Optimizing Energy Efficiency with QoE-Awareness in Multi-Access Edge Computing

Multi-access edge computing (MEC) brings computational resources closer to end-users by widely distributing the physical edge services, reducing end-to-end service latency at the network edge. Continuous operation of edge servers results in high energy usage and significant carbon footprints. Efficient resource management is essential for MEC sustainability. Recent solutions focus on demand response to reduce energy consumption. However, the reduction in available resources due to server shutdowns forces a degradation in the quality of service (QoS) provided to users, significantly impacting their quality of experience (QoE). Moreover, the non-linear relationship between QoS and QoE further complicates the issue. Therefore, maintaining user's QoE with energy consumption is critical to achieving sustainable edge services. To tackle these challenges, we formulate the QoE-aware energy saving (QoEES) problem and propose QESGame, a game-theoretical algorithm to solve this problem effectively and efficiently with a guaranteed convergence to Nash equilibrium. Through extensive evaluations, we demonstrate that QESGame surpasses the representative approaches by up to 20.21%, 41.62%, and 23.54% in terms of the overall system benefit, energy saving, and QoE.