LLM-Assisted Service Migration and Resource Allocation for Dynamic Cloud-Edge Computing

Dynamic cloud–edge computing environments require continuous service placement and resource allocation under changing workloads, user mobility, network conditions, and heterogeneous computing resources. Conventional heuristic approaches provide efficient decision making but may lack adaptability, while deep reinforcement learning (DRL) approaches can require substantial training and computational overhead. This paper proposes an \textit{LLM-Hybrid Service Migration and Resource Allocation (SMRA)} framework that combines Large Language Model (LLM)-based candidate generation with heuristic optimization and multi-objective cost evaluation. The framework represents the current system state using workload, latency, available resources, energy consumption, bandwidth, and migration-related information, which are provided to the LLM to generate candidate migration strategies. In parallel, a heuristic module generates analytically derived candidate strategies. The feasible candidates from both modules are evaluated using a unified cost function incorporating service latency, energy consumption, and migration overhead, and the minimum-cost strategy is selected for execution. A simulation-based evaluation involving mobile users and edge servers compares the proposed framework with heuristic, DRL-based, and random baseline approaches. The reported results show reductions of approximately 29% in average service latency, 17% in energy consumption, and 22% in total system cost compared with the heuristic baseline. The findings demonstrate the potential of combining LLM-based contextual candidate generation with deterministic optimization for adaptive service management in dynamic cloud-edge environments.