基于Openflow网络的高可靠性虚拟网络映射算法

该文基于Openflow网络提出了具有容错能力的虚拟网络映射模型,并且采用蚁群算法对其进行求解。针对虚拟网络的故障恢复机制,提出了区分用户优先级的故障恢复算法(Priority_Diff),该算法为用户提供不同的网络可靠性级别,对高级用户采用提前映射的备份路径替代故障链路,对低级用户重新映射故障链路;设计了故障备份链路重映射(BLRM)算法,将故障链路中的备份资源迁移到相邻链路,增强了备份链路的可用性。最后,通过仿真实验,从虚拟网络故障修复率、虚拟网络成功运行率和工作链路资源利用率3个方面验证了所提算法的优越性。     

虚拟网络的服务迁移技术研究

提出了一种面向单域环境的虚拟网络迁移算法。基于动态服务质量计算,定义了迁移判定因子,量化迁移代价与收益参数,动态评判迁移的时机,并选择判定因子最小的虚拟节点作为服务迁移节点。该算法可以有效地减少服务延迟,提高用户的服务体验质量,实现网络高效的资源管理和降低能耗的目标。     

基于深度信念网络资源需求预测的虚拟网络功能动态迁移算法

针对5G网络场景下缺乏对资源需求的有效预测而导致的虚拟网络功能(VNF)实时性迁移问题,该文提出一种基于深度信念网络资源需求预测的VNF动态迁移算法。该算法首先建立综合带宽开销和迁移代价的系统总开销模型,然后设计基于在线学习的深度信念网络预测算法预测未来时刻的资源需求情况,在此基础上采用自适应学习率并引入多任务学习模式优化预测模型,最后根据预测结果以及对网络拓扑和资源的感知,以尽可能地减少系统开销为目标,通过基于择优选择的贪婪算法将VNF迁移到满足资源阈值约束的底层节点上,并提出基于禁忌搜索的迁移机制进一步优化迁移策略。仿真表明,该预测模型能够获得很好的预测效果,自适应学习率加快了训练网络的收敛速度,与迁移算法结合在一起的方式有效地降低了迁移过程中的系统开销和服务级别协议(SLA)违例次数,提高了网络服务的性能。     

基于集成深度神经网络流量预测的动态网络切片迁移算法

针对5G网络切片(NS)场景下由于缺乏提前对物理网络资源进行感知而导致切片迁移滞后的问题,该文提出一种基于集成深度神经网络流量预测的动态切片调整和迁移算法(DSAM)。首先建立了基于计算、内存、带宽资源配置的网络总惩罚模型;其次,提出基于集成深度神经网络的流量预测算法预测未来网络流量情况,并根据流量类型的不同将其转换成对未来时刻物理网络的资源占用及切片的资源需求感知;最后,根据感知结果,以尽可能大地降低运营商惩罚为目标,通过动态切片调整和迁移策略将虚拟网络功能(VNF)和虚拟链路迁移到满足资源限制的物理节点和链路上。仿真结果表明,所提算法有效提高了切片迁移的效率和网络资源利用率。     

运营商网络中基于深度强化学习的服务功能链迁移机制

为改善运营商网络提供的移动服务体验,该文研究服务功能链(SFC)的在线迁移问题。首先基于马尔可夫决策过程(MDP)对服务功能链中的多个虚拟网络功能(VNF)在运营商网络中的驻留位置迁移进行模型化分析。通过将强化学习和深度神经网络相结合提出一种基于双深度Q网络(double DQN)的服务功能链迁移机制,该迁移方法能在连续时间下进行服务功能链的在线迁移决策并避免求解过程中的过度估计。实验结果表明,该文所提出的策略相比于固定部署算法和贪心算法在端到端时延和网络系统收益等方面优势明显,有助于运营商改善服务体验和资源的使用效率。     

基于时空特征提取的智能网络切片算法

5G网络作为新一代的移动通信网络,提供了差异多样的各类业务服务,而通过网络切片可以将5G网络的资源虚拟化并更为有效地分配给各类服务.然而,当用户的服务需求发生变化时,需要及时地进行切片资源的重新分配和管理.为了准确适配业务需求的时空变化,提升切片性能,考虑采用深度强化学习的人工智能算法对切片资源进行实时管理,提出了基于...     

基于LDAP与Kerberos的认证系统研究与设计——广东非物质文化遗产信息管理系统设计

为了解决现有非物质文化遗产网络资源分散管理系统各自独立,网络用户信息凌乱并严重重复不能得到有效访问控制等问题。通过对现有网络情况、用户使用需求以及网络服务技术进行分析,设计基于LDAP目录服务与Kerberos认证机制相结合的统一认证系统,提出目录管理与跨域访问认证的模式。集中管理系统资源和用户信息,提高了资源利用率和网络系统资源的安全性。     

一种星地融合网络动态服务迁移重构方法

星地融合网络架构下,用户业务需求和网络拓扑的动态性都会导致原有应用场景下的资源配置与服务部署不再完全适用,如何高效获取最优服务迁移策略是提供连续且高质量业务保障和服务支持的关键.针对业务需求调整和网络拓扑变化引发的服务迁移重构问题,提出了一种模糊逻辑与量子遗传算法相结合的动态服务迁移重构策略优化算法——FQGA-SR....     

