Dynamic process location management in 5G

5G, as the next generation of wireless networks, promises very high throughput and low latency to mobile users that calls for a substantial innovation in computing management platforms to attend QoS metrics. Thanks to emerging technologies such as software‐defined networking (SDN)/network function virtualization (NFV), many features are available in 5G design to detect and control two types of latency caused by computation and communication. In this paper, taking features of caching mechanisms and SDN into the account, a platform is proposed to minimize latency in 5G via caching big flows intelligently and avoiding bottlenecks that may cause by virtualized computing components. First, the pioneering idea of compromising between the cloud radio access network (CRAN) and mobile edge computing (MEC)/information‐centric network (ICN) via dynamic processing location management platform is investigated. Accordingly, a mathematical optimization problem to minimize the average latency is formulated. Due to the problem complexity, a heuristic algorithm is proposed to treat the latency via dynamic orchestration of processing functionalities. Through numerical results, the performance of the proposed algorithm is analyzed, and the simulations corroborate our analytical results and illustrate the superior performance of the proposed algorithm with acceptable optimality gap.     

Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.     

移动边缘网络中基于用户移动的服务功能链迁移策略

移动边缘计算(Mobile Edge Computing,MEC)通过在网络边缘部署服务器,提供计算和存储资源,可为用户提供超低时延和高带宽业务。网络功能虚拟化(Network Function Virtualization,NFV)与MEC技术相结合,可在MEC服务器上提供服务功能链(Service Function Chain,SFC),提升用户的业务体验。为了保证移动用户的服务质量,需要在用户跨基站移动时将SFC迁移到合适的边缘服务器上。主要以最小化用户服务的端到端时延和运行成本为目标,提出了MEC网络中具有资源容量约束的SFC迁移策略,以实现移动用户业务的无缝迁移。仿真结果表明,与现有方案相比,该策略具有更好的有效性和高效性。     

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.     

Resource optimization algorithm of combination of NFV and SDN for application of multiple services

Various services of internet of things (IoT) require flexible network deployment to guarantee different quality of service (QoS).Aiming at the problem of IoT service function chain deployment,network function virtualization (NFV) and software defined networking (SDN) were combined to optimize resources.Considering forwarding cost and traffic load balance,a joint optimization model of virtual network function placement and service function chain routing was given and was proved to be NP-Hard.In order to solve this model,two heuristic algorithms were proposed.One was the service chain deployment algorithm of first routing then placing (FRTP) and the other was the placing followed by routing (PFBR) based on node priority.Simulation results demonstrate that FRTP and PFBR algorithm can significantly balance network traffic load while alleviating congestion and improving the acceptance ratio of the chain requests compared with other algorithms.     

An Efficient Delay Tolerance and Cost-Effective Secure NFV Enabled Multi-casting in Mobile Edge Cloud Networks

The mobile edge cloud has developed as the main platform to offer low latency network services from the edge of networks for stringent delay necessities of mobile applications. In mobile edge cloud networks, network functions virtualization (NFV) creates the frameworks for building up a new dynamic resource management framework structure to effectively utilize network resources. Delay tolerance NFV-enabled multicast request admissions in a mobile edge-cloud network are explored in this paper to limit request admission delays or maximizing system performance for a group of requests arriving individually. At first, for the cost reduction issue of a single NFV-empowered multicast request admission, the admission cost of each multicast request is assessed, and the Support based graph is constructed. Here, the multicast requests are prioritized dependent on their admission cost. Subsequently, trust and the delay-based local gradient are assessed for the prioritized multicast requests. At long last, delay tolerance NFV multicasting is accomplished by successful (First Come First Serve) FCFS queuing reliant on the assessed local gradient of requests. When compared to existing approaches, the exploratory results show that the proposed methodology is superior in terms of throughput, admission cost, and running time.

     

The intelligent multi‐domain service function chain deployment: Architecture,challenges and solutions

Network function virtualization (NFV) technology achieves flexible service deployment by replacing the middleboxes with virtual network functions (VNFs). In NFV, a set of VNFs are chained in a given order, called service function chain (SFC), and accordingly, data flow is steered to traverse all the VNFs in order to offer a service. With a large number of network devices and end users being connected into Internet, there is a growing demand for large‐scale multi‐domain networks to dynamically deploy the SFC across multiple network domains, in order to support efficient service provisioning. To this end, in this paper, we first investigate the state of the art of multi‐domain SFC deployment, and then propose an intelligent multi‐domain SFC deployment (IMSD) architecture by leveraging software‐defined networking (SDN), NFV, and deep learning technologies. Furthermore, we discuss the potential challenges to realize the IMSD and provide some promising solutions.     

