With the rapid expansion of service‑oriented architectures and the shift of software systems toward cloud and cloud‑native environments, the optimal placement of service‑based components has become a critical challenge for improving service quality, reducing latency, and enhancing resource efficiency. This research introduces an intelligent and adaptive approach for component placement in cloud environments that leverages data‑driven clustering and evolutionary optimization to enable more informed decision‑making compared to classical methods.



In the proposed method, the logical topology of service‑based components is first derived using the K‑means clustering algorithm based on their communication patterns. Similarly, the physical topology of computational nodes is clustered according to workload characteristics and response times. Subsequently, the mapping and placement of components onto computational nodes are optimized adaptively using a genetic algorithm that considers the number of replicas, communication delays, and resource capacities. The objective of this optimization process is to minimize end‑to‑end latency, improve the execution time of service‑based applications, and increase the overall efficiency of the system.



The performance of the proposed approach is evaluated through simulations conducted in the CloudSim environment under various scenarios. The results demonstrate that the presented method achieves significant improvements in latency, makespan, and execution time compared to conventional algorithms. These findings highlight the capability of the proposed approach to support intelligent and efficient deployment of service‑based components in dynamic and scalable cloud environments