Internet of Things (IoT) services have been implemented for several network applications from smart cities to rural areas. However, there are many barriers to provide an efficient solution for the IoT service deployment underlying innovation SDN/NFV-based technologies. First, though an IoT service can flexibly deploy via virtual network functions (VNFs), a deployment scheme needs to solve the joint routing and resource allocation problem, which becomes more difficult than the traditional centralized cloud/datacenter solution due to distributed resources in the edge-cloud network. In addition, due to uncertain workloads in IoT services, static optimization solutions may not deal with uncompleted knowledge of the entire input, which is often given by assumptions, but unrealistic in current provisioning approaches. Aiming to address these issues, we model an online mechanism for the dynamic IoT service chain deployment to optimize the operational cost in a finite horizon. We propose a JOint Routing and Placement problem for IoT service chain (JORP) that can dynamically scale in/out the number of VNF instances. We then propose a learning method to efficiently solve JORP based on branch-and-bound (BnB). Our proposed learning mechanism can intelligently imitate the branching/pruning actions of BnB, and remove unlikely solutions in the search space based on the deep neural network model to improve the performance. In that respect, we take an intensive simulation that illustrates the promising result of our proposed deep learning method compared to BnB and the greedy baseline in terms of the performance of the algorithm and the operational cost reduction.

中文翻译：

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边缘云平台中优化的IoT服务链实施：深度学习框架

物联网（IoT）服务已针对从智慧城市到农村地区的多种网络应用程序实施。但是，在基于创新SDN / NFV的技术基础上为物联网服务部署提供有效解决方案存在许多障碍。首先，尽管可以通过虚拟网络功能（VNF）灵活地部署IoT服务，但是部署方案需要解决联合路由和资源分配问题，由于边缘的分布式资源，与传统的集中式云/数据中心解决方案相比，这变得更加困难-云网络。此外，由于物联网服务中工作负载的不确定性，静态优化解决方案可能无法处理整个输入的不完整知识，而这通常是通过假设得出的，但在当前的配置方法中是不现实的。为了解决这些问题，我们对动态物联网服务链部署的在线机制进行建模，以在有限的范围内优化运营成本。我们提出了IoT服务链（JORP）的JOint路由和放置问题，该问题可以动态扩展/扩展VNF实例的数量。然后，我们提出了一种基于分支定界（BnB）的有效解决JORP的学习方法。我们提出的学习机制可以智能地模仿BnB的分支/修剪动作，并基于深度神经网络模型来消除搜索空间中不太可能的解决方案，从而提高性能。在这方面，我们进行了深入的仿真，从算法的性能和运营成本的降低方面，我们展示了与BnB和贪婪的基线相比，我们提出的深度学习方法的有希望的结果。