Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive code or at least a part of it will be transferred to the nearby resource-rich servers. Different approaches have been proposed to help make better decisions in respect of whether, where, when, and how much to offload and to improve the efficiency of the offloading process in the literature. On the other hand, the dynamic behavior of mobile devices running on-demand applications faces the offloading to the new challenges, which could be described as stochastic behaviors. Therefore, various stochastic offloading models have been proposed in the literature. However, to the best of the author’s knowledge, despite the existence of plenty of related offloading studies in the literature, there is not any systematic, comprehensive, and detailed survey paper focusing on stochastic-based offloading mechanisms. In this paper, we propose a survey paper concerning the stochastic-based offloading approaches in various computation environments such as Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Fog Computing (FC) in which to identify new mechanisms, a classical taxonomy is presented. The proposed taxonomy is classified into three main fields: Markov chain, Markov process, and Hidden Markov Models. Then, open issues and future unexplored or inadequately explored research challenges are discussed, and the survey is finally concluded.

来自不同智能设备（例如智能手机，IoT / IIoT网络和运行不同特定应用（例如增强现实（AR），虚拟现实（VR）和定位系统）的车载网络）产生的数据的快速增长，要求越来越多的处理和存储资源。卸载是一种有前途的技术，可以解决这类设备的固有局限性，通过这种局限性，资源密集型代码或至少一部分资源密集型代码将被传输到附近的资源丰富的服务器。在文献中，已经提出了不同的方法来帮助做出关于是否卸载，何时，何时以及卸载多少的更好的决定，并提高卸载过程的效率。另一方面，运行按需应用程序的移动设备的动态行为面临着新挑战的负担，这可以描述为随机行为。因此，文献中提出了各种随机卸载模型。然而，据作者所知，尽管文献中存在大量相关的卸载研究，但没有任何系统，全面和详细的调查论文关注基于随机的卸载机制。在本文中，我们提出了一份调查论文，涉及在各种计算环境（例如，移动云计算（MCC），移动边缘计算（MEC）和雾计算（FC））中基于随机的卸载方法，其中可以识别新的机制，介绍了经典分类法。拟议的分类法分为三个主要领域：马尔可夫链，马尔可夫过程和隐马尔可夫模型。然后，