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 ms latency, 92.7% reliability, and 75.6 Mbps throughput with reduced energy consumption of 92.3 J, outperforming static baselines. Runtime overhead was limited to 9.6 ms with 95 KB communication per round, enabling 5,200 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.