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

Carlos Guerrero, Isaac Lera, Carlos Juiz

TL;DR

The paper tackles fog service placement by framing it as a three-objective optimization: minimize free resources, maximize service replication spread, and minimize network latency. It adapts three evolutionary algorithms (WSGA, NSGA-II, MOEA/D) to a common representation of service allocations on a Barabási–Albert fog network and evaluates them on 100 devices with 100–200 services. Results show NSGA-II yields the best objective performance and the most diverse Pareto fronts, MOEA/D achieves substantially faster convergence, and WSGA offers no added benefit. These findings highlight the trade-off between solution quality and runtime, and suggest potential for hybrid approaches and broader applicability to multi-level fog architectures.

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.

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

TL;DR

The paper tackles fog service placement by framing it as a three-objective optimization: minimize free resources, maximize service replication spread, and minimize network latency. It adapts three evolutionary algorithms (WSGA, NSGA-II, MOEA/D) to a common representation of service allocations on a Barabási–Albert fog network and evaluates them on 100 devices with 100–200 services. Results show NSGA-II yields the best objective performance and the most diverse Pareto fronts, MOEA/D achieves substantially faster convergence, and WSGA offers no added benefit. These findings highlight the trade-off between solution quality and runtime, and suggest potential for hybrid approaches and broader applicability to multi-level fog architectures.

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.
Paper Structure (23 sections, 14 equations, 6 figures, 4 tables, 3 algorithms)

This paper contains 23 sections, 14 equations, 6 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Problem statement
  4. System model
  5. Optimization model
  6. Resource usage
  7. Service spread
  8. Network latency
  9. Model constraints
  10. Evolutionary optimization
  11. Evolutionary algorithms
  12. Weighted sum genetic algorithm
  13. Non-dominated sorting genetic algorithm-II
  14. Multi-objective evolutionary algorithm based on decomposition
  15. Genetic operators and structures
  16. ...and 8 more sections

Figures (6)

  • Figure 1: An example of a fog computing architecture.
  • Figure 2: Services interoperability of the three applications.
  • Figure 3: Evolution of the weighted sum of the objectives for the best solution for each evolutionary algorithm.
  • Figure 4: Evolution of the three objectives for the best solution for each evolutionary algorithm.
  • Figure 5: Final set of solutions obtained for the experiment with 200 services.
  • ...and 1 more figures