Fog Computing is a widely adopted paradigm that allows distributing the computation in a geographic area. This makes it possible to implement time-critical applications and opens the study to a series of solutions that permit smartly organizing the traffic among a set of fog nodes, which constitute the core of the Fog Computing paradigm. As a typical smart city setting is subject to a continuous change in traffic conditions, it is necessary to design algorithms that can manage all the computing resources by properly distributing the traffic among the nodes in an adaptive way. In this paper, we propose a cooperative and decentralized algorithm based on Reinforcement Learning that is able to perform online scheduling decisions among fog nodes. This can be seen as an improvement over the power-of-two random choices paradigm used as a baseline. By showing results from our delay-based simulator and then from our framework “P2PFaaS” installed on 12 Raspberry Pis, we show how our approach maximizes the rate of the tasks executed within the deadline, outperforming the power-of-two random choices both in a fixed load condition and with traffic extracted from a real smart city scenario.

中文翻译：

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基于强化学习的智慧城市雾计算在线分散调度


雾计算是一种广泛采用的范例，允许将计算分布在一个地理区域中。这使得实现时间关键的应用程序成为可能，并开启了一系列解决方案的研究，这些解决方案允许智能地组织一组雾节点之间的流量，这构成了雾计算范式的核心。由于典型的智慧城市环境会受到交通条件不断变化的影响，因此有必要设计能够通过以自适应方式在节点之间适当分配流量来管理所有计算资源的算法。在本文中，我们提出了一种基于强化学习的协作和去中心化算法，能够在雾节点之间执行在线调度决策。这可以被视为对用作基线的二次幂随机选择范式的改进。通过显示基于延迟的模拟器的结果，然后显示安装在 12 个 Raspberry Pi 上的框架“P2PFaaS”的结果，我们展示了我们的方法如何最大化在截止日期内执行任务的速率，在以下方面都优于两个随机选择的幂固定负载条件和从真实智慧城市场景中提取的流量。