Algorithm to analyse fog colonies and service placement using genetic algorithms and hierarchical clustering
This dataset contains the experimental results generated by the hybrid optimization approach combining hierarchical clustering and a genetic algorithm for fog colony layout and service placement in cloud–fog–edge infrastructures. It includes: - multiple synthetic infrastructure scenarios with varyin...
| Autores: | , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::251f19d9d195f0a8158682ce15a76adf |
| Acceso en línea: | https://doi.org/10.34810/DATA2673 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer and Information Science clustering hierarchical fog computing |
| Sumario: | This dataset contains the experimental results generated by the hybrid optimization approach combining hierarchical clustering and a genetic algorithm for fog colony layout and service placement in cloud–fog–edge infrastructures. It includes: - multiple synthetic infrastructure scenarios with varying numbers of nodes, applications, and experimental repetitions; - CSV files representing Pareto fronts obtained for each execution, where each row corresponds to a solution (e.g., a colony layout and service assignment) with its associated objective values (e.g., service deployment time, end-to-end latency, or communication cost) and metadata (e.g., number of nodes, number of applications, random seed, and clustering strategy used); - configuration scripts (configuration.py, domainConfiguration.py) defining experimental parameters such as the number of generations, range of applications, or infrastructure size; - result folders (results/) structured by scenario and repetition, along with generated plots summarizing the optimization outcomes. |
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