## 5 Results
### Emission Reduction  
This analysis aims to understand the average reductions in carbon emissions throughout the year 2021 for the two state-of-the-art strategies: Flexible Start and Pause & Resume. For each month, six days are considered for the analysis. This approach has been adopted to closely align with the modus operandi of the authors in [9] and to ensure a reliable comparison of all strategies. Furthermore, this evaluation involves calculating the reductions based on the variation of key input parameters of the strategies, particularly the following:  
* The time window will be calculated by adding each value from the set [6; 12; 18; 24], which we will refer to as the hours-set, to the length of the workload. * The size of the time window will be calculated by increasing the workload length by each percentage value in the set \([25\%,50\%,75\%,100\%]\), which we will refer to as the percentage set for simplicity. * The checking time will be set to values in the set \([15;30;60;120]\). In the next subsections, we conduct a careful analysis for each AI workload, followed by a detailed analysis to offer insights into a real-world scenario. Specifically, we consider the Northern Carolina region as a benchmark. Graphs in Figures 6-9 summarise the obtained results. Graphs are grouped per combination of strategy (Flexible Start/Pause & Resume) and window sizes (percentage/hours set). On the \(x\) axis, we have the different  
Figure 5: The carbon emissions from training HF-SCA (AD on 8 \(\times\) A100 GPUs for 16 h) in seven different regions (one region per line) at various times throughout the year are shown. Each line is relatively flat, indicating that emissions in a single region are consistent across different months. However, there is significant variation between the least carbon-intensive regions (represented by the lowest lines) and the most carbon-intensive regions (represented by the top lines). This confirms that selecting the region in which experiments are conducted can have a substantial impact on emissions, with differences ranging from 0.25 kg in the most efficient regions to 2.5 kg in the least efficient regions. percentages in the case of the percentage set (Figures 7 and 9), and the different time extensions in the case of the hours set (Figures 6 and 8). The \(y\) axis is the checking time (in minutes). The \(z\) axis is the emission reduction percentage. The graphs have colour bands needed to appreciate the height of the surfaces in the 3D space. Figure 6: Emission reduction percentage for the four AI workloads using Flexible Start strategy and the hours set in Northern Carolina. The \(x\) axis represents the time extension (6, 12, 18, 24 h) assigned to the workload to complete the job. The \(y\) axis represents the checking time (15, 30, 60, 120 min) for carbon intensity. The \(z\) axis represents the emission reduction percentages for each specific combination of time extension and checking time. (**a**) Isolation Forest workload; (**b**) SVM workload; (**c**) HF-SCA workload; (**d**) autoencoder workload. Figure 7: Emission reduction percentage for the four AI workloads using Flexible Start strategy and the percentage set in Northern Carolina. The \(x\) axis represents the time extension (+25%, 50%, 75%, 100% of the original training time) assigned to the workload to complete the job. The \(y\) axis represents the checking time (15, 30, 60, 120 min) for carbon intensity. The \(z\) axis represents the emission reduction percentages for each specific combination of time extension and checking time: (**a**) Isolation Forest workload; (**b**) SVM workload; (**c**) HF-SCA workload; (**d**) autoencoder workload. Figure 8: Emission reduction percentage for the four AI workloads using Pause & Resume strategy and the hours set in Northern Carolina. The \(x\) axis represents the time extension (6, 12, 18, 24 h) assigned to the workload to complete the job. The \(y\) axis represents the checking time (15, 30, 60, 120 min) for carbon intensity.