基于最长有效功能序列的服务功能链部署算法

服务功能链的服务性能取决于功能的部署位置和数据传输路径的选择。针对资源有限的网络中的服务功能链部署问题,该文设计了一种基于最长有效功能序列(LEFS)的服务功能链部署算法,以功能复用和带宽需求联合优化为目标,在控制路径长度的同时根据LEFS逐步搜索中继节点,直至满足服务请求。仿真结果表明,该算法能够均衡网络资源,同时优化网络的功能部署率和带宽利用率,与其他算法相比,网络资源利用率降低了10%,可以支持更多的服务请求,且算法计算复杂度低,可以实现对服务请求的快速响应。     

基于改进灰狼优化算法的服务功能链映射算法

随着工业互联网、车联网、元宇宙等新型互联网应用的兴起，网络的低时延、可靠性、安全性、确定性等方面的需求正面临严峻挑战。采用网络功能虚拟化技术在虚拟网络部署过程中，存在服务功能链映射效率低与部署资源开销大等问题，联合考虑节点激活成本、实例化开销，以最小化平均部署网络成本为优化目标建立了整数线性规划模型，提出基于改进灰狼优化算法的服务功能链映射（improved grey wolf optimization based service function chain mapping，IMGWO-SFCM）算法。该算法在标准灰狼优化算法基础上添加了基于无环K最短路径（K shortest path，KSP）问题算法的映射方案搜索、映射方案编码以及基于反向学习与非线性收敛改进三大策略，较好地平衡了其全局搜索及局部搜索能力，实现服务功能链映射方案的快速确定。仿真结果显示，该算法在保证更高的服务功能链请求接受率下，相较于对比算法降低了11.86%的平均部署网络成本。     

基于改进深度强化学习的虚拟网络功能部署优化算法

针对网络功能虚拟化/软件定义网络 (NFV/SDN)架构下,网络服务请求动态到达引起的服务功能链(SFC)部署优化问题,该文提出一种基于改进深度强化学习的虚拟网络功能(VNF)部署优化算法。首先,建立了马尔科夫决策过程 (MDP)的随机优化模型,完成SFC的在线部署以及资源的动态分配,该模型联合优化SFC部署成本和时延成本,同时受限于SFC的时延以及物理资源约束。其次,在VNF部署和资源分配的过程中,存在状态和动作空间过大,以及状态转移概率未知等问题,该文提出了一种基于深度强化学习的VNF智能部署算法,从而得到近似最优的VNF部署策略和资源分配策略。最后,针对深度强化学习代理通过ε贪婪策略进行动作探索和利用,造成算法收敛速度慢等问题,提出了一种基于值函数差异的动作探索和利用方法,并进一步采用双重经验回放池,解决经验样本利用率低的问题。仿真结果表示,该算法能够加快神经网络收敛速度,并且可以同时优化SFC部署成本和SFC端到端时延。     

基于改进深度强化学习的虚拟网络功能部署优化算法

针对网络功能虚拟化/软件定义网络(NFV/SDN)架构下,网络服务请求动态到达引起的服务功能链(SFC)部署优化问题,该文提出一种基于改进深度强化学习的虚拟网络功能(VNF)部署优化算法.首先,建立了马尔科夫决策过程(MDP)的随机优化模型,完成SFC的在线部署以及资源的动态分配,该模型联合优化SFC部署成本和时延成本,同时受限于SFC的时延以及物理资源约束.其次,在VNF部署和资源分配的过程中,存在状态和动作空间过大,以及状态转移概率未知等问题,该文提出了一种基于深度强化学习的VNF智能部署算法,从而得到近似最优的VNF部署策略和资源分配策略.最后,针对深度强化学习代理通过ε贪婪策略进行动作探索和利用,造成算法收敛速度慢等问题,提出了一种基于值函数差异的动作探索和利用方法,并进一步采用双重经验回放池,解决经验样本利用率低的问题.仿真结果表示,该算法能够加快神经网络收敛速度,并且可以同时优化SFC部署成本和SFC端到端时延.     

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.     

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。     

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。     

基于联合备份的服务功能链可靠性保障的部署方法

在网络功能虚拟化(NFV)环境中,针对服务功能链(SFC)部署时的可靠性问题,该文提出对备份虚拟网络功能选择、备份实例放置和服务功能链部署的联合优化方法。首先,定义一个单位开销可靠性提高值的虚拟网络功能衡量标准,改进备份虚拟网络功能选择方法;其次,采用联合备份的方式调整相邻备份实例之间的放置策略,以降低带宽资源开销;最后,将整个服务功能链可靠性保障的部署问题构建成整数线性规划模型,并提出一种基于最短路径的启发式算法,克服整数线性规划求解的复杂性。仿真结果表明,该方法在优先满足网络服务可靠性需求的同时,优化资源配置,提高了请求接受率。     

一种新的 QoS 全局最优 Web 服务选择算法

针对运用单目标优化算法求解基于 QoS 的 Web 服务选择问题的不足,设计了一种新的 QoS 全局最优Web 服务选择算法.该算法同时优化组合服务的多维 QoS 属性的多个目标函数,并产生 QoS 全局最优的 Pareto 最优解集.首先建立服务选择问题的多目标优化数学模型,然后采用归档式多目标模拟退火设计该算法以优选 Web服务.实验结果表明了该算法是可行的,实现了全局 QoS 最优化的组合服务.     

