为本地物联网边缘网络启用Docker虚拟化纳米服务

J. Islam, E. Harjula, T. Kumar, Pekka Karhula, M. Ylianttila
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引用次数: 15

摘要

边缘计算是一种新颖的计算范式,使服务器资源更接近终端设备。它有助于释放高性能接入网络在超低延迟和传输速率方面的全部潜力,并提高对核心网络和数据中心问题的弹性。MEC (Multi-access Edge Computing)是欧洲电信标准协会(ETSI)为接入网级边缘计算提供的标准解决方案。MEC在接入网级运行,是大多数情况下的理想解决方案。然而,仍有一些挑战需要解决:一是与接入网络问题的脆弱性有关,二是接入网络和MEC服务器的高负载问题。这在大规模物联网(IoT)用例中是一个特别的问题,其中许多传感器可能产生大量数据,或者在接入网问题期间必须确保关键系统功能。在本文中,我们研究了利用本地硬件容量将一些边缘功能作为虚拟化和动态部署组件带到本地级别的可行性。在这项研究中,我们实现了一个基于本地微服务(称为纳米服务)的本地边缘网络原型,该原型使用Docker容器实现,并使用基于Docker swarm的编排进行部署。由于物联网网络通常由容量受限的设备组成,因此我们的重点是优化所提议的纳米服务的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Docker Enabled Virtualized Nanoservices for Local IoT Edge Networks
Edge computing is a novel computing paradigm moving server resources closer to end-devices. It helps unleashing the full potential of high-performance access networks with respect to ultra-low latency and transfer rate and improve resilience to problems at core networks and data centers. Multi-access Edge Computing (MEC), is a standard solution by European Telecommunications Standards Institute (ETSI) for access network-level edge computing. MEC, operating at access network level, is an ideal solution for the most cases. However, there are still some challenges to address: first is related to the vulnerability to access network problems and the second is about the high load inflicted to access networks and MEC servers. This is a particular issue in massive-scale Internet of Things (IoT) use cases, where numerous sensors may produce high amounts of data, or where critical system functionalities must be ensured also during access network problems. In this paper, we study the feasibility of bringing some edge functions to the local level as virtualized and dynamically deployable components utilizing local hardware capacity. For the study, we have implemented a local edge networking prototype based on local microservices, called nanoservices, implemented using Docker containers and deployed using Docker Swarm-based orchestration. Since IoT networks typically consist of constrained-capacity devices, our focus is in optimizing the resources of the proposed nanoservices.
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