## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.2 Execution Time: Model Selection and Inference  
To evaluate the time required to deliver forecasting services under varying resource conditions, we analyzed three components of execution latency: model selection time, inference time, and total service time. These metrics together represent the responsiveness and practicality of the system in delivering forecasting results in real-time environments. Figure 5: Relative performance gap between the proposed method and exhaustive search. Figure 6 shows the model selection time for both the proposed meta-learning framework and the exhaustive search baseline. The meta-learning approach completed model selection within 0-3 milliseconds across all CPU levels and sensor request sizes. In contrast, the exhaustive search method showed significant selection delays over 100 s, regardless of CPU availability or concurrency level, due to the need to evaluate every model candidate sequentially. This performance gap directly results from the brute-force nature of exhaustive search, which evaluates all candidate models for each sensor sequentially. The model selection time for the meta-learning framework remained consistently low, completing within approximately 1-2 milliseconds across all CPU levels and sensor request sizes. However, a slight increase in selection time was observed at higher CPU availability levels. This minor variation is unlikely to stem from the model selection logic itself, given its inherently lightweight and rapid design. Instead, it is more plausible that the increase is caused by background processes running on the system's operating environment. As CPU resources become more available, non-critical background software and system management tasks might opportunistically utilize the excess capacity, leading to subtle but measurable delays in the overall timing of the selection process. Despite this, the impact remains negligible in practice, as the selection time is orders of magnitude shorter than the inference time or total service time. Figure 7 presents the inference time for the selected forecasting models. While the inference time generally decreased with higher CPU availability, it increased as the number of concurrent models grew due to competition for computational resources. Importantly, the inference time was comparable between the meta-learning and exhaustive methods, since both ultimately execute the same forecasting models after selection. This confirms that inference efficiency is a function of model complexity and resource availability, rather than the selection method itself. Figure 6: Comparison of the model selection time between the proposed meta-learning method and exhaustive search. The combined effect of model selection and inference time is summarized in Figure 8, which illustrates the total service time, defined as the duration from receiving a forecasting request to returning the prediction result. The proposed framework consistently completed the full service--including model selection and inference--within 7 to 8 s, even under the most constrained setting of 100 concurrent sensors at 20% CPU. In contrast, the exhaustive search method required over 120 s in total, highlighting its infeasibility for real-time deployment. Figure 8: Comparison of the total service time (model selection + inference) between the proposed framework and exhaustive search. Figure 7: Inference time of selected forecasting models under varying CPU availability and sensor concurrency levels. These results emphasize the following critical insight: although the inference phase is computationally similar between the two methods, the model selection phase introduces a massive overhead in the exhaustive baseline, severely limiting its feasibility in real-time environments. The proposed system, by reducing the model selection time to near zero, ensures that the end-to-end latency remains within acceptable operational bounds, even as the forecasting demand scales up.