Cloud computing is attractive to business owners and allows enterprises to start from the small and increase resources only when there is a rise in service demand, but cloud may become expensive. Fog computing has many advantages, and it is suited for the applications whereby real time is very important, but fog resources may also be highly limited. The cloud and fog computing may perform tasks together to attend different types of applications and mitigate their limitations. However, taking into account variables such as latency, workload and computational capacity, it becomes complex to define under what circumstances it is more advantageous to use the cloud layer or the fog. This paper proposes a stochastic Petri net to model such a scenario by considering cloud and fog. The model permits to configure 12 parameters including, for example, the number of available resources, workload and mean requests arrival time. We present a case study using a classical big data algorithm to validate the model. The case study is a practical guide to infrastructure administrators to adjust their architectures by finding the trade-off between cost and performance.

中文翻译：

---

混合云和雾架构性能和成本分析的随机模型

云计算对企业主很有吸引力，允许企业从小做起，只有在服务需求上升时才增加资源，但云计算可能会变得昂贵。雾计算有很多优点，适用于实时性非常重要但雾资源也可能非常有限的应用。云计算和雾计算可以一起执行任务以参与不同类型的应用程序并减轻它们的局限性。然而，考虑到延迟、工作负载和计算能力等变量，定义在什么情况下使用云层或雾更有利变得复杂。本文提出了一个随机 Petri 网，通过考虑云和雾来模拟这种场景。该模型允许配置 12 个参数，例如，可用资源的数量、工作量和平均请求到达时间。我们展示了一个使用经典大数据算法来验证模型的案例研究。该案例研究是基础设施管理员通过找到成本和性能之间的权衡来调整其架构的实用指南。