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 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.