雾计算十年研究：相关性、挑战和未来方向,Software: Practice and Experience

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雾计算十年研究：相关性、挑战和未来方向
Software: Practice and Experience ( IF 2.6 ) Pub Date : 2023-07-18 , DOI: 10.1002/spe.3243
Satish Narayana Srirama ₁

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物联网 (IoT) 和实时应用的最新发展导致联网设备及其生成的数据空前增长。传统上，作为物联网应用的一部分，这些传感器数据在云端传输和处理，并将控制信号发送回相关执行器。这种以云为中心的物联网模型导致延迟和网络负载增加，并损害隐私。为了解决这些问题，思科在十年前的 2012 年创造了雾计算，它利用近端计算资源来处理传感器数据。自从提出以来，雾计算就引起了极大的关注，研究界致力于解决不同的挑战，例如雾框架、模拟器、资源管理、布局策略、服务质量方面、雾经济学等。然而，经过十年的研究，我们仍然没有看到可用于实现有趣的物联网应用的公共/私有雾网络的大规模部署。在文献中，我们只看到试点案例研究和小规模测试平台，以及利用模拟器来演示解决各自技术挑战的指定模型的规模。造成这种情况的原因有很多，最重要的是，雾计算尚未为公司和参与个人提供明确的商业案例。本文通过跨不同集群进行整合，总结了阻碍采用雾计算的技术、非功能和经济挑战。本文还总结了应对这些挑战的相关学术和工业贡献，并提供了实现实时雾计算应用的未来研究方向，同时考虑了联邦学习和量子计算等新兴趋势。

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A decade of research in fog computing: Relevance, challenges, and future directions

Recent developments in the Internet of Things (IoT) and real-time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the cloud, and the control signals are sent back to the relevant actuators, as part of the IoT applications. This cloud-centric IoT model, resulted in increased latencies and network load, and compromised privacy. To address these problems, Fog Computing was coined by Cisco in 2012, a decade ago, which utilizes proximal computational resources for processing the sensor data. Ever since its proposal, fog computing has attracted significant attention and the research fraternity focused at addressing different challenges such as fog frameworks, simulators, resource management, placement strategies, quality of service aspects, fog economics and so forth. However, after a decade of research, we still do not see large-scale deployments of public/private fog networks, which can be utilized in realizing interesting IoT applications. In the literature, we only see pilot case studies and small-scale testbeds, and utilization of simulators for demonstrating scale of the specified models addressing the respective technical challenges. There are several reasons for this, and most importantly, fog computing did not present a clear business case for the companies and participating individuals yet. This article summarizes the technical, non-functional, and economic challenges, which have been posing hurdles in adopting fog computing, by consolidating them across different clusters. The article also summarizes the relevant academic and industrial contributions in addressing these challenges and provides future research directions in realizing real-time fog computing applications, also considering the emerging trends such as federated learning and quantum computing.

更新日期：2023-07-18

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