# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 3 Performance Evaluation of the 4 DT-Wx Prototype System
### Performance Analysis of MET Data Processing on 4 DT-Wx Prototype System
#### 3.2.1 Scalability and Efficiency Analysis: Speedup and Partition Optimization  
An analysis was conducted from the perspectives of scalability and efficiency of the 4 DT-Wx prototype system. [PERSON] [52] defined specific metrics for comparing the effects of various parallel algorithms, such as the speedup, efficiency, redundancy, utilization, and quality. The metrics speedup, S\({}_{speedup}\), which indicates how much faster the parallel \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interpolation Method** & **Spline Interpolation** & **IDW Interpolation** & **Linear Interpolation** \\ \hline No. of points/s & 1195.95 & 1163.04 & 1200.06 \\ Time/point & 0.000836 s & 0.000860 s & 0.000833 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Processing speed comparison for interpolation methods (spline, IDW, and linear). Figure 4: Performance comparison of variables (\(T\), \(WD\), and \(WS\)) and interpolation methods (IDW, linear, and spline) in a Taylor diagram. algorithm is compared with the corresponding serial algorithm, and efficiency, \(E_{efficiency}\), which indicates how well the processor is utilized to solve the problem, are mainly used in parallelism analyses [53; 54]:  
\[\mathrm{S}_{ speedup}(n,\ p)=\frac{ET_{serial}(n,1)}{ET_{parallel}(n,p)} \tag{3}\]  
\[E_{efficiency}(n,\ p)=\frac{S_{ speedup}(n,p)}{p} \tag{4}\]  
where \(n\) is the number of API requests, \(p\) is the number of partitions, \(ET_{serial}(n,\ 1)\) is the execution time of the serial algorithm, and \(ET_{parallel}(n,p)\) is the execution time of the parallel algorithm with \(p\) partitions. Therefore, in this section, we analyzed the speedup and efficiency of the proposed parallel algorithm (Equations (3) and (4); Figures 5 and 6). The system's parallel processing capability was found to improve as the number of API requests increased. Specifically, when the number of API requests increased from 50 to 2000, using 32 partitions resulted in an increase in the speedup from 6.85 to 13.13, approximately 91.68%, and an increase in efficiency from 0.21 to 0.41, an improvement of 95%. This suggests that the 4 DT-Wx system is more effective for large-scale data processing. Upon examining the impact of the buffer presence, a slight performance improvement was observed when using BOm compared with BOff. For example, with 2000 API requests and 128 partitions, BOn showed an approximately 15.8% higher speedup than BOff. This is assumed to be due to the structural advantage of BOn (data locality), which allows more data to be processed in a single operation, indicating the potential for expanding the buffer  
Figure 5: Efficiency heatmap for BOff (**left**) and BOn (**right**) across different numbers of API requests. Figure 6: Speedup heatmap for BOff (**left**) and BOn (**right**) across different numbers of API requests. size for API services. However, a decrease in efficiency with an increase in the number of partitions was also observed. When the number of API requests was 2000, increasing the number of partitions from four to eight resulted in a decrease in efficiency from 0.85 to 0.75, which is a factor to consider when scaling the system. The optimal number of partitions varied depending on the number of API requests. Additionally, for 50-100 API requests, 8-32 partitions showed an optimal performance, while for 500-2000 API requests, 32-256 partitions performed best. This result considers both the speedup and efficiency. Notably, although 256 partitions provided the maximum speedup at high API request volumes, the efficiency dropped significantly, excluding it from the optimal partitions from a practical perspective. Consequently, this suggests the need to dynamically adjust resources according to the anticipated load of the API service. Furthermore, the figures clearly illustrate the difference between the parallel and serial codes corresponding to the optimal partitions, demonstrating a consistent performance difference.