Autonomous driving paper index
Performance Evaluation of Cloud Simulation and Fog Computing Under Dynamic Load Conditions
One-line summary
This study addresses the challenge of evaluating performance in distributed computing by comparing Cloud Simulation and Fog Computing under dynamic load conditions.
Engineering notes
In contrast, Fog Computing achieves up to 46% lower latency at full load, reduced CPU usage, and more stable SIF values, indicating better efficiency and responsiveness.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This study addresses the challenge of evaluating performance in distributed computing by comparing Cloud Simulation and Fog Computing under dynamic load conditions. Using key metrics—latency, CPU utilization, bandwidth, and Signal Interference Factor (SIF)—the analysis demonstrates that Cloud Simulation shows a linear increase in resource consumption and interference with higher loads. In contrast, Fog Computing achieves up to 46% lower latency at full load, reduced CPU usage, and more stable SIF values, indicating better efficiency and responsiveness. The methodology involves performance monitoring across varying workloads, with results visualized through comparative plots. The findings highlight Fog Computing's suitability for latency-sensitive and resource-intensive applications and contribute insights for developing hybrid cloud-fog architectures aimed at optimized performance and scalability.
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