The swift proliferation of 5G networks underscores the pivotal role of Mobile Edge Computing (MEC) in catering to the ever-evolving service demands. This study introduces a dynamic resource orchestration framework tailored for MEC within 5G standalone cellular architectures. This framework addresses the intricate challenges of computational resource provisioning and power management in a holistic manner. Leveraging convex optimization principles, we devise an optimal resource allocation strategy that incorporates reliability metrics to ensure robust performance. Furthermore, we employ Deep Reinforcement Learning (DRL) techniques to tackle a formulated Markov Decision Process (MDP), aimed at optimizing resource distribution for latency minimization. Our simulation outcomes demonstrate the efficacy of the proposed approach in mitigating task delays and enhancing the system’s adaptability across diverse workload profiles and network environments, thereby contributing novel insights into the field with minimal overlap in existing literature.
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