<p>Cloud computing is essential for modern enterprises, but it introduces complex cost structures related to computing resources, storage, and network traffic. Edge computing presents a viable solution to reduce these costs by processing data closer to its source, thereby minimizing transmission latency and network expenses. This paper proposes a novel service deployment strategy for edge environments that balances microservice latency and service provider costs, formulating the deployment problem as a cost-effectiveness optimization task. The approach incorporates an interaction weight factor within a directed acyclic graph (DAG) to evaluate microservice interdependence, while also introducing a cost-effectiveness optimization function. We conduct comprehensive experiments comparing various deployment strategies, including Greedy and Interaction-aware approaches, demonstrating that our Cost-Effective Simulated Annealing Algorithm (CESAA) significantly outperforms others in terms of CPU and memory utilization, microservice latency, and overall cost efficiency. The findings emphasize the importance of integrating economic considerations into microservice deployment strategies in edge computing environments. Future work will focus on enhancing load capacity and general applicability by refining resource utilization models and exploring dynamic resource allocation techniques. </p>