Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures

TL;DR

This paper evaluates and compares three evolutionary algorithms for optimizing service placement in fog architectures, focusing on network latency, service spread, and resource utilization.

Contribution

It provides a comprehensive comparison of WSGA, NSGA-II, and MOEA/D algorithms for fog service placement, including a new model for the problem domain.

Findings

NSGA-II achieved the best objective optimization and solution diversity.

MOEA/D was the fastest in execution times.

WSGA showed no significant advantages over the others.

Abstract

This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.