## 2 Related Works  
Lately, the issue of energy efficiency has attracted considerable attention in the Mobile Edge Computing System (MECS) context [21]. In [22], the authors considered user association and power allocation in millimeter-wave (mmWave)-based ultra-dense networks while ensuring load balance and energy efficiency, harvesting energy from base stations, meeting user quality of service requirements, and managing cross-tier interference. The authors of [23] explored the optimization challenge of power control and sensing time in a cognitive small-cell network, addressing concerns such as mitigating cross-tier interference, imperfect hybrid spectrum sensing, and energy efficiency. Efficient energy-saving schemes [24; 25], extensively researched and widely acclained, stand out as one of the most prominent avenues for achieving substantial energy savings in cellular networks. Additionally, proposals for base-station sleeping (BSs) control [26; 27; 28; 29] have garnered significant attention in the pursuit of energy efficiency within this domain. However, integrating MEC with BSs significantly complicates the energy-saving issue, as the BS now provide both radio access and caching services. Additionally, because caching resources on MECSs are limited, downloading some content from the CN is inevitable. Consequently, energy consumption is intricately linked to caching capacity and MECSs' sleeping decisions over time. In this context, content popularity and caching capacity emerge as two main factors influencing the MECSs' sleeping decisions. In [30], the researchers discussed the caching deployment problem with a given wireless transmission rate while assuming fixed factors for the backhaul transmission rate, MECS storage capacity, and system energy consumption. However, base stations must account for various wireless channel states, conditions, diverse backhaul links, and system power in practical mobile networks. Thus, it is necessary and crucial for caching deployment and active MECS to consider the above three factors [31; 32]. In [33], service delay was minimized by virtual machine (VM) mitigation in SCN, and the impact of the mobility of UEs and the dense deployment of small cells were discussed. In [34], the authors analyzed the coverage performance for computation tasks within a two-tier small-cell network. Note that energy efficiency should be discussed in these works, and the service delay requirement has been neglected thus far. [PERSON] et al. [35] addressed the evolving demands of vehicular services by proposing an intelligent Internet of Vehicles (IoV) framework to minimize overall energy consumption while meeting user delay constraints. Recognizing the limitations of vehicular fog nodes in delivering satisfactory user experiences, the authors constructed a three-layer offloading framework within the context of intelligent IoV. To tackle the high computational complexity of the problem, it was decomposed into flow redirection and offloading decision components. Subsequently, the researchers introduced a deep reinforcement learning (DRL)-based scheme to solve the optimization problem efficiently. Real-world traces of taxis in Shanghai, China were utilized for performance evaluations, demonstrating the effectiveness of the proposed methods. The results indicated a significant decrease in average energy consumption by approximately 60% compared to the baseline algorithm. However, despite its contributions this work lacks comprehensive simulation-based evaluations to validate the proposed framework under various scenarios and conditions. While real-world trace-based evaluations can offer valuable insights into the effectiveness of the proposedmethods, simulation-based studies provide a more controlled environment for testing and validating the system's performance across a wide range of parameters and scenarios. [PERSON] et al.