The various applications of the Internet of Things and the Internet of Vehicles impose high requirements on the network environment, such as bandwidth and delay. To meet low-latency requirements, the concept of mobile edge computing has been introduced. Through virtualisation technology, service providers can rent computation resources from the infrastructure of the network operator, whereas network operators can deploy all kinds of service functions (SFs) to the edge network to reduce network latency. However, how to appropriately deploy SFs to the edge of the network presents a problem. Apart from improving the network efficiency of edge computing service deployment, how to effectively reduce the cost of service deployment is also important to achieve a performance-cost balance. In this paper, we present a novel SF deployment management platform that allows users to dynamically deploy edge computing service applications with the lowest network latency and service deployment costs in edge computing network environments. We describe the platform design and system implementation in detail. The core platform component is an SF deployment simulator that allows users to compare various SF deployment strategies. We also design and implement a genetic algorithm-based service deployment algorithm for edge computing (GSDAE) in network environments. This method can reduce the average network latency for a client who accesses a certain service for multiple tenants that rent computing resources and subsequently reduce the associated SF deployment costs. We evaluate the proposed platform by conducting extensive experiments, and experiment results show that our platform has a practical use for the management and deployment of edge computing applications given its low latency and deployment costs not only in pure edge computing environments but also in mixed edge and cloud computing scenarios.

中文翻译：

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用于边缘计算的新型遗传服务功能部署管理平台

物联网和车辆互联网的各种应用对网络环境提出了很高的要求，例如带宽和延迟。为了满足低延迟要求，已经引入了移动边缘计算的概念。通过虚拟化技术，服务提供商可以从网络运营商的基础设施租用计算资源，而网络运营商可以将各种服务功能（SF）部署到边缘网络以减少网络延迟。然而，如何将SF适当地部署到网络边缘提出了一个问题。除了提高边缘计算服务部署的网络效率外，如何有效降低服务部署成本对于实现性能成本平衡也很重要。在本文中，我们提供了一种新颖的SF部署管理平台，该平台允许用户在边缘计算网络环境中以最低的网络延迟和最低的服务部署成本动态部署边缘计算服务应用程序。我们将详细描述平台设计和系统实现。核心平台组件是SF部署模拟器，它允许用户比较各种SF部署策略。我们还为网络环境中的边缘计算（GSDAE）设计并实现了一种基于遗传算法的服务部署算法。对于租用计算资源的多个租户，访问某项服务的客户端可以减少客户端的平均网络延迟，从而降低相关的SF部署成本。我们通过进行广泛的实验来评估所提议的平台，