V2X offloading and resource allocation under SDN and MEC architecture

To address the serious problem of delay and energy consumption increase and service quality degradation caused by complex network status and huge amounts of computing data in the scenario of vehicle-to-everything (V2X),a vehicular network architecture combining mobile edge computing (MEC) and software defined network (SDN) was constructed.MEC sinks cloud serviced to the edge of the wireless network to compensate for the delay fluctuation caused by remote cloud computing.The SDN controller could sense network information from a global perspective,flexibly schedule resources,and control offload traffic.To further reduce the system overhead,a joint task offloading and resource allocation scheme was proposed.By modeling the MEC-based V2X offloading and resource allocation,the optimal offloading decision,communication and computing resource allocation scheme were derived.Considering the NP-hard attribute of the problem,Agglomerative Clustering was used to select the initial offloading node,and Q-learning was used for resource allocation.The offloading decision was modeled as an exact potential game,and the existence of Nash equilibrium was proved by the potential function structure.The simulation results show that,as compared to other mechanisms,the proposed mechanism can effectively reduce the system overhead.     

Design of an optimized traffic-aware routing algorithm using integer linear programming for software-defined networking

The number of internet users and connected devices has dramatically expanded due to the recent technological boom and the benefits that the internet of things offers to ease our lives. Network scheduling, quality of service, resource allocation, and security issues are now being addressed via software-defined networking (SDN). SDN has several benefits over traditional networks, including global centralized control, managing network traffic, and separating the forwarding and control plane. The work done in this paper aims to design and implement a traffic-aware routing framework based on routing optimization presented as an integer linear programming (ILP) to improve heterogeneous traffic flows' quality of service (QoS) in a simulated SDN environment. With the knowledge that the routing problem is a nondeterministic polynomial-time-hard problem, the proposed scheme aims to decrease the computational routing time to make the ILP-based routing system more suitable for real-time processing. The simulation results illustrate that the proposed framework reduces the computational time by 23% and 49% for Abilene and Goodnet topology, respectively. Additionally, with 1000 flows in the network, the suggested scheme reduces the number of network flows that violate the QoS by 9% and 22% (with Abilene topology) and 16% and 51% (with Goodnet topology) as compared to the existing shortest path delay and sway methods, respectively.     

基于TDD的低轨卫星跳波束资源分配算法

低轨(LEO)卫星跳波束技术可以灵活分配系统资源,适用于业务分布不均匀的场景。时分双工(TDD)方式可以减少星载和地面终端设备的天线数量,有效降低其复杂度,并有利于开展上下行非对称业务。本文提出一种基于TDD的LEO卫星跳波束资源分配算法,在满足业务需求的基础上,以最小化时域资源消耗为目标,建立支持跳波束和多频时分多址接入(MF-TDMA)机制的LEO卫星反向链路资源分配模型;综合考虑星地动态时延补偿,采取一种多层次的跳波束时隙架构设计,以最大化可用时隙为目标,建立上下行时隙切换模型,并提出一种基于TDD的跳波束时隙排布优化方法。仿真结果表明,对比于传统的MF-TDMA资源分配方法或固定多波束均分算法,本文提出的算法能有效提高系统的时隙利用率和吞吐量。     

Resource allocation for real-time traffic in unreliable wireless cellular networks

Providing reliable transmission for real-time traffic in wireless cellular networks is a great challenge due to the unreliable wireless links. This paper concentrates on the resource allocation problem aiming to improve the real-time throughput. First, the resource allocation problem is formulated as a Markov Decision Process and thus the optimal resource allocation policy could be obtained by adopting the value iteration algorithm. Considering the high time complexity of the optimal algorithm, we further propose an approximate algorithm which decomposes the resource allocation problem into two subproblems, namely link scheduling problem and packet scheduling problem. By this method, the unreliable wireless links are only constrained in the link scheduling problem, and we can focus on the real-time requirement of traffic in packet scheduling problem. For the link scheduling problem, we propose the maxRel algorithm to maximize the long-term network reliability, and we theoretically prove that the maxRel algorithm is optimal in scenarios with dynamic link reliabilities. The Least Laxity First algorithm is adopted for the packet scheduling problem. Extensive simulation results show that the proposed approximate resource allocation algorithm makes remarkable improvement in terms of time complexity, packet loss rate and delay.     

Radio resource management for cellular CDMA systems supporting heterogeneous services

A novel radio resource management (RRM) scheme for the support of packet-switched transmission in cellular CDMA systems is proposed by jointly considering the physical, link, and network layer characteristics. The proposed resource management scheme is comprised of a combination of power distribution, rate allocation, service scheduling, and connection admission control. Power distribution allows individual connections to achieve their required signal-to-interference-plus-noise ratio, while rate allocation guarantees the required delay/jitter for real-time traffic and the minimum transmission rate requirement for non-real-time traffic. Efficient rate allocation is achieved by making use of the randomness and burstiness; of the packet generation process. At the link layer, a packet scheduling scheme is developed based on information derived from power distribution and rate allocation to achieve quality of service (QoS) guarantee. Packet scheduling efficiently utilizes the system resources in every time slot and improves the packet throughput for non-real-time traffic. At the network layer, a connection admission control (CAC) scheme based on the lower layer resource allocation information is proposed. The CAC scheme makes use of user mobility information to reduce handoff connection dropping probability (HCDP). Theoretical analysis of the grade of service performance, in terms of new connection blocking probability, HCDP, and resource utilization, is given. Numerical results show that the proposed RRM scheme can achieve both effective QoS guarantee and efficient resource utilization.     

