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Peer Data Caching Algorithms in Large-Scale High-Mobility Pervasive Edge Computing Environments

机译:对等数据缓存算法在大型高迁移率普遍存在边缘计算环境中

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Emerging innovative edge devices like drones, self-driving cars, phones/tablets and IoT nodes are revolutionizing our daily lives. Caching data among peer edge devices enables data sharing needed in many applications. In such applications, network scalability and node mobility bring many challenges. They change the topology and the resources in the network and make the network less robust. In this paper, we propose peer data caching strategies that consider the scale and mobility of these increasingly popular edge devices. We propose a grouping method creating a layered design to reduce the number of entities in each layer. We propose inter-group and intra-group optimization problems which proactively cache data onto best places to support robust and fast data access. We develop a 7-approximation algorithm for inter-group optimization and use uncapacitated facility location problems to solve intra-group optimization. We also transform the mobility of nodes into node behaviors to reduce the impact of mobility on the network. Our extensive simulation results show that our proposed strategies can apply to large-size and high-mobility networks, while achieving satisfactory results for data access.
机译:新兴创新的边缘设备,如无人机,自动驾驶汽车,手机/平板电脑和物联网节点正在彻底改变我们的日常生活。对等边缘设备之间的缓存数据使许多应用程序中需要数据共享。在这种应用中,网络可伸缩性和节点移动性带来了许多挑战。它们更改了网络中的拓扑和资源,并使网络更稳健。在本文中,我们提出了考虑这些越来越受欢迎的边缘设备的比例和移动性的对等数据缓存策略。我们提出了一个分组方法,创建分层设计,以减少每层的实体数。我们提出了小组间和组间优化问题,这些问题主动缓存数据到最佳地点,以支持强大和快速数据访问。我们开发了一个7近似算法,用于组间优化,并使用未列为设施位置问题来解决组内优化。我们还将节点的移动性转换为节点行为,以减少移动性在网络上的影响。我们广泛的仿真结果表明,我们的策略可以适用于大型和高流动性网络,同时实现数据访问的令人满意的结果。

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