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Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

机译:边缘计算实现的比较:雾计算,cloudlet和移动边缘计算

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When it comes to storage and computation of large scales of data, Cloud Computing has acted as the de-facto solution over the past decade. However, with the massive growth in intelligent and mobile devices coupled with technologies like Internet of Things (IoT), V2X Communications, Augmented Reality (AR), the focus has shifted towards gaining real-time responses along with support for context-awareness and mobility. Due to the delays induced on the Wide Area Network (WAN) and location agnostic provisioning of resources on the cloud, there is a need to bring the features of the cloud closer to the consumer devices. This led to the birth of the Edge Computing paradigm which aims to provide context aware storage and distributed Computing at the edge of the networks. In this paper, we discuss the three different implementations of Edge Computing namely Fog Computing, Cloudlet and Mobile Edge Computing in detail and compare their features. We define a set of parameters based on which one of these implementations can be chosen optimally given a particular use-case or application and present a decision tree for the selection of the optimal implementation.
机译:在海量数据的存储和计算方面,在过去十年中,云计算已成为事实上的解决方案。但是,随着智能和移动设备的迅猛发展以及物联网(IoT),V2X通信,增强现实(AR)等技术,重点已转移到获取实时响应以及对上下文感知和移动性的支持上。由于在广域网(WAN)上引起的延迟以及与云上资源的位置无关的资源调配,因此需要使云的功能更接近消费者设备。这催生了边缘计算范式的诞生,该范式旨在在网络边缘提供上下文感知存储和分布式计算。在本文中,我们详细讨论了边缘计算的三种不同实现,即雾计算,Cloudlet和移动边缘计算,并比较了它们的功能。我们定义了一组参数,基于这些参数,可以在给定特定用例或应用的情况下最佳地选择这些实现之一,并提供用于选择最佳实现的决策树。

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