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.     

面向多节点故障的生存性虚拟网络映射方法

针对网络虚拟化环境中虚拟网络的生存性问题,提出一种面向多节点故障的生存性虚拟网络映射方法。该方法预先在物理网络中按比例留出备份资源并根据拓扑属性构造节点的候选集合,在发生节点故障后从故障节点的候选集合中选取重映射目标并动态分配预留的备用资源来恢复受到影响的虚拟网络,提高长期运营利润。仿真实验在多节点故障模型下从适用性、稳定性及性能方面验证了该方法的有效性。     

一种基于主-备份节点同步映射的VNF热备份机制

网络功能虚拟化实现了网络功能与专属硬件平台的分离,虚拟网络功能（Virtual Network Function,VNF）通过动态组合形成服务功能链,实现同一硬件平台部署不同的功能服务。针对软件定义网络功能服务可靠性和物理网络资源效率问题,提出了一种基于主备份节点同步映射的VNF节点热备份机制。首先,选择满足节点资源约束的物理节点为主备份节点候选映射对;然后,选择满足带宽约束的最短物理链路为服务路径,实现节点与链路资源总开销的优化。实验结果表明,与传统的备份机制相比,该方法能够满足电信级高可靠性的服务需求,降低了VNF节点备份资源总开销,提高了服务请求的接受率和底层物理网络资源的利用率。     

信任感知的安全虚拟网络映射算法

针对网络虚拟化技术带来的新的安全威胁,将信任关系和信任度引入到虚拟网络资源分配中,量化分析了网络虚拟化环境中的安全问题,构建了安全虚拟网络映射的数学模型,并在映射过程中考虑节点的局部和全局重要性,采用逼近理想排序法方法对节点进行多属性重要度排序,提出了一种信任感知的安全虚拟网络映射算法。仿真结果表明,该算法在满足虚拟网络请求可信需求的条件下,获得了较好的映射成功率、映射收益和资源利用率。     

MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略

为了有效改善多集群共存的移动边缘网络中业务流端到端服务时延,提出了一种基于改进遗传模拟退火算法的虚拟网络功能部署策略。通过开放Jackson排队网络对移动业务流的时延进行最优化建模,在证明其NP性的基础上提出了将遗传算法与模拟退火算法相结合的求解策略,该策略通过对服务节点的提前映射机制避免了可能带来的网络拥塞,并通过个体的约束性判断和纠正遗传的方法避免了局部最优的出现。在不同的服务请求量、服务节点规模、集群数量及虚拟网络功能之间的逻辑连接关系等参数下的对比实验表明,该策略能提供更低时延的端到端服务,使时延敏感类移动业务获得更好体验。     

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。     

A unified enhanced particle swarm optimization‐based virtual network embedding algorithm

Virtual network (VN) embedding is a major challenge in network virtualization. In this paper, we aim to increase the acceptance ratio of VNs and the revenue of infrastructure providers by optimizing VN embedding costs. We first establish two models for VN embedding: an integer linear programming model for a substrate network that does not support path splitting and a mixed integer programming model when path splitting is supported. Then we propose a unified enhanced particle swarm optimization‐based VN embedding algorithm, called VNE‐UEPSO, to solve these two models irrespective of the support for path splitting. In VNE‐UEPSO, the parameters and operations of the particles are well redefined according to the VN embedding context. To reduce the time complexity of the link mapping stage, we use shortest path algorithm for link mapping when path splitting is unsupported and propose greedy k‐shortest paths algorithm for the other case. Furthermore, a large to large and small to small preferred node mapping strategy is proposed to achieve better convergence and load balance of the substrate network. The simulation results show that our algorithm significantly outperforms previous approaches in terms of the VN acceptance ratio and long‐term average revenue. Copyright © 2012 John Wiley & Sons, Ltd.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

应用粒子群优化的绿色虚拟网络映射算法

绿色网络是近年来网络技术研究的热点,以节能为优化目标的虚拟网络映射算法成为基于网络虚拟化技术中资源分配研究的重点.提出了应用粒子群优化的绿色虚拟网络映射算法,重定义粒子群优化算法中的参数和粒子进化行为,以关闭底层网络节点和链路数量最多为适应度函数,在较低的算法复杂度条件下,获得绿色虚拟网络映射的最优解.仿真结果表明,与对比算法相比,静态环境下所提算法的运行时间大幅度降低;动态环境下所提算法的节点关闭率、链路关闭率、虚拟网络请求接受率均有所提升,算法运行时间也大大缩短.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

