E-PSOGA: An Enhanced Hybrid Metaheuristic for Optimal Edge-to-Cloud Placement of Services With Multi-Version Components

The evolution of edge-to-cloud networks has significantly increased the complexity of determining optimal service placement across these infrastructures, a challenge identified as an NP-complete problem. To address such problems, exact algorithms are impractical at larger scales owing to their computational demands. Heuristics exhibit faster runtimes but lower solution quality, whereas metaheuristics provide high-quality solutions at the cost of increased runtime. In this study, service placement in edge-to-cloud systems is investigated and formulated as an optimisation problem, where each service component is provided by different vendors and is available in multiple versions. The inclusion of multi-version components adds an additional layer of complexity, making the placement problem even more challenging. Specifically, this study addresses the service placement problem in Augmented Reality (AR)-and Virtual Reality (VR)-based remote repair and maintenance use cases, where service response time and system reliability are critical performance metrics. To optimise both metrics, we propose a novel hybrid metaheuristic algorithm (E-PSOGA) which combines the fast convergence of Particle Swarm Optimisation (PSO) with the global search capabilities of Genetic Algorithms (GA). A custom healing operator is also introduced to further enhance the solution quality and reduce the algorithm runtime. A comprehensive performance assessment shows that E-PSOGA reduces the response time by 37% compared with the other implemented baseline algorithms. E-PSOGA achieved 98% platform and 97% service reliability while maintaining a reasonable algorithm runtime. These results indicate that the proposed approach is well-suited for large-scale and time-sensitive scenarios requiring both computational efficiency and high solution quality.