CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915393800503296 |
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| author | Mahdizadeh, Soheil Rasouli, Amir Mahdi Pourashory, Mohammad Galavani, Sadra Ansari, Mohsen |
| author_facet | Mahdizadeh, Soheil Rasouli, Amir Mahdi Pourashory, Mohammad Galavani, Sadra Ansari, Mohsen |
| contents | Reducing latency in the Internet of Things (IoT) is a critical concern. While cloud computing facilitates communication, it falls short of meeting real-time requirements reliably. Edge and fog computing have emerged as viable solutions by positioning computing nodes closer to end users, offering lower latency and increased processing power. An edge-fog framework comprises various components, including edge and fog nodes, whose strategic placement is crucial as it directly impacts latency and system cost. This paper presents an effective and tunable node placement strategy based on a genetic algorithm to address the optimization problem of deploying edge and fog nodes. The main objective is to minimize latency and cost through optimal node placement. Simulation results demonstrate that the proposed framework achieves up to 2.77% latency and 31.15% cost reduction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_12445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments Mahdizadeh, Soheil Rasouli, Amir Mahdi Pourashory, Mohammad Galavani, Sadra Ansari, Mohsen Networking and Internet Architecture Hardware Architecture Distributed, Parallel, and Cluster Computing Reducing latency in the Internet of Things (IoT) is a critical concern. While cloud computing facilitates communication, it falls short of meeting real-time requirements reliably. Edge and fog computing have emerged as viable solutions by positioning computing nodes closer to end users, offering lower latency and increased processing power. An edge-fog framework comprises various components, including edge and fog nodes, whose strategic placement is crucial as it directly impacts latency and system cost. This paper presents an effective and tunable node placement strategy based on a genetic algorithm to address the optimization problem of deploying edge and fog nodes. The main objective is to minimize latency and cost through optimal node placement. Simulation results demonstrate that the proposed framework achieves up to 2.77% latency and 31.15% cost reduction. |
| title | CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments |
| topic | Networking and Internet Architecture Hardware Architecture Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2507.12445 |