A Robust and Adaptive Service Recommendation Framework for Distinct Behavioral Services in Dynamic Multi-Cloud Environments

Effective service recommendation is essential in multi-cloud environments, directly influencing system performance, resource utilization, and service composition quality. However, existing methods often overlook key issues: (i) the inability to identify distinct behavioral services (DBS), leading to inaccurate or inefficient recommendations; (ii) the neglect of temporal dynamics in service behaviors, which results in unstable or outdated decisions; and (iii) the lack of effective strategies for handling cold-start and data sparsity, causing incomplete or biased scoring. To address these challenges, this study proposes a behavior-aware recommendation framework that explicitly integrates DBS characteristics. First, three types of DBS are defined and identified through a two-phase topology-based detection algorithm. Second, a sliding window with role-stability judgment is introduced to capture behavioral evolution and ensure consistent role labeling. Third, a Markov-based scoring model is designed to propagate compatibility scores across the invocation topology, enabling infrequently invoked services to obtain nonzero scores during early iterations. Extensive simulations demonstrate notable gains in accuracy and robustness, showing a 22.85% improvement in precision and a 41.69% improvement in F1-score, effectively mitigating cold-start and sparsity challenges.