基于微服务架构的粒度可变服务功能链映射算法

针对5G环境下服务功能链（SFC）端到端时延无法满足时延敏感型应用需求的问题,将传统虚拟网络功能（VNF）拆分成粒度不一的映射单元,提出了基于微服务架构的粒度可变服务功能链映射（VG-SFCM）算法。首先将传统粗粒度的VNF解耦成细粒度的微服务单元,随后通过SFC内冗余微服务单元的合并及SFC间微服务单元的复用,减少微服务单元的实例化,降低SFC的处理时间。仿真结果表明,所提算法在降低平均部署网络成本的同时,其SFC端到端时延相较于传统的映射算法降低了14.81%。     

特征关联的服务功能链可靠性优化算法

针对优化服务功能链(Service Function Chain,SFC)可靠性时资源利用率低的问题,提出了特征关联的服务功能链可靠性优化算法。首先,从底层节点的资源可用率、周边资源密集度、全网中心性评价其作为虚拟网络功能（Virtual Network Function,VNF）实例备选节点的优势,从VNF实例的节点度数与带宽流量评价其作为备选对象的优势。其次,逐个对优越性最高的VNF实例选择备份底层节点,直到所有SFC的可靠性满足客户要求。在为VNF实例选择备份底层节点时,提出了基于资源特征关联感知的备份节点选择算法,将当前VNF实例所映射的底层节点与其他待备份VNF实例的底层节点的邻接节点进行关联,为当前VNF实例选择最优备份底层节点的同时,减少为其他待备份VNF实例选择备份底层节点时链路的开销。仿真结果表明,相比于已有算法,该SFC可靠性优化算法下备份实例数降低了约29%,备份带宽资源消耗降低了约42%,SFC接受率提升了约12%。     

基于知识图谱的服务功能链在线部署算法

沉浸式云XR、全息通信等新型网络业务的出现对网络服务质量提出了更高的要求。为保证网络服务的可获得性,在实施虚拟网络功能部署时,必须根据网络功能的属性以及之间的依赖关系,将网络服务的时延、可靠性等控制在一定的服务质量等级内。然而邻接矩阵、边表等传统的网络表征形式无法涵盖这些关键网络信息,造成算法在输入阶段的信息缺失,进而导致计算结果的偏差。因此,为了准确提取用户需求,反映网络资源的动态变化,采用知识图谱对网络及其业务进行表征,提出了一种基于知识图谱的服务功能链在线部署算法。所提算法对网络业务请求与底层网络分别进行知识提取,构建或更新相应的知识图谱,分解出它们的关系集合,并基于此设计了一种基于编辑距离的关系对齐方法,指导复杂依赖关系下的服务功能链在线部署。实验表明,所提算法使复杂网络环境下的服务功能链部署请求接收率提高了10%～15%,网络平均能耗降低了约13%,且复杂度低,时效性较强。     

基于深度强化学习的服务功能链可靠部署算法

针对当前关于服务功能链(SFC)的部署问题都未考虑到虚拟网络功能(VNF)的失效重要度,该文提出了基于深度强化学习的SFC可靠部署算法。首先建立VNF和虚拟链路可靠映射模型,为重要的VNF设置高可靠性需求,并通过链路部署长度限制尽可能保证虚拟链路可靠性需求。其次,以负载均衡为资源协调原则,与VNF可靠性联合优化,最终使用深度强化学习得到服务功能链部署策略。另外,提出了基于重要度的节点备份和链路备份策略,用于应对部署过程中VNF/链路可靠性难以满足的情况。仿真结果表明,该文的可靠部署算法在保证可靠性需求的基础上能够有效减少SFC失效损失,同时使虚拟网络更加稳定可靠。

     

面向时延与可靠性优化的服务功能链部署方法

针对5G网络高可靠性、低时延的服务需求,该文提出一种面向时延与可靠性优化的服务功能链(SFC)部署(DROSD)方法。在不预留冗余资源的情况下,首先通过功能互斥约束来确定SFC中相邻虚拟网络功能(VNF)是否可聚合;其次通过功能性约束、资源约束选择可聚合物理节点集合,实现负载均衡,提高SFC可靠性;然后通过跳数约束进行优化,进一步筛选可聚合物理节点集合以降低SFC的端到端时延;最后通过节点可用资源、节点度以及与原节点跳数指标进行降序排列,取最大值物理节点部署VNF。SFC的路由选择,采用K-最短路径算法。仿真实验表明,该文所提算法提高了请求接受率、长期平均收益开销比,增强了SFC可靠性,降低了端到端时延,减小了平均带宽开销。     

