## 6 Experiments
### Performance Comparison Results  
Figure 6 presents a comparison of the average epoch completion time and the DJR violation degree (%) for all approaches, including MIN, MAX, eOPT, DP-CTR, EB-FJ, and our proposed RA-FS approach. The DJR violation degree is a measure of how much the completion time of a DL job has exceeded and been delayed relative to a pre-defined deadline. For instance, if the deadline is set at 30 s and the actual training completion time is 60 s, the DJR violation degree would be 100%. Figure 6a-c display the average epoch completion times for all approaches with renewable energy generation in Regions 1 to 3, respectively. The MIN and MAX approaches serve as baselines (i.e., the lower and upper bounds of average epoch completion time) for performance comparison. In addition, note that the eOPT approach only focuses on energy reduction, so it may not minimize the epoch completion time and not ensure the DJR compliance. Our proposed RA-FS approach demonstrates slightly better training performance (an average epoch completion time of 79.3 s) compared to the other approaches, except for the MAX approach. The DP-CTR and EB-FJ approaches achieve training performance comparable to our approach (average epoch completion times of 85.4 s and 80.2 s, respectively). This stems from their ability to minimize DNN model training time through sophisticated optimization modeling. Notably, the EB-FJ approach employs a combination of GPU frequency scaling (micro control) and dynamic DL job allocation (macro control) to achieve performance akin to our RA-FS approach. Figure 6d-f illustrate the DJR violation degree of all approaches in Regions 1 to 3, respectively. In contrast to Figure 6a-c, our RA-FS approach (average of 2.7%) surpasses the DP-CTR (average of 17.8%) and EB-FJ (average of 13.5%) approaches, nearing the performance of the MAX approach (average of 0.7%). Utilizing DJR- and DJTS-based frequency scaling, our RA-FS approach effectively addresses the performance requirements of heterogeneous DL job requests. Our RA-FS approach prioritizes high frequency values for DL jobs with tight deadlines, while allocating lower frequency values to those with more flexible deadlines. Figure 7 examines the ratio of total energy consumption to renewable energy consumption across all approaches in Regions 1 to 3. To facilitate a fair comparison, we set the energy budget for the EB-FJ approach to equal the total energy consumption incurred by our RA-FS approach. This budget is dynamically adjusted on an hourly basis. For instance, if the total energy consumption by our approach is 200 [kWh] for the 2:00 p.m. to 3:00 p.m. time slot, the corresponding energy budget for the EB-FJ approach during the same time slot is also set to 200 kWh. Figure 6(a)-c present bar charts illustrating the performance in Region 1, categorized by workload size. In all cases, our proposed RA-FS approach consistently achieves the best performance (average of 147 [kWh]) in terms of renewable energy utilization. The results of our approach closely approximate those of the MAX approach (average of 164 [kWh]). This is because our problem formulation explicitly incorporates the \(E(k)-r(k)\) term (refer to Problem 1) and adjusts frequency values accordingly. Our approach dynamically allocates larger capacity (i.e., higher frequency values) to DL jobs during periods of high renewable energy generation, while assigning smaller capacity (i.e., lower frequency values) to DL jobs during periods of low renewable generation. The other approaches demonstrate inferior performance in renewable energy utilization compared to ours. The DP-CTR approach averages a renewable generation of about 127 [kWh], while the EB-FJ approach averages approximately 69.3 [kWh]. Although the DP-CTR approach explicitly considers the amount of renewable generation, it underperforms compared to our proposed approach because its focus is on minimizing power consumption costs rather than directly reducing 'energy consumption' costs.