移动CDN网络架构和资源调度方法研究

针对移动核心网的网络架构问题,探讨基于云化核心网资源的移动CDN节点部署方法,给出一种基于核心网资源池的移动CDN网络架构。进而针对移动CDN资源协同问题,提出一种面向区域资源协同的移动CDN资源调度方法,建立基于多因素的资源协同目标规划数学模型。最后进行算例分析,实验结果验证了该方法的可行性和有效性。     

基于智能网络的租户服务质量保证技术

在多租户虚拟网络环境中,用户对于网络服务的多样性以及性能的稳定性需求并不会随着网络架构和运营模式的升级而削弱,用户需求之间的差异性和动态性对于不同切片间资源的分配和调度效率提出了新的挑战.针对多租户虚拟网络的特殊环境,首先提出了QVR(QoS-Virtual Routing)流量调度算法,同时将用户流量调度与网络虚拟资...     

Allocation of light splitters in all-optical WDM networks with sparse light splitting capabilities

In all-optical WDM networks, splitters at branch nodes are used to realize multicast trees. The problem of selecting a subset of nodes to place the splitters such that certain performance measure is optimized is called the splitter placement problem. This paper studies the splitter placement problem in all-optical WDM networks in which a light-forest is used to realize a multicast connection. The goal is to place a given number of splitters in the network such that the average per link wavelength resource usage of multicast connections is minimized. An upper bound and a lower bound on the per link average wavelength resource usage are derived. Two splitter placement methods are proposed for this problem. The proposed splitter methods are shown to yield significant lower average wavelength resource usage than the random placement method. One of the methods is shown to produce near minimum average wavelength resource usage.     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.     

Orchestration mechanism for VNF hardware acceleration resources in SDN/NFV architecture

The hardware acceleration mechanism for VNF (virtual network function) is recently a hot research topic in SDN/NFV architecture because of the low processing performance of VNF.Once hardware acceleration resources have been plugged into the network,how to optimally mange and orchestrate these resources under service requirements remains a question to be solved.Firstly,a unified management architecture based on separated control for hardware acceleration resources was proposed.Then,traditional network topology together with hardware acceleration resources were modeled into a unified network model and then the hardware acceleration resource orchestration problem was transferred into a multi-objective linear programming problem.Finally,a hardware-accelerator-card-prior’ heuristic algorithm was designed.Experimental results show that compared with existing methods,the proposed orchestration mechanism can efficiently manage hardware acceleration resources and reduce the processing latency by 30%.     

基于强化学习的光传送网路由波长优化

针对光传送网中动态业务的路由和波长问题,提出一种基于强化学习的深度路由波长分配算法DeepRWA。算法基于软件定义网络架构,通过强化学习灵活地调整控制光传送网,实现光网络路由波长分配策略优化。针对路由选择问题,结合链路上的波长使用情况,使用A3C算法选择合适的路由,使得阻塞率最小;针对波长分配问题,使用首次命中算法选择波长。考虑阻塞率、资源利用率、策略熵、价值损失、运行时间及收敛速度等多个指标,利用14节点NSFNET网络拓扑仿真实验。结果表明:当信道中包含18个波长时,与传统KSP-FF算法相比,所提出的路由波长分配算法的阻塞率降低了0.06,资源利用率提高了0.02,但运行时间有增加;在波长数超过45以后,与传统KSP-FF算法相比,所提算法保持阻塞率和资源利用率的同时,运行时间开始降低;当信道中包含波长数为58时,与传统KSP-FF算法相比,所提算法运行时间减少了0.07 ms。由此可见,提出的算法使路由选择和波长分配得到了优化。     

Routing framework and creation algorithm in Ad Hoc based SDN

Node energy-limited and mobility,indeed,requirement of customized service,made Ad Hoc press for customization Routing.Based on the core principle of software defined networking (SDN),the control framework and the design of routing creation bran-new approach about Ad Hoc based SDN was analyzed firstly.Secondly,the hierarchical routing model was established,and the problem of routing key nodes and key link centralized selection based on service path was proposed and its algorithm was designed,which could embed customize service into physical networks via nodes mapping based network performance maximization.Finally,it corroborate the advantage of routing implement via SDN in function,performance and the whole networks utilization.Experiments show that the energy consumption of network nodes is more balanced and the overall utilization of the network is nearly 14% higher than the traditional method.     

DDoS attack detection and defense based on hybrid deep learning model in SDN

Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.     

