Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures

2019-08-01

Future generations computer systems

AI Summary

This work addresses the multi-objective service placement optimization problem in fog computing, aiming to minimize end-to-end latency, maximize resource utilization, and accelerate convergence. We systematically evaluate three state-of-the-art multi-objective evolutionary algorithms—NSGA-II, SPEA2, and GDE3—under a unified simulation benchmark, enabling the first comprehensive cross-algorithm comparison for fog service placement. To reduce computational overhead, we propose a lightweight fitness evaluation mechanism. Experimental results demonstrate that NSGA-II achieves the best trade-off between Pareto solution quality and runtime efficiency: it reduces average end-to-end latency by 23.7% and improves node-level resource utilization by 19.4% compared to baseline approaches. Our study provides a reproducible algorithm selection guideline and an efficient optimization framework for fog-based service deployment.

Technology Category

• Search and Optimization: Evolutionary Computation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Problem

• Cloud Systems
• Bio-inspired Algorithms
• Service Optimization

Innovation

• Evolutionary Algorithms
• Fog Service Placement
• Multi-Objective Optimization