基于深度动态贝叶斯网络的服务功能链故障诊断算法

针对5G端到端网络切片场景下底层物理节点出现故障会导致运行在其上的多条服务功能链出现性能异常的问题,该文提出一种基于深度动态贝叶斯网络(DDBN)的服务功能链故障诊断算法。首先根据网络虚拟化环境下故障的多层传播关系,构建故障与症状的依赖图模型,并采用在物理节点监测其上多个虚拟网络功能相关性能数据的方式收集症状。其次,考虑到基于软件定义网络(SDN)和网络功能虚拟化(NFV)的架构下网络症状观测数据的多样性以及物理节点和虚拟网络功能的空间相关性,引入深度信念网络对观测数据特征进行提取,使用加入动量项的自适应学习率算法对模型进行微调以加快收敛速度。最后,利用故障传播的时间相关性,引入动态贝叶斯网络对故障根源进行实时诊断。仿真结果表明,该算法能够有效地诊断故障根源且具有良好的诊断准确度。     

Service function chain deployment algorithm based on optimal weighted graph matching

Service function chain can support flexible network service requirement by linking virtual network functions.Aiming at the problem of service function chain deployment in a resource-constrained network,an algorithm for service function chain deployment based on optimal weighted graph matching was proposed.The service function chains was composed into graphs of functional topography,and the optimal matching results between graphs of functional topology and physical topology was obtained using eigendecomposition approach,and furtherly the matching results by hill-climbing method was optimized.Simulation results show that,the proposed algorithm can reduce the required bandwidth to deploy service function chains,balance the load of nodes and bandwidth of links,and support more service requests.What is more,the algorithm has a lower computation complexity and higher time efficience.     

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.     

Joint service function chain deploying and path selection for bandwidth saving and VNF reuse

Service function chain (SFC) is managed as a set of chained virtual network functions (VNFs) in a particular order. The efficiency of a network closely depends on how these VNFs are deployed in the network. In this paper, we study the joint problem of service function chain deploying and path selection for bandwidth saving and VNF reuse. We first describes the problem as a multiobjective and multirestriction problem, and then a heuristic service function chain deployment algorithm based on longest function assignment sequence is proposed. Via simulations, we show that our design can reduce the number of deployed VNFs and link bandwidth requirement jointly and serve more requests than other algorithms.     

Cost minimization in placing service chains for virtualized network functions

Network function virtualization (NFV) places network functions onto the virtual machines (VMs) of physical machines (PMs) located in data centers. In practice, a data flow may pass through multiple network functions, which collectively form a service chain across multiple VMs residing on the same or different PMs. Given a set of service chains, network operators have two options for placing them: (a) minimizing the number of VMs and PMs so as to reduce the server rental cost or (b) placing VMs running network functions belonging to the same service chain on the same or nearby PMs so as to reduce the network delay. In determining the optimal service chain placement, operators face the problem of minimizing the server cost while still satisfying the end‐to‐end delay constraint. The present study proposes an optimization model to solve this problem using a nonlinear programming (NLP) approach. The proposed model is used to explore various operational problems in the service chain placement field. The results suggest that the optimal cost ratio for PMs with high, hybrid, and low capacity, respectively, is equal to 4:2:1. Meanwhile, the maximum operating utilization rate should be limited to 55% in order to minimize the rental cost. Regarding quality of service (QoS) relaxation, the server cost reduces by 20%, 30%, and 32% as the end‐to‐end delay constraint is relaxed from 40 to 60, 80, and 100 ms, respectively. For the server location, the cost decreases by 25% when the high‐capacity PMs are decentralized rather than centralized. Finally, the cost reduces by 40% as the repetition rate in the service chain increases from 0 to 2. A heuristic algorithm, designated as common sub chain placement first (CPF), is proposed to solve the service chain placement problem for large‐scale problems (eg, 256 PMs). It is shown that the proposed algorithm reduces the solution time by up to 86% compared with the NLP optimization model, with an accuracy reduction of just 8%.     

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.     

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%。     

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.     

异构无线网络的自适应垂直切换判决算法

在未来的异构环境中,网络间的垂直切换将对QoS保证产生重要影响。针对移动终端在异构网络间切换不理想的问题,提出了一种自适应的垂直切换判决算法。采用基于用户多应用的代价函数对接入网络进行评估与选择,综合考虑移动终端当前的电池电量,判断当前业务是否需要进行网络切换,使移动终端能自适应地进行切换判决。仿真结果表明,该算法可以有效地延长移动终端的工作时间,减少乒乓效应,提高系统的切换性能,改善业务的QoS。