面向软件定义网络的服务功能链优化部署算法研究

针对网络功能虚拟化(NFV)环境下,现有服务功能链部署方法无法在优化映射代价的同时保证服务路径时延的问题,该文提出一种基于IQGA-Viterbi学习算法的服务功能链优化部署方法。在隐马尔可夫模型参数训练过程中,针对传统Baum-Welch算法训练网络参数容易陷入局部最优的缺陷,改进量子遗传算法对模型参数进行训练优化,在每一迭代周期内通过等比例复制适应度最佳种群的方式,保持可行解多样性和扩大空间搜索范围,进一步提高模型参数的精确度。在隐马尔科夫链求解过程中,针对隐含序列无法直接观测这一难点,利用Viterbi算法能精确求解隐含序列的优势,解决有向图网络中服务路径的优化选择问题。仿真实验结果表明,与其它部署算法相比,所提IQGA-Viterbi学习算法能有效降低网络时延和映射代价的同时,提高了网络服务的请求接受率。

     

基于重叠网络结构的服务功能链时空优化编排策略

网络功能虚拟化(NFV)的引入大幅降低了互联网业务的运营成本。针对现有的服务功能链(SFC)编排方法无法在优化底层资源的同时保证业务时延性能的问题,该文提出一种基于重叠网络结构的SFC时空优化编排策略。在将计算、网络资源与细粒度时延约束纳入考虑的基础上,该策略通过建立重叠网络模型实现了计算与网络资源的分离,将构建SFC所需的资源开销与相关时延共同抽象化为重叠网络链路权重,从而使SFC编排问题转化为易于求解的最短路径问题。对于需要批量处理的SFC集合设计了基于重叠网络的模拟退火迭代优化编排算法(ONSA)。通过对比实验证明了该策略下编排方案的平均端到端时延、链路资源占用率与运营开销相对其他方案分别降低29.5%, 12.4%与15.2%,请求接受率提高22.3%,虚拟网络功能(VNF)负载均衡性能得到显著提升。     

运营商网络中面向时延优化的服务功能链迁移重配置策略

在引入网络功能虚拟化（NFV）后,运营商能基于通用的计算和网络平台提供更具弹性的服务.本文研究基于NFV的运营商网络中的服务功能链（SFC）的迁移重配置问题.首先分析了业务流的动态性导致的运营商网络中资源利用率下降和负载不均的问题,然后在将计算资源、网络资源和业务流端到端限制等条件纳入考虑的基础上,建立了面向业务流服务时延优化的数学模型,并证明了SFC迁移重配置问题的NP性.进一步基于遗传算法提出了一种易于部署的启发式SFC迁移重配置策略.通过详细的对比实验表明算法在端到端时延、链路带宽资源占用率和通用服务器资源占用率等关键指标上优于其它方案12.3%、10.9%和15.8%以上.这进一步说明所提出方案通过对运营商网络资源的合理调配,在保证业务流服务质量的同时有效改善了资源利用效率.     

基于深度Q网络的在线服务功能链部署方法

针对5G网络资源状态动态变化和网络模型高维度下服务功能链部署的复杂性问题,该文提出一种基于深度Q网络的在线服务功能链部署方法(DeePSCD)。首先,为描述网络资源动态变化的特征,将服务功能链部署建模成马尔可夫决策过程,然后,针对系统资源模型的高维度问题采用深度Q网络的方法进行在线服务功能链部署策略求解。该方法可以有效描述网络资源状态的动态变化,特别是深度Q网络能有效克服求解复杂度,优化服务功能链的部署开销。仿真结果表明,所提方法在满足服务时延约束条件下降低了服务功能链的部署开销,提高了运营商网络的服务请求接受率。     

Discrete particle swarm optimization based multi-objective service path constructing algorithm

Aiming at previous research primarily focused on constructing service paths with a single objective,for exam-ple,latency minimization,cost minimization or load balance,which ignored the overall performance of constructed ser-vice paths,a multi-objective service path constructing algorithm based on discrete particle swarm optimization (MOPSO) was proposed.To promote the convergence rate and improve constructing performance,the criterions for selecting can-didate physical nodes and paths were explored,and a particle position initialization and update strategy (PIFC) was de-signed.Simulation experiments show that the proposed algorithms can improve the overall quality of service paths and increase the success rate and long-term average revenue.     

Cross-domain service chain mapping mechanism based on Q-learning

A virtual service resources controlling architecture with regional centralized management and global coordinated scheduling was proposed for the problem of cross-domain service chain mapping in SDNFV environment. On this basis, an effective mapping framework was built and the cross-domain mapping problem was modeled as an ILP with the purpose of minimizing mapping cost. A partitioning algorithm was designed to solve the problem based on Q-learning mechanism under this framework. Simulation results show that the performances of this method are better than other traditional methods on average partition time, average mapping cost, and acceptance ratioof service chain mapping request.     