基于链路可靠性的无线虚拟网络分配方法

针对无线网络链路可靠性差影响无线虚拟网络的分配这一问题,提出了一种基于链路可靠性的无线虚拟网络分配算法（WVNEA-LR）。该算法通过物理网络拓扑预处理和允许同一个虚拟请求中的多个虚拟节点映射到同一个物理节点上的方法,提高了Vn构建成功率和节约了物理链路资源。利用Q因子改善了因拓扑分配稀疏时Vn构建成功率低的问题。此外,WVNEA-LR的节点分配为链路分配作了准备,并通过选择可靠性高的承载路径保证了分配后的Vn具有高可靠性。仿真结果表明, WVNEA-LR获得较好的虚拟网络构建成功率、较高的收益成本比和资源利用率。     

Research of embedding algorithm for wireless network virtualization

Wireless network virtualization has become one of the promising techniques in fifth generation mobile networks (5G),and the virtual network embedding plays a very important role.A model was formulated to describe the problem and a wireless virtual network embedding algorithm based on joint bandwidth and power allocation (WVNEA-JBP) was put forward,which selected the suitable node and path to serve the data transmission from virtual networks.In order to minimize the overhead while satisfy the higher capacity request,this algorithm considered the impact of interference of wireless links and supports path splitting.A case study was introduced to show how WVNEA-JBP to work.Furthermore,simulation results show that WVNEA-JBP is able to accomplish the efficient utilization of network resources and improve the accept rate of virtual network requests.     

基于链路优先的快速协同虚拟网络映射算法

在网络虚拟化环境中,为了减少链路映射成本和提高收益,提出了一种链路优先的协同映射算法。该算法交替映射链路和节点,并使用贪心思想优先将带宽资源需求较大的虚拟链路映射到跳数较少的物理路径上,最终达到降低虚拟网络平均消耗和提高虚拟网络接受率的目的。仿真结果表明,与已有算法相比,所提算法节约了链路映射的成本,提高了虚拟网络接受率和物理网络收益,算法运行时间也有效缩短。     

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。     

基于节点可靠性感知和共享路径保护的虚拟网映射算法研究

网络虚拟化技术为目前的网络架构提供了一种有效的扩展手段。近年来,底层网络基础设施失效事件频发,因此如何提高虚拟网络的可靠性成为目前该领域一个研究热点。对在保证虚拟网络可靠性的同时如何最小化底层网络映射开销问题进行研究,设计了一个新的启发式算法对其进行求解。实验表明,相比其他算法,所提算法网络带宽资源开销更低。     

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%。     

一种基于模糊逻辑的虚拟服务迁移策略

在网络虚拟化环境中,用户对虚拟网络的需求不是一成不变的,而是受到用户行为、位置、移动性等多方面因素的影响。针对用户需求变化的应用场景,为了实现绿色节能、低能耗、高效利用的虚拟资源迁移与调度,提出了基于模糊逻辑推理的虚拟服务迁移机制。该机制能够有效地对用户需求的变化进行判断,根据用户需求的变化情况,对虚拟服务进行迁移,既提高了资源利用率,也提升了用户的服务体验。     

Distributed parallel algorithms for online virtual network embedding applications

Network virtualization (NV) has ubiquitously emerged as an indispensable attribute to enable the success of the forthcoming virtualized networks (eg, 5G network and smart Internet of Things [IoT]). Virtual network embedding (VNE) is the major challenge in NV that allows multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. A great number of VNE algorithms have been proposed, but over the past decades, most of them are only targeting for VNE node mapping. In this paper, we propose two distributed parallel genetic algorithms, which are based on two versions of crossover and mutation schemes, for online VN link embedding problems with low latency and high efficiency. Furthermore, we conduct a time analysis on the executing time of independently distributed parallel computing machines in details. This comprehensive analysis validates the parallel computing scalability on an identical number of predefined parallel machines. Extensive simulations have shown that our proposed algorithms can achieve better performance than integer linear programming (ILP)–based solutions while meeting the stringent time requirements for online VN embedding applications. Our proposed algorithms yield superior performance in running time with 32.78% up to 1727.8% faster than existing popular VNE algorithms. Additionally, the theoretical analysis indicates that the execution time can be reduced to logarithmic times by applying proposed distributed parallel algorithms.     

基于遗传算法的时延受限代价最小组播路由选择方法

目前多媒体业务的涌现对网络提出了更高的要求。希望既能满足实时性，又能够高效地利用网络资源。本文提出了一种基于遗传算法的组播路由选择方法，该方法在满足时延限制条件的基础上寻找代价最小的组播树。实验表明，该算法收敛速度快，可靠性高，能够满足多媒体网络对实时性的要求。     

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.