## 4 Performance Evaluation
### Experimental Results
#### 4.3.5 Latency Results  
The x-axis in Figure 9 shows time steps from 1 to 2000, and the y-axis shows the user access latency for each application.  
The latency results show different trends for each algorithm, which correspond to their individual workload-management tactics and user-experience effects. The best-fit algorithm consistently maintains a latency range of 15-20 ms, with sporadic spikes that usually do not impair the user experience too much. This steady performance shows that the workload is distributed effectively, guaranteeing that latency stays within reasonable ranges for the majority of applications.  
On the other hand, the average latency range of the worst-fit method is comparable, but it exhibits more frequent and noticeable spikes at both lower and higher levels. These fluctuations point to a less consistent performance, which can result in a worse user experience, particularly when latency is higher. Even if these spikes are short-lived, they could nonetheless have an effect on applications that need steady response times.  
The power-saving algorithm has a latency that is between 15 and 20 ms, but it deviates from the average the most out of all the algorithms, with peaks as high as 23 ms and troughs as low as 8 ms. The algorithm's emphasis on assigning workloads to servers with the lowest power consumption is probably the cause of this unpredictability, as it might result in inefficiencies in circumstances where latency is a concern. These variations could have a detrimental effect on the user experience, especially for apps that require dependable and timely responses. With a substantially shorter average latency of 10-15 ms and brief spikes on both ends, the location-based approach stands out.  
Compared to other algorithms, this algorithm's architecture, which puts server proximity to customers first, reduces latency by 30.82%. The location-based method efficiently  
Figure 9: Latency per algorithm.  
lowers network congestion and improves response times by optimizing resource allocation based on user location and minimizing data transmission distances. As such, it is the best choice for settings where customer happiness depends on minimal latency and fast service delivery.  
By dynamically allocating workloads to the closest available servers, the location-based algorithm excels in decreasing physical distance and data transmission times, consequently increasing user experience and reducing latency times. This proximity optimization guarantees fewer network hops and less congestion when combined with effective real-time resource allocation based on geography data. The algorithm's efficiency is further increased by its capacity to adjust to shifting user locations and task demands. The location-based algorithm greatly reduces latency by emphasizing user proximity and real-time requirements; this makes it especially useful for time-sensitive applications and guarantees a flawless user experience.  
To sum up, the latency outcomes highlight the benefits of the location-based algorithm in handling tasks that require quick turnaround times. Applications needing quick and reliable performance might benefit greatly from its low latency and little volatility. However, although the average latencies of the best-fit and power-saving algorithms are consistent, their greater unpredictability may cause problems in situations when strict latency requirements are required. The frequent spikes seen in the worst-fit method underscore the necessity for more sophisticated approaches to guarantee consistent and effective latency control.