Service chain mapping algorithm based on reinforcement learning

A service chain resource scheduling architecture of multi-agent based on artificial intelligence technology was proposed.Meanwhile,a service chain mapping algorithm based on reinforcement learning was designed.Through the Q-learning mechanism,the location of each virtual network element in the service chain was determined according to the system status and the reward and punishment feedback after the deployment.The experimental results show that compared with the classical algorithms,the algorithm effectively reduces the average transmission delay of the service and improves the load balance of the system.     

网络功能虚拟化中的性能感知服务组合优化

服务功能链是网络功能虚拟化的重要支撑,为了构建满足功能和性能需求的服务功能链,需要建立服务的性能模型,从而产生基于性能的服务组合优化问题。一种基于性能模型的服务组合优化问题被建模,并针对复杂约束情况下的无效解干扰,提出了改进的模拟退火算法,该算法包含基于层次属性的产生函数和基于偏离度的目标函数。仿真结果表明,该算法提高了21%的服务组合成功率,同时降低了组合成本和时间消耗。仿真结果验证了所提算法的有效性。     

Service function chain construct algorithm based on reliability

With the continuous expansion of network size and content quantity,network operators often face the problem of data transmission interruption,thus how to improve the reliability of data transmission has become a research hotspot.Started with the huge advantages of NFV environment over traditional TCP/IP architecture based on the background that every network node has the capability of security service,a reliability algorithm for constructing functional service chains was proposed.First,the network topology was introduced and abstracted it into a digraph for a better analyzation.Then,several quantitative indices were used to define the upper and lower bounds of failure probability and resolved the model through a heuristic algorithm.The experimental results show that the proposed algorithm increases the reliability of service chain selection and spends less time.     

Service function chain embedding algorithm with wireless multicast in mobile edge computing network

To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L_p norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.     

基于改进灰狼优化算法的DV-Hop定位算法

针对无线传感器网络节点DV-Hop定位算法由于节点分布不均,距离估计不准确,导致定位精度较低的问题,提出了一种基于改进灰狼优化算法的DV-Hop定位算法,采用先进的灰狼优化算法以寻找最优值的方式得到未知节点、坐标。同时,为进一步提高优化算法的寻优能力,克服可能出现局部最优的情况,将优化算法与免疫算法相结合,提高优化算法中灰狼种群的多样性,进而提高对最优解的搜索能力,达到提高定位精度的目的。实验结果表明,相对于普通的DV-Hop定位算法和普通的灰狼优化算法,改进之后的定位算法精度更高。     

Binomial distribution based grey wolf optimization algorithm for channel estimation in wireless communication system

Several input high-data-rate transmissions over broadband wireless channels are possible using multiple input multiple output (MIMO) systems paired with orthogonal frequency division multiplexing (OFDM) technology. Channel estimation is an essential technique and a necessary component of MIMO-OFDM systems. However, the noise will be there in MIMO-OFDM due to the environment. As a result, the wireless system performs degrades in terms of bit error rate (BER). The suggested method offers a better pilot pattern strategy for MIMO-OFDM and an efficient power allocation to address this issue. The binomial distribution-based grey wolf optimization (BDGWO) algorithm is proposed to identify the optimal pilot patterns. The power is then adaptively distributed to each transmit antenna to increase the spectral efficiency and maximum channel capacity through an adaptive neuro-fuzzy inference system with a sigmoid membership function (SMFANFIS). The best pilot patterns in PDGSIP (pilot design with generalized shift invariant property) were determined using the BDGWO algorithm based on the binomial distribution. According to the simulation results, the proposed BDGWO established pilot design with generalized shift invariant property (BDGWO-DGSIP) achieves higher performance compared other existing approaches such as PDGSIP, TPDGSIP, and LS in terms of NMSE, BER, and SER. Compared to the PDGSIP technique, the proposed PDGSIP-BDGWO system minimizes NMSE at 10%, BER at about 12%, and SER at 15%.     

Reliability‐aware service chaining mapping in NFV‐enabled networks

Network function virtualization can significantly improve the flexibility and effectiveness of network appliances via a mapping process called service function chaining. However, the failure of any single virtualized network function causes the breakdown of the entire chain, which results in resource wastage, delays, and significant data loss. Redundancy can be used to protect network appliances; however, when failures occur, it may significantly degrade network efficiency. In addition, it is difficult to efficiently map the primary and backups to optimize the management cost and service reliability without violating the capacity, delay, and reliability constraints, which is referred to as the reliability‐aware service chaining mapping problem. In this paper, a mixed integer linear programming formulation is provided to address this problem along with a novel online algorithm that adopts the joint protection redundancy model and novel backup selection scheme. The results show that the proposed algorithm can significantly improve the request acceptance ratio and reduce the consumption of physical resources compared to existing backup algorithms.