基于改进遗传算法的C-RAN网络动态无线资源分配

针对云无线网络(Cloud Radio Access Network,C-RAN)中传统静态资源分配效率低下以及动态无线资源分配中资源种类单一的问题,提出了一种基于用户服务质量(Qulity of Service,QoS)约束的动态无线资源分配方案,对无线资源从无线射频单元(Remote Radio Head,RRH)选择、子载波分配和RRH功率分配三个维度进行研究.首先,根据传统的C-RAN系统传输模型和QoS约束在时变业务环境下建立了以发射功率为变量,以吞吐量最大为优化目标的优化问题;然后,基于改进的遗传算法,将原优化方案转变为通过优化RRH选择、子载波分配和RRH功率分配来达到提高系统吞吐量的目的;最后,将改进的遗传算法与其他智能算法在种群规模变化下进行了时间复杂度对比.实验结果表明,所提算法具有较低时间复杂度,所提资源分配方案下的平均吞吐量增益为17％.     

基于粒子群优化算法的数据中心网络流量调度策略

为解决当前数据中心网络存在链路负载不均衡及带宽资源浪费问题,提出了一种基于粒子群优化算法的流量调度策略.该策略结合软件定义网络控制器可获取全局网络拓扑信息的特性,依据当前链路带宽资源状况及网络流量的带宽需求建立目标函数.首先,根据流的源地址和目的地址找出最短路径集,通过定义粒子聚合度判断算法是否有陷入局部最优的趋势;然后,结合约束条件与目标函数,利用优化的粒子群算法从最短路径集中找出网络流量的最佳调度路径.实验结果表明,相比于其他算法,该算法有效地提高了网络平均吞吐量,获取了较低的丢包率,从而减轻了带宽资源的浪费,更好地实现了网络的负载均衡.     

基于SDN/NFV的未来网络实验平台

依据SDN/NFV集中管控、动态、灵活、高效、可编排等特点,提出了基于SDN/NFV技术的未来网络实验平台的构建方案.该平台主要采用OpenStack和OpenDaylight的开源架构,同时研发设计SDN跨域虚拟网络通信、虚拟网元管理以及网络服务编排三大关键技术,实现了底层异构资源的实时动态管理与开放共享.同时,根据实验用户对网络资源的需求,灵活按需编排各种网络资源与SFC服务,为用户提供端到端的网络实验验证服务.     

A genetic algorithm‐based flow update scheduler for software‐defined networks

Software‐defined networking (SDN) facilitates network programmability through a central controller. It dynamically modifies the network configuration to adapt to the changes in the network. In SDN, the controller updates the network configuration through flow updates, ie, installing the flow rules in network devices. However, during the network update, improper scheduling of flow updates can lead to a number of problems including overflowing of the switch flow table memory and the link bandwidth. Another challenge is minimizing the network update completion time during large‐network updates triggered by events such as traffic engineering path updates. The existing centralized approaches do not search the solution space for flow update schedules with optimal completion time. We proposed a hybrid genetic algorithm‐based flow update scheduling method (the GA‐Flow Scheduler). By searching the solution space, the GA‐Flow Scheduler attempts to minimize the completion time of the network update without overflowing the flow table memory of the switches and the link bandwidth. It can be used in combination with other existing flow scheduling methods to improve the network performance and reduce the flow update completion time. In this paper, the GA‐Flow Scheduler is combined with a stand‐alone method called the three‐step method. Through large‐scale experiments, we show that the proposed hybrid approach could reduce the network update time and packet loss. It is concluded that the proposed GA‐Flow Scheduler provides improved performance over the stand‐alone three‐step method. Also, it handles the above‐mentioned network update problems in SDN.     

NFV业务链资源分配技术

SDN/NFV为运营商实现网络重构和架构演进提供了支撑技术,业务链及其组网技术是NFV的一个重要应用场景,相关的业务链资源分配问题是业界的研究热点。首先对业务链现状进行了梳理,重点分析了已有的资源分配机制和存在的问题,提出了一种业务链资源分配方案,充分考虑了对网络资源的优化利用,实现了VNF实例放置和业务链路径优化选择。实验结果表明,本方案具有更大的系统用户容量和更短的业务路径。     

无线接入网络自适应公平调度算法

研究了无线接入网络环境下自适应公平调度模型,讨论了服务区分的必要性和无线网络调度补偿方法,设计了一种二级结构的服务区分自适应公平调度算法,算法能够区分不同服务类型,公平分配网络资源。对于链路造成的暂时性服务中断给予了多级服务补偿,并通过分组级的公平资源分配,保障服务质量和流队列的平滑降级。     

Wavelength assignment for multicast in all-optical WDM networks with splitting constraints

Multicast is an important application in all-optical WDM networks. The wavelength assignment problem for WDM multicast is to assign a set of wavelengths to the links of a given multicast tree. In an all-optical WDM network without wavelength conversions, wavelength assignment is the key to guarantee the quality of service and to reduce communication costs. In this paper, we study wavelength assignment for WDM multicast with two criteria, to cover the maximum number of destinations, and to minimize the wavelength costs. The computational complexity of the problem is studied. Three heuristic algorithms are proposed and the worst-case approximation ratios for some heuristic algorithms are given. We also derive a lower bound of the minimum total wavelength cost and an upper bound of the maximum number of reached destinations. The efficiency of the proposed heuristic algorithms and the effectiveness of the derived bounds are verified by the simulation results.