## 4 Performance Evaluation, and Result Analysis and Discussion
### Experimental Results and Analysis of the Results
The average convergence deviation of the proposed algorithm was the most stable of the three. It was 3% more stable than that of the existing one and 4% more stable than that of the basic PID one. In Scenario 4, the average convergence time of the proposed algorithm was the fastest of the three. It was 38% faster than that of the existing one and 141% faster than that of the basic PID one. The average convergence deviation of the proposed algorithm was the most stable of the three. It was 4% more stable than that of the existing one and the basic one respectively. As a result, the existing algorithm has better performance in periodic environments, whereas the proposed algorithm has better performance in aperiodic environments. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Scenario** & \multicolumn{3}{c}{**1**} & \multicolumn{3}{c}{**2**} & \multicolumn{3}{c}{**3**} & \multicolumn{3}{c}{**4**} \\ \cline{2-13}
**Algorithm** & **B** & **E** & **P** & **B** & **E** & **P** & **B** & **E** & **P** & **B** & **E** & **P** \\ \hline Average running time (sec) & 30.19 & 30.17 & 30.20  
Cloud computing provides cloud services anywhere, anytime, on any device. Cloud nodes and edge nodes dynamically receive various user requests and handle them as soon as possible. Caching in a cloud computing environment is also a cloud service that should be done dynamically as soon as possible. Scenario 4 represents that dynamic case. Thus, the proposed algorithm is more suitable for actual cloud services than the existing algorithm. Table 7 shows the cache hit ratio by cache capacity. The objective of this experiment was to find the optimal cache capacity, which is related to billing and insufficient resources in edge nodes. An edge node's limited computing resources mean that reducing the capacity of the cache is beneficial in terms of both the resource efficiency of the edge node and cost reduction for the user. We performed this experiment using the proposed algorithm for both CSPs and CSCs, and each cache hit ratio represents the average of 30 experiments. We repeated this experiment starting with 5 MB and decreasing by 0.5 MB. From 5 MB to 1.5 MB, it showed excellent performance, maintaining a cache hit ratio of 96% or more. However, below 1 MB, the cache hit ratio decreased gradually. This result could be related to the amount of data consumed by the cloud service in the experiment. In this experiment, from 128 KB to 1 MB of data were consumed. Therefore, to guarantee an adequate cache hit ratio for high quality service, 1.5 times the cache capacity above the maximum consumption per unit time could be needed. As a result, EDCrammer, a lightweight and agile algorithm, can reduce cache capacity. It could help CSPs to efficiently manage the storage resources of edge nodes, and it could help CSCs reduce their service fees. Table 8 shows the cache hit patterns marked at 5 MB and 1 MB. This experiment was performed to analyze the pattern of cache hits and determine the reason for the cache miss ratio of around 4%. The analysis in Table 7 indicates that 5 MB of cache capacity is sufficient, and 1 MB of cache capacity is insufficient. Both patterns confirmed that a certain period of waiting or preparation time is necessary. At the beginning of all graphs, a cache miss occurred within 10 iterations or within 1.5 s. This issue can thus be solved using methods such as buffering or delayed start, which are already applied to typical video streaming.