论文标题
了解边缘计算的不确定性：新原理和设计方法
Understanding Uncertainty of Edge Computing: New Principle and Design Approach
论文作者
论文摘要
由于边缘在云和用户之间的位置以及最新的深神经网络（DNN）应用的激增，Edge Computing会带来不确定性，必须单独理解。特别是，Edge用户在本地特定的要求，这些要求根据时间和位置而变化，导致一种称为数据集偏移的现象，定义为培训和测试数据集表示之间的差异。它为解决不确定性不足的许多最新方法提供了。我们没有找到围绕它的方法，而是通过利用新的原则来利用这种现象：AI模型多样性，当允许用户从多个AI模型中进行机会选择时，这是实现的。为了利用AI模型多样性，我们提出了模型多样性网络（MODNET），并为有效学习驱动的沟通方案提供了设计指南和未来方向。
Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users' locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets' representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.
