## 7 Conclusions  
In this paper, we introduce a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient deep learning (DL) job processing clusters. To the best of our knowledge, this is the first study to specifically address renewable energy generation in reducing the energy consumption costs associated with DL job processing, utilizing a real-time GPU frequency scaling method. Furthermore, to address the non-convex optimization challenge posed by the multiplication of power and training time, we introduce a log-transformation technique. This technique effectively transforms the non-convex problem into a convex one, ensuring the optimality of the derived solution. We incorporate deadline constraints, defined as Deep-Learning Job Requirement (DJR) and Deep-Learning Job Completion per Slot (DJCS), into the objective function. By doing this, our approach enables  
Figure 7: Comparison of Ratio of Total Energy Consumption to Renewable Energy Consumption.  
maximizing renewable energy utilization while meeting the performance requirements of each DL job. Using the NVIDIA-SMI utility on recent NVIDIA GPU architectures, including RTX3060, RTX3090, and RTX4090, we measured the actual data of epoch completion times, power consumption, and associated energy consumption for various deep neural network (DNN) models--SqueezeNet, PreActResNet, and SEResNet--trained across a range of GPU frequency settings. Through diverse experimental results, which included renewable generation data from multiple regions, we confirmed that our proposed RA-FS approach surpasses its competitors in terms of training performance and energy consumption cost efficiency. The RA-FS approach achieves an average improvement of approximately 5% in epoch completion time, 71% in deadline satisfaction, 10.7% in energy consumption, and 31% in renewable energy utilization compared to its competitors. We conclude that our work is a highly promising option for future deep learning job processing clusters, especially those aiming for carbon neutrality and the use of natural, renewable energy sources.  
In future work, we plan to expand our RA-FS approach to include parallel DL job processing on both homogeneous and heterogeneous worker nodes for practicality. We will develop an online-based RA-FS approach for clusters to ensure acceptable performance, even when dealing with the uncertain behaviors of DL job requests. Furthermore, we will explore diverse strategies, including the integration of an energy storage system and continual learning job allocation, to effectively enhance the utilization of renewable energy.  
Conceptualization, D.-K.K.; methodology, D.-K.K. and H.-G.P.; software, D.-K.K.; validation, D.-K.K. and H.-G.P.; formal analysis, D.-K.K.; investigation, D.-K.K. and H.-G.P.; resources, D.-K.K. and H.-G.P.; data curation, D.-K.K.; writing--original draft preparation, D.-K.K. and H.-G.P.; writing--review and editing, D.-K.K.; visualization, D.-K.K. and H.-G.P.; supervision, D.-K.K.; project administration, D.-K.K. and H.-G.P.; funding acquisition, D.-K.K. and H.-G.P. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Wonkwang University in 2021.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.