Reinforcement learning driven sustainable resource and power management for the MEC

With the advent of beyond 5G applications, the execution of computationally intense tasks moves further closer to the network edge. Alongside the capabilities of a MultiAccess Edge Computing (MEC), smart decision-making considering sustainability aspects has become achievable. In this paper, a resource management technique utilizing Reinforcement Learning (RL) at the MEC is presented in order to promote power efficient solutions. CPU resources at the MEC are managed and distributed to several network services for their individual disposal. A direct relation between the CPU resources and power consumption at the MEC is proposed further establishing the need for efficient resource handling. A Soft-Actor Critic (SAC) approach is leveraged to learn the patterns for intelligent resource allocation minimizing the power expenditure. Further, two baseline algorithms, the Knapsack method and the proportional resource allocation scheme, are implemented to prove the dominance of the proposed RL-based algorithm. The results confirm that in the SAC-based RL implementation, the power consumption at the MEC server is lower compared to the two baseline algorithms. The promising results pave way for the deployment of RL-based algorithms for efficient performance, thus promoting green technologies at the MEC.