Dynamic Metric-Constrained Optimization Algorithm for Edge-Cloud Collaborative Environments: Multi-Dimensional Situation Verification Modeling Under Federated SOA Services

This paper proposes a dynamic metric-constrained optimization algorithm for edge-cloud collaborative environments with multi-dimensional situation verification under federated SOA services. The framework integrates adaptive metric encoding, federated optimization, and constraint verification to ensure robust and efficient service orchestration. Experimental evaluations on Google, Alibaba and Azure cluster traces demonstrate the superiority of the proposed approach over HEFT, NSGA-II and MOEA/D. Specifically, latency violations were reduced to 5%, while other constraint breaches were maintained below 7%. SLA satisfaction consistently exceeded 88% across diverse stress conditions, peaking at 91% under node churn. Furthermore, the algorithm achieved 170[Formula: see text]ms latency, 92.7% reliability and 75.6[Formula: see text]Mbps throughput with reduced energy consumption of 92.3[Formula: see text]J, outperforming static baselines. Runtime overhead was limited to 9.6[Formula: see text]ms with 95[Formula: see text]KB communication per round, enabling 5200 decisions per second. These results confirm that the proposed framework achieves a practical balance between adaptability, efficiency and robustness, making it suitable for deployment in dynamic edge-cloud systems.