## 6 Conclusions and Future Works  
In this paper, we present a novel deep learning-based framework for optimizing energy efficiency in MEC systems through dynamic management of MECS operational states while considering the maximum allowable service delay constraints. To exploit the inherent  
Figure 9: \(\eta(t)\) match rate according to the Euclidean distance between \(\tilde{\lambda}(t)\) and \(\tilde{\lambda}(t)\) (i.e., \(d_{\lambda}(t)\)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Server ID** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \(ES_{R}\) & 0.27 & 0.25 & 0.41 & 0.04 & 0.12 & 0.17 & 0.84 \\ \hline \(GA_{T}\) & 0.34 & 0.38 & 0.45 & 0.27 & 0.34 & 0.32 & 0.44 \\ \hline proposed & 0.24 & 0.24 & 0.29 & 0.05 & 0.08 & 0.52 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the rates at which each server is selected to be in an active state. spatio-temporal correlations in user mobility and service usage patterns, we use the STGCN for predicting the workload distribution at the time when MECS operational states must be determined. Acknowledging the inevitability of prediction errors, we incorporate the DQN that embeds similar system states in close proximity within the feature space, thereby enhancing the robustness and the generality of optimal MECS state determination in the presence of prediction uncertainties. Extensive simulation experiments conducted using real-world datasets demonstrate that by leveraging the robust generalization capabilities of DQN, our method achieves superior performance in real-world scenarios characterized by diverse levels of workload distribution prediction errors. Specifically, the approach achieves significant reductions in delay violation rates: an 8% improvement over \(ES_{P}\), a 6% reduction compared with \(GA_{T}\), and a 5% decrease relative to \(GA_{A}\). These results indicate that in practical scenarios where prediction uncertainties are present, our proposed framework not only surpasses the performance of GA-based methods but also outperforms the \(ES_{P}\) approach, which conducts an exhaustive search across all possible \(\eta(t)\)s. Furthermore, conventional approaches, which exclusively utilize given state information, demonstrate significant vulnerability to prediction errors, exhibiting a dramatic increase of over 13% in delay violation rates when comparing scenarios with workload prediction errors to those without. In contrast, our proposed method, which leverages DQN for the extraction and utilization of important features from state information, achieves superior generalization performance. Specifically, our approach maintains the differential in delay violation rates between these two scenarios (with and without prediction errors) at a notably low level of less than 2%, substantially outperforming existing methodologies in terms of robustness to prediction uncertainties. Moreover, our method exhibits superior computational efficiency, achieving a 20-fold acceleration in \(\eta(t)\) determination time relative to \(GA_{A}\) when evaluated under identical experimental conditions. Our proposed framework exhibits limitations in both the MEC system model and deep learning model aspects. To address these issues, we plan to conduct the following future research works. Firstly, our controller operates on a large time scale and assumes that MECS processes tasks in a FIFO manner. However, tasks may have different priority levels and varying CPU cycle requirements. Therefore, it is necessary to develop an integrated solution that combines the task scheduling method in a MECS with our proposed MECS state management technique to enhance MEC system energy efficiency at both large and small time scales. Secondly, in our current system model, BS groups are statically mapped to their primary MECS. However, the user population connected to each BS varies spatially and temporally, leading to dynamic workload patterns.