Genetic-based fog colony optimization hybridized with hierarchical clustering and its influence in the placement of fog services

TL;DR

This paper introduces a hybrid approach combining hierarchical clustering and genetic algorithms to optimize fog device organization into colonies, improving system performance metrics.

Contribution

It proposes a novel method using hierarchical clustering and NSGA-II to define fog colonies, enhancing communication and management efficiency.

Findings

Genetic algorithms improved network communication and application placement times.

The proposed approach outperformed control algorithms in all tested scenarios.

137 generations were sufficient for optimal solutions in worst-case scenarios.

Abstract

The organization of fog devices into fog colonies has reduced the complexity management of fog domains. One of the main influencing factors on this complexity is the large number of devices, i.e. the high scale level of the infrastructure. Fog colonies are subsets of fog devices that are managed independently from the other colonies. Thus, the number of devices involved in the management of a colony is much smaller. Previous studies have evaluated the influence of the fog colony layout on system performance metrics. We propose to use a hierarchical clustering as the base definition of the fog colony layout of the fog infrastructure. The dendrogram obtained from this hierarchical clustering includes all the colony candidates. A genetic algorithm is in charge of selecting the subset of colony candidates that optimizes the two performance metrics under study: the network communication time between users and applications, and the execution time of the algorithms that manage internally the placement of the applications in each colony. We implemented the NSGA-II, a common multi-objective approach for GAs, to evaluate our proposal. The results show that a meta-heuristic such as a GA improves the performance metrics by defining the fog colony layout through the use of the dendrogram. Nine different experiment scenarios, varying the number of applications and fog devices, were studied. In the worst of the cases, 137 generations were enough to the results of the GA dominated the solutions obtained with two control algorithms. The number of genetic solutions and their homogeneous distribution in the Pareto front were also satisfactory.