The huge amount of data generated by the Internet of things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons. Cloud computing provides enhanced data storage and computing power but causes high communication latency. Edge and fog computing provide similar services with lower latency but with limited capacity, capability, and coverage. A single computing paradigm cannot fulfil all the requirements of IoT devices and a federation between them is needed to extend their capacity, capability, and services. This federation is beneficial to both subscribers and providers and also reveals research issues in traffic offloading between clouds, edges, and fogs. Optimization has traditionally been used to solve the problem of traffic offloading. However, in such a complex federated system, traditional optimization cannot keep up with the strict latency requirements of decision making, ranging from milliseconds to sub-seconds. Machine learning approaches, especially reinforcement learning, are consequently becoming popular because they can quickly solve offloading problems in dynamic environments with large amounts of unknown information. This study provides a novel federal classification between cloud, edge, and fog and presents a comprehensive research roadmap on offloading for different federated scenarios. We survey the relevant literature on the various optimization approaches used to solve this offloading problem, and compare their salient features. We then provide a comprehensive survey on offloading in federated systems with machine learning approaches and the lessons learned as a result of these surveys. Finally, we outline several directions for future research and challenges that have to be faced in order to achieve such a federation.
翻译:互联网( IoT) 设备产生的大量数据需要由云、 边缘和雾计算范式提供的计算能力和存储能力。 这些计算模式都有其自身的利弊。 云计算提供强化的数据存储和计算能力, 但却造成高通信延迟。 边和雾计算提供类似的服务, 其潜伏较低, 但能力、 能力和覆盖面有限。 单种计算模式无法满足IoT 设备的所有要求, 以及它们之间的一个联合会, 以扩展它们的能力、 能力和服务。 这个联合会对用户和供应商都有好处, 并揭示了在云、 边缘和雾间进行运货调查时遇到的研究问题。 优化通常用于解决交通流量问题。 然而, 在这样一个复杂的联邦化系统中, 传统的优化无法跟上决策的严格通缩要求, 从毫秒到次秒不等。 机器学习方法, 特别是强化学习方法, 因而越来越受欢迎, 因为它们可以迅速解决动态环境中大量未知的信息用户和供应商之间的卸载问题。 优化通常被用来解决联邦系统之间的交通问题, 我们用一个新的深度分析模型, 来分析, 将这种深度分析。