云计算平台生存性研究

本文主要研究云计算平台的生存性问题,设计并提出一种云计算平台生存性机制,利用虚拟化技术把硬件环境与运行系统进行强隔离后,通过把虚拟机迁移到稳定可靠的硬件设备上来保证虚拟机承载的服务的可用性,以提升云计算平台在大规模系统故障发生的情况下的存活能力。具体工作包括设计基于基础设施运行状况的实时监控、基于虚拟机的调度优化、云服务的自动迁移调度和服务自动恢复、以及在异常出现情况下的预警和告警的机制。本文的主要贡献包括确立了云计算平台的生存性需求,提出了基于虚拟资源迁移的生存性机制以及实现并整合生存性机制到现有的Vebula私有云基础设施平台。     

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...     

云环境下基于迁移的虚拟机集群优化算法

针对云环境中虚拟机集群负载不均衡问题,提出一种基于虚拟机迁移的集群优化算法。通过对节点负载的实时监测,动态调整各种资源的权重,根据资源权重选择可最大程度降低主机负载的虚拟机进行迁移。该算法利用预测机制,消除主机资源利用率的临时越界引起的不必要的虚拟机迁移。在选择目标节点时,采用多目标决策法,兼顾多资源匹配率,服务级目标违背率(SLA)等多种管理目标。实验结果表明,与同类型的负载均衡算法相比,该算法能减少迁移次数,降低SLA违背率。     

Towards efficiently migrating virtual networks in cloud-based data centers

With the expansion of cloud computing, virtual network (VN) migration becomes the very perspective technology for saving energy, ensuring Service Level Agreements or improving the survivability of virtual networks in cloud networks. At present, the majority of research on the VN migration, however, are for saving energy or improving resource utilizations, and few of them for the entire virtual network migration for guaranteeing QoS or improving the survivability of virtual networks. Since the regional failure, network maintenance or QoS violation, the service provider generally needs to migrate the VN for guaranteeing the QoS or improving the survivability of virtual networks. In the paper, we research the live migration problem of the virtual network to optimize the virtual network migration performance. To efficient migrate virtual network, we present an effective VN migration method, VNM. To control the cost of migration or migration traffic, based on the VNM algorithm, we present an effective VN migration method with migration traffic control, VNM-MTC. We use two networks as substrate networks to simulate the performances of our presented algorithms. From the experiment, we can see that the total VN reconfiguration cost, total VN redeployment cost, total VN migration cost and blocking ratio of our presented algorithms are better than that of the contrast algorithm.     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

云环境下基于虚拟机动态迁移的调度策略研究

针对目前云环境资源调度采用静态负载均衡策略易于导致资源浪费的问题,提出了一种双限定值的虚拟机动态迁移的调度策略.该策略将当前负载状况与负载过重或过轻时两个限定值比较,选择介于二者之间能耗较低的虚拟机迁移至目标节点.仿真实验表明,该策略能够减少迁移次数,降低虚拟机迁移能耗,从而尽可能达到负载均衡和满足服务等级协议的需求.     

基于KVM的虚拟机Post-Copy动态迁移算法稳定性优化

虚拟机动态迁移技术为虚拟化系统的资源调度提供了强有力的支撑,Post-Copy算法作为虚拟机动态迁移的两个核心算法之一,凭借其总体迁移时间稳定与迁移停机时间短的优点,一直是国内外学者研究的热点问题。对虚拟机的故障容错机制、迁移过程中的内存页面传输方式与缺页错误的关联性,以及QEMU-KVM平台源码进行了深入的研究,提出了基于事务同步的故障容错方法以提升Post-Copy迁移算法的稳定性。试验结果表明,提出的虚拟机Post-Copy迁移优化算法,能保证迁移过程中源端虚拟机故障、目标端虚拟机故障以及网络故障的迅速修复,能通过较小的代价解决稳定性问题,所提出的方法有效地提升了Post-Copy迁移算法的稳定性,也为以后的优化研究方向提供了参考。     

基于合作博弈的多虚拟机实时迁移带宽分配机制

IaaS云计算平台采用虚拟机实时迁移技术进行资源动态调度和管理。在实际应用场景下,需要并行实时迁移多个虚拟机。由于实时迁移算法本身以最大利用带宽的方式进行数据传输,存在着迁移进程间竞争带宽的问题,无法保证带宽全局最优分配,影响整体迁移的性能。提出一种基于合作博弈的多虚拟机实时迁移带宽分配机制,将带宽分配问题建模为一个纳什议价,通过求解纳什议价解得到帕累托最优的带宽分配方案,并在实际的虚拟化平台上进行了实现。实验结果表明,相比标准的并行实时迁移,所提出的带宽分配机制能够公平有效地分配带宽,提高了并行实时迁移的性能。     

一种基于共享路由的传感器网络能量感知服务选择算法(英文)

This paper presents a novel energy-aware algorithm for service composition based on sharing routes in Wireless Sensor Networks (WSNs). The method integrates the resource of the overlapping WSNs to a virtual resource pool in the execution cycles of the workflow. This approach chooses the suitable service instances according to the current execution environment and user requirements with minimum energy consumption. Finally, the performance of sharing routes service composition selection in WSNs has been evalu...     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.     

Mobility aware edge service migration strategy

To address the problem of load imbalance among edge servers and quality of service degradation caused by dynamic changes of user locations in mobile edge computing networks,a mobility aware edge service migration algorithm was proposed.Firstly,the optimization problem was formulated as a mix integer nonlinear programming problem,with the goal of minimizing the perceived delay of user service request.Then,the delay optimization problem was decoupled into the edge service migration and edge node selection sub-problems based on the Lyapunov optimization approach.Thereafter,the fast edge decision algorithm was proposed to optimize the resource allocation and edge service migration under a given radio access strategy.Finally,the asynchronous optimal response algorithm was proposed to iterate out the optimal radio access strategy.Simulation results validate the proposed algorithm can reduce the perceived delay under the service migration cost constraint while comparing with other existing algorithms.