E2C simulator workload traces

该数据集包含了五个不同深度学习应用在模拟过程中的请求到达时间的工作负载追踪。此外，数据集还包含了面部识别、语音识别、图像分类、下一句预测和文本分类应用的配置信息。该数据集的任务是评估边缘多AI及其神经网络模型驱逐策略的有效性。

This dataset encompasses workload traces of request arrival times for five distinct deep learning applications during simulation experiments. Additionally, the dataset provides configuration details for applications including facial recognition, speech recognition, image classification, next-sentence prediction, and text classification. The core task of this dataset is to evaluate the effectiveness of eviction policies for multi-AI edge systems and their associated neural network models.