As a promising technology, edge computing extends computation, communication, and storage facilities toward the edge of a network. This new computing paradigm opens up new challenges, among which computation offloading is considered to be the most important one. Computation offloading enables end devices to offload computation tasks to edge servers and receive the results after the servers' execution of the tasks. In computation offloading, offloading modeling plays a crucial role in determining the overall edge computing performance. We present a comprehensive overview on the past development as well as the recent advances in research areas related to offloading modeling in edge computing. First, we present some important edge computing architectures and classify the previous works on computation offloading into different categories. Second, we discuss some basic models such as channel model, computation and communication model, and energy harvesting model that have been proposed in offloading modeling. Next, we elaborate on different offloading modeling methods which are based on (non-)convex optimization, Markov decision process, game theory, Lyapunov optimization, or machine learning. Finally, we highlight and discuss some research directions and challenges in the area of offloading modeling in edge computing.

作为一种很有前途的技术，边缘计算将计算，通信和存储设施扩展到了网络边缘。这种新的计算范式提出了新的挑战，其中，计算分流被认为是最重要的挑战。通过计算分流，终端设备可以将计算任务分流到边缘服务器，并在服务器执行任务后接收结果。在计算分流中，分流建模在确定总体边缘计算性能方面起着至关重要的作用。我们对边缘计算中的卸载模型相关的研究领域的过去发展以及最新进展进行了全面概述。首先，我们介绍一些重要的边缘计算体系结构，并将先前有关计算分流的工作分类为不同的类别。第二，我们将讨论卸载模型中提出的一些基本模型，例如通道模型，计算和通信模型以及能量收集模型。接下来，我们详细介绍基于（非）凸优化，马尔可夫决策过程，博弈论，李雅普诺夫优化或机器学习的不同卸载建模方法。最后，我们重点介绍并讨论了边缘计算中的卸载建模领域的一些研究方向和挑战。