Transformer-based language models have been increasingly adopted to enhance semantic awareness in web service selection systems. However, the computational cost of large transformer encoders poses significant challenges for real-time and resource-constrained deployment scenarios. This study presents a deployment-oriented hybrid semantic–QoS framework that integrates transformer-based domain-level semantic signals with traditional Quality of Service (QoS) metrics to support scalable service selection pipelines. Rather than aiming to establish end-to-end ranking optimality, this work focuses on a comparative analysis of transformer encoders within a unified pipeline, emphasizing accuracy–latency trade-offs, resource utilization, and deployment feasibility. Four representative BERT family models—BERT, DistilBERT, RoBERTa, and ALBERT—are evaluated under identical experimental conditions. The semantic component operates at the level of domain relevance estimation, and its output is combined with QoS indicators using a controllable weighting mechanism to examine sensitivity to deployment priorities. The results reveal clear trade-offs between semantic expressiveness and computational efficiency, with lightweight models such as DistilBERT demonstrating favorable scalability and response-time characteristics despite reduced semantic capacity. The findings provide practical insights for selecting transformer encoders in QoS-aware service selection pipelines deployed in cloud, edge, or real-time environments. By framing evaluation around deployment feasibility rather than ranking optimality, this study offers guidance for balancing semantic enrichment with operational constraints in real-world service selection systems.
Rao et al. (2026) studied this question.