NFV技术在未来网络试验设施及运营商网络中的应用

未来网络试验设施作为我国在网络信息领域唯一的重大科技基础设施,其面向各类用户不同试验支持的需求使网络功能虚拟化(NFV)技术在其建设中得到重要的应用。阐述了NFV技术在试验设施从设备研发、网络组网到服务支持等方面的应用,并对其相关产品在当前运营商网络中的应用进行了分析和研究。     

A low latency service function chain with SR-I/OV in software defined networks

A network flow is required to be processed by multiple network functions such as PGW and SGW in mobile networks as a service function chain (SFC). Compared to hardware-based network functions, virtualized network functions are more flexible for deployment. Software defined network (SDN) provides a centralized network architecture to manage network resources and route the network flow among network functions in sequence and virtual machines are leveraged to deploy the network functions as network function virtualization (NFV). However, currently the performance of NFV suffers from I/O latency because packet processing causes lots of interrupts that decreases CPU utilization. To address the I/O latency issue, SR-I/OV network card is designed to replace OpenvSwitch in host machines to reduce the system interrupts. However, SR-I/OV is not compatible with existing SDN system, which is an important component in future 5G networks. Therefore, we propose an integrated architecture called the low latency service function chain from a wider perspective in system design to overcome main defects described above. We modify appropriate components in SR-I/OV driver and OpenvSwitch to dramatically reduce packet processing latency in SFC composed by several VNFs. Moreover, our design is compatible with SDN environment and benefited by central control.

     

5G核心网云网一体化运维

全球大多数主流运营商已转向5G独立组网架构,并开始了5G独立组网架构的商用部署。5G核心网采用网络功能虚拟化技术,将通用的计算、存储、网络等硬件形成多种虚拟资源,基于需要实现网络功能及资源动态灵活性部署。随着网络功能虚拟化的5G核心网的大规模商用部署,5G核心网的运营维护也面临新的挑战。分析了5G核心网的虚拟化部署方案带来的运营维护工作难点,提出5G核心网运营维护系统建设的架构和方案,并在此基础上提出5G核心网云网一体化运营维护组织架构建议,为5G核心网云网一体化运维工作提供参考。     

Separating VNF and Network Control for Hardware‐Acceleration of SDN/NFV Architecture

A hardware‐acceleration architecture that separates virtual network functions (VNFs) and network control (called HSN) is proposed to solve the mismatch between the simple flow steering requirements and strong packet processing abilities of software‐defined networking (SDN) forwarding elements (FEs) in SDN/network function virtualization (NFV) architecture, while improving the efficiency of NFV infrastructure and the performance of network‐intensive functions. HSN makes full use of FEs and accelerates VNFs through two mechanisms: (1) separation of traffic steering and packet processing in the FEs; (2) separation of SDN and NFV control in the FEs. Our HSN prototype, built on NetFPGA‐10G, demonstrates that the processing performance can be greatly improved with only a small modification of the traditional SDN/NFV architecture.     

多接入移动边缘计算技术在移动网络的部署实践与思考

多接入移动边缘计算(MEC)技术是当前兴起的一项新技术。通过把计算、存储、带宽和应用等资源放在网络的边缘侧,以减小传输延迟和带宽消耗。MEC可以广泛应用于运营商的4G/5G等移动网络以及Wi-Fi无线网络,并将会成为未来工业自动化和信息化应用的新一代解决方案。本文介绍了多接入移动边缘计算技术如何与4G/5G移动网络结合并在行业场景中的实际尝试和探索,并对主要研究进展和需解决的关键问题进行了介绍。     

5G MEC边缘智能架构研究

作为边缘计算与人工智能融合驱动的新模式,边缘智能已然渗透到各个行业。5G MEC作为运营商新型网络边缘的锚点,需要借助边缘智能来充分释放网络边缘价值。文章初步探讨网络边缘智能化需求,提出一种基于5G MEC的边缘智能优化架构,扩展了面向异构计算的弹性AI加速服务和自适应云边智能协同调度能力,从而实现了MEC平台运营智能化和AI能力服务化。     

Optimal deployment of cloudlets based on cost and latency in Internet of Things networks

