The cloud-based application component placement problem is complex and often tackled with heuristics to find near-optimal solutions maintaining the application’s performance and avoiding resource over-provisioning. Mapping application components into virtual machines in potentially unpredictable cloud conditions is challenging and lacks performance guarantees. To address this challenge, we present an Artificial Intelligence (AI)-based resource and workload-aware mechanism, formulated as a dynamic decision-making problem solver based on Markov Decision Process (MDP). By leveraging Deep Reinforcement Learning (DRL) models, Depth-Search-First, and Dancing Links algorithms, our approach provides workload-dependent solutions ensuring keeping a balance between the application’s overall performance and the virtual machines’ resource allocation. In our study, we conducted experiments …