these metrics for the ant-inspired algorithm with random and greedy algorithms.The simulation results reveal that the proposed algorithm inspired by swarm intelligence can increase resource utilization at the edge and reduce the dependency on higher layers, while also decreasing the communication cost for the task of resource allocation

Original language	English
Title of host publication	Proceedings of the 17th International Conference on Agents and Artificial Intelligence
Subtitle of host publication	Volume 1
Place of Publication	Portugal
Publisher	SciTePress
Pages	310-317
Number of pages	8
Volume	1
ISBN (Electronic)	978-989-758-737-5
DOIs	https://doi.org/10.5220/0013140800003890
Publication status	Published - 25 Feb 2025
Event	17th International Conference on Agents and Artificial Intelligence - Porto, Portugal Duration: 23 Feb 2025 → 25 Feb 2025 Conference number: 17

Publication series

Name	International Conference on Agents and Artificial Intelligence
Publisher	ScitePress
Volume	1
ISSN (Print)	2184-433X

Conference

Conference	17th International Conference on Agents and Artificial Intelligence
Abbreviated title	ICAART 2025
Country/Territory	Portugal
City	Porto
Period	23/02/25 → 25/02/25

Bibliographical note

Funding

Funded by the European Union, project MYRTUS, by grant No. 101135183. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Funders	Funder number
European Union	101135183

Keywords

Swarm Intelligence
Edge-Fog-Cloud Continuum
Ant Colony Optimization.

Access to Document

10.5220/0013140800003890Licence: CC BY-NC-ND

Ghasemi2025VoRFinal published version, 1.18 MBLicence: CC BY-NC-ND

Cite this

Wu, K., Ghasemi, A., & Schranz, M. (2025). Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence : Volume 1 (Vol. 1, pp. 310-317). (International Conference on Agents and Artificial Intelligence; Vol. 1). SciTePress. https://doi.org/10.5220/0013140800003890

Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum. / Wu, Kefan ; Ghasemi, Abdorasoul; Schranz, Melanie .
Proceedings of the 17th International Conference on Agents and Artificial Intelligence : Volume 1. Vol. 1 Portugal: SciTePress, 2025. p. 310-317 (International Conference on Agents and Artificial Intelligence; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Wu, K, Ghasemi, A & Schranz, M 2025, Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum. in Proceedings of the 17th International Conference on Agents and Artificial Intelligence : Volume 1. vol. 1, International Conference on Agents and Artificial Intelligence, vol. 1, SciTePress, Portugal, pp. 310-317, 17th International Conference on Agents and Artificial Intelligence, Porto, Portugal, 23/02/25. https://doi.org/10.5220/0013140800003890

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abstract = "This paper addresses the workload placement problem in the edge-fog-cloud continuum. We model the edge fog-cloud computing continuum as a multi-agent framework consisting of networked resource supply and demand agents. Inspired by the swarm intelligence behavior of the ant colony optimization, we propose a workload scheduler for the arriving demand agents to increase local resource utilization and reduce communication costs without relying on a centralized scheduler. Like the ants, the demand agents will release pheromones on the resource agent to indicate the available resources. The next arriving demand agent will most probably choose a neighbor, following the pheromone value and communication cost. The framework{\textquoteright}s performance is evaluated in terms of local resource utilization, dependency on fog and cloud, and communication cost. We compare these metrics for the ant-inspired algorithm with random and greedy algorithms.The simulation results reveal that the proposed algorithm inspired by swarm intelligence can increase resource utilization at the edge and reduce the dependency on higher layers, while also decreasing the communication cost for the task of resource allocation",

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AB - This paper addresses the workload placement problem in the edge-fog-cloud continuum. We model the edge fog-cloud computing continuum as a multi-agent framework consisting of networked resource supply and demand agents. Inspired by the swarm intelligence behavior of the ant colony optimization, we propose a workload scheduler for the arriving demand agents to increase local resource utilization and reduce communication costs without relying on a centralized scheduler. Like the ants, the demand agents will release pheromones on the resource agent to indicate the available resources. The next arriving demand agent will most probably choose a neighbor, following the pheromone value and communication cost. The framework’s performance is evaluated in terms of local resource utilization, dependency on fog and cloud, and communication cost. We compare these metrics for the ant-inspired algorithm with random and greedy algorithms.The simulation results reveal that the proposed algorithm inspired by swarm intelligence can increase resource utilization at the edge and reduce the dependency on higher layers, while also decreasing the communication cost for the task of resource allocation

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Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum

Abstract

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Bibliographical note

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Keywords

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