With the development of the Internet of Things (IoT), a large amount of data is generated on the network edge. Given the limited computing power of mobile devices (MDs) and access to computing resources from remote clouds, which leads to high latency to MDs, edge computing provides a way to reduce service latency by building a miniature cloud (Cloudlet). MDs transfer tasks they generate to nearby cloudlets for lower latency. Although a lot of research has been done in the field of edge computing, little attention has been paid to how to deploy cloudlets in the network. In this paper, we study the cloudlet deployment on a large number of wireless access points (APs) in an IoT network to optimize both deployment cost and network latency. When the cloudlets has been deployed in the network, we propose a fault-tolerant cloudlet deployment scheme. When the original cloudlets in the network fail, the software-defined network technology is used to start the fault-tolerant cloudlets in time to ensure the stability of the network latency. To address the above problems, we propose a binary-based differential evolution cuckoo search (BDECS) algorithm, which selects the permanent cloudlet deployment location among a large number of APs on the network. Extensive simulations reveal that the proposed algorithm has better performance in minimizing cost and latency compared with other deploymegt algorithms. Moreover, the convergence speed of the BDECS algorithm is also superior to other algorithms.

     

Software‐defined satellite cloud RAN

This paper provides an assessment study on the virtualization of a Digital Video Broadcasting ‐ Satellite ‐ Second Generation (DVB‐S2)/ Digital Video Broadcasting ‐ Return Channel Satellite ‐ Second Generation (DVB‐RCS2) satellite ground infrastructure and proposes a framework, named Satellite Cloud Radio Access Network (SatCloudRAN), that aims to ease the integration of satellite components in forthcoming 5G systems. Special attention is given to the design of SatCloudRAN by considering the split and placement of virtualized and nonvirtualized functions while taking into account the characteristics of the transport links connecting both type of functions. We assess how virtualization and softwarization technologies, namely, network function virtualization and software‐defined networking, can deliver part of the satellite gateway functionalities as virtual network functions and achieve a flexible and programmable control and management of satellite infrastructure. Under the network function virtualization paradigm, building virtual network function blocks that compose a satellite gateway have been identified, and their interaction exhibited. This paper also gives insights on how the SatCloudRAN approach can allow operators to provide software‐defined networking‐based (1) bandwidth on demand, (2) dynamic Quality of Service, and (3) satellite gateway diversity. Copyright © 2017 John Wiley & Sons, Ltd.     

Aerial edge computing for 6G

In the 6th generation mobile communication system (6G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things (IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle (UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6G are elaborated to provide some guidance for future research.     

“5G+MEC”为智能制造赋能的部署应用

MEC是5G网络的关键技术之一,可以将应用本地化,促进网络和业务的深度融合。为实现“5G+MEC”在垂直行业的应用,采用NFV技术部署了服务化架构的5G核心网,并在郑州格力制造园区部署面向智能制造的MEC,采用独立组网方式实现园区5G覆盖,打造了“计算+连接”的5G MEC网络。通过将“5G+MEC”应用在智能制造行业,实现工业制造的网络化、信息化、智能化,使得生产数据在网络边缘处理而不必上传至核心网,降低了网络时延,实现了智能制造的数据闭环。     

面向5G网络的MEC部署方案

移动/多接入边缘计算( MEC)技术通过将计算存储能力与业务服务能力向网络边缘迁移,使应用、服务和内容可以实现本地化、近距离、分布式部署,从而在一定程度上解决了 5G 增强移动宽带、低时延高可靠以及大规模机器通信类终端连接等场景的业务需求。本文在分析 MEC 技术在LTE网络中的应用方案以及对于 5G 网络的价值与意义的基础上,给出了 5G MEC 部署方案以及MEC关键技术的方案。     

Intent‐based service management for heterogeneous software‐defined infrastructure domains

One of the main challenges in delivering end‐to‐end service chains across multiple software‐defined networking (SDN) and network function virtualization (NFV) domains is to achieve unified management and orchestration functions. A very critical aspect is the definition of an open, vendor‐agnostic, and interoperable northbound interface (NBI) that should be as abstract as possible and decoupled from domain‐specific data and control plane technologies. In this paper, we propose a reference architecture and an intent‐based NBI for end‐to‐end service management across multiple technological domains. The general approach is tested in a heterogeneous OpenFlow/Internet‐of‐Things (IoT) SDN test bed, where the proposed solution is applied to a rather complex service provisioning scenario spanning three different technological domains: an IoT infrastructure deployment, a cloud‐based data collection, processing, and publishing platform, and a transport domain over a geographic network interconnecting the IoT domain and the data center hosting the cloud services.     

A blockchain-enabled security management framework for mobile edge computing

Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.