Cloud computing is attractive mostly because it allows companies to increase and decrease available resources, which makes them seem limitless. Although cloud computing has many advantages, there are still several issues such as unpredictable latency and no mobility support. To overcome these problems, fog computing extends communication, storage, and computation toward the edge of network. Therefore, fog computing may support delay-sensitive applications, which means that the application latency from end users can be improved, and it also decreases energy consumption and traffic congestion. The demand for performance, availability, and reliability in computational systems grows every day. To optimize these features, it is necessary to improve the resource utilization such as CPU, network bandwidth, memory, and storage. Although fog computing extends the cloud computing resources and improves the quality of service, executing capacity planning is an effective approach to arranging a deterministic process for web-related activities, and it is one of the approaches of optimizing web performance. The goal of capacity planning in fog computing is preparing the system for an incoming workload, so we are able to optimize the system’s utilization while minimizing the total task execution time, which happens before sending the load toward the cloud environment or not sending it at all. In this paper, we evaluate the performance of a web server running in a fog environment. We also use QoS metrics to plan its capacity. We proposed performance closed-form equations extracted from a continuous-time Markov chain model of the system.

中文翻译：

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雾计算中Web服务器容量规划的随机性能模型

云计算之所以有吸引力，主要是因为它允许公司增加和减少可用资源，这使得它们看起来是无限的。虽然云计算有很多优势，但仍然存在不可预测的延迟和没有移动性支持等几个问题。为了克服这些问题，雾计算将通信、存储和计算扩展到网络边缘。因此，雾计算可以支持对延迟敏感的应用，这意味着可以改善来自最终用户的应用延迟，同时也可以减少能源消耗和交通拥堵。计算系统对性能、可用​​性和可靠性的需求每天都在增长。要优化这些特性，就需要提高 CPU、网络带宽、内存和存储等资源利用率。尽管雾计算扩展了云计算资源并提高了服务质量，但执行容量规划是为 Web 相关活动安排确定性过程的有效方法，也是优化 Web 性能的方法之一。雾计算中容量规划的目标是为传入的工作负载准备系统，因此我们能够优化系统的利用率，同时最大限度地减少总任务执行时间，这发生在将负载发送到云环境或根本不发送之前. 在本文中，我们评估了在雾环境中运行的 Web 服务器的性能。我们还使用 QoS 指标来规划其容量。我们提出了从系统的连续时间马尔可夫链模型中提取的性能闭式方程。