Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach
Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach
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DOI：
10.1109/mnet.011.2100121
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发表时间：
2021-09-01
期刊：
影响因子：
9.3
通讯作者：
Scaglione, Anna
中科院分区：
文献类型：
--
作者：
Balasubramanian, Venkatraman;Alogaily, Moayad;Scaglione, Anna
物联网（IoT）设备的无处不在的性质提出了许多在5G时代需要创新解决方案的挑战。软件定义的网络（SDN）在管理下一代IoT网络的几个方面是必不可少的，这些网络的几个方面是需要控制高度异构，地理上分散的移动物联网设备的需求。一个这样的方面是边缘的缓存管理。最近，出现了多种形式的边缘资源，包括移动设备云和微观数据中心，以提供可扩展的高速缓存位置，以降低移动网络运营商（MNO）的成本。由于所有这些服务位置均已在MNO（或在5G基站注册后建立的链接）注册，因此应根据用户的需求放置内容，并且用户愿意付费以接收所需水平的成本QoS。为此，考虑到高度动态的用户移动性，了解内容的最佳位置的未来流行非常重要。在本文中，我们解决了移动物联网网络的两个关键方面：安全性和无缝连接以进行数据传输。我们依靠联合学习（FL）体系结构，该体系结构可以利用最终用户设备的数据和计算功能来训练机器学习模型。我们在物联网用例（例如缓存）的边缘计算领域中研究FL概念。我们从各种最先进的模型中得出结论，并提出了可以通过新颖提出的控制算法克服的几个挑战。
The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.