软件定义网络中面向时延和负载的多控制器放置策略

在多控制器管理的软件定义网络(SDN)中,时延和负载是控制器放置问题(CPP)要考虑的重要因素。该文以降低控制器之间的传播时延、流请求的传播时延和排队时延、均衡控制器间负载为目标,提出一种控制器放置及动态调整的策略,其中包括用于初始控制器放置的负载均衡算法(BCRA)和遗传算法(GA),用于动态调整控制器负载的在线调整算法(ADOA)。以上算法均考虑网络连通性。仿真结果表明:在初始控制器放置时,在保证流请求的传播时延、排队时延和控制器传播时延较低的情况下,BCRA部署在中小型网络中时,其负载均衡性能与GA相近且优于k-center和k-means算法;GA部署在大型网络中时,与BCRA, k-center和k-means算法相比,使得负载均衡率平均提高了49.7%。在动态情况下,与现有动态调整算法相比,ADOA可以保证较低排队时延和运行时间的同时,仍能使负载均衡参数小于1.54。     

SDN中基于Q-learning的动态交换机迁移算法

由于网络流量动态变化,控制器负载均衡成为大规模部署软件定义网络研究的重点.提出基于Q-learning的动态交换机迁移算法,首先对软件定义网络中的控制器部署问题建模,再应用Q-learning反馈机制学习实时网络流量,最后根据Q表格将交换机从高负载控制器动态迁移到低负载控制器上,实现控制器的负载均衡.仿真结果表明,所提算法能够获得较低的控制器负载标准方差.     

基于改进遗传算法的SDN控制器放置优化研究

与传统的网络不同,软件定义网络将网络的控制层与数据层分开,从而提供了网络的集中视图,可以灵活控制网络流量。然而在大规模的软件定义网络中不合理的控制器部署会直接影响到网络的性能。为了找到网络中最优的控制器放置位置和数量,同时最小化时延、均衡负载并优化成本,提出一种基于改进遗传算法的求解算法。在该算法中引入贪婪思想并改进变异算子从而改善其收敛速度和寻优能力。此外,在一些真实的拓扑上将该算法与其他求解算法进行对比,结果表明改进的遗传算法性能更好。     

多域SDN网络中多控制器负载均衡算法研究

针对当前多域SDN网络中多个控制器同时运行时会出现低负荷或过载运行状态,严重影响网络正常运行的问题,开展对多控制器负载均衡算法的设计研究。结合多控制器运行特点,判定多控制器三种基本运行状态;在多域SDN网络中,选择需要迁移的交换机和目标控制器;最后结合动态自适应计算的方式,实现多控制器的负载均衡运行。通过对比应用新算法前后的多控制器运行情况得出,在应用新算法后,多个控制器的负荷均能够控制在理想范围内,实现负载均衡,确保多域SDN网络的稳定运行。     

SDN多控制器场景下动态迁移策略

在软件定义网络(SDN)中多控制器部署问题常分为两类,静态部署和动态部署。文中针对静态部署方案在实现上无法实时均衡网络负载的局限性,提出动态场景下的一种多控制器部署策略,以优化网络中的迁移成本、过载次数与连接恢复时间。根据仿真实验,文中的动态迁移方案可将上述指标达到最优,具有理论正确性与现实可行性。     

改进NSGA-Ⅲ的卫星网络多控制器部署方法

软件定义卫星网络能够促使卫星网络管理便利化、网络控制灵活化、网络配置简单化,通过卫星控制器节点可实时掌握卫星网络的状态信息,实现卫星网络资源的集中控制与高效分配。针对卫星网络控制器部署问题研究中,星间链路的可靠性常常被忽略的问题,建立了以降低控制链路时延、提升星间链路的可靠性、维持负载均衡为研究目标,提出了一种改进的非支配排序遗传算法-Ⅲ（Improved Non-dominated Sorting Genetic AlgorithmⅢ,INSGA-Ⅲ）的卫星网络多控制器部署方法。仿真结果表明,基于INSGA-Ⅲ算法的多控制器部署方法能够降低网络时延,同时在星间链路的可靠性和网络负载均衡方面均有显著提升,在时延、负载均衡、可靠性方面,INSGA-Ⅲ相比于INSGA-Ⅱ分别提升约10.12%、97.37%、0.50%,INSGA-Ⅲ相比于NSGA-Ⅲ分别提升约6.25%、95.49%、0.44%。     

一种基于负载均衡的高能效LLN路由算法

针对低功耗有损网络(LLN)中由于节点部署不均匀易导致负载不均衡的问题,提出一种基于负载均衡的高能效LLN路由算法(EELB-RPL)。通过将链路质量、传输时延、节点剩余能量、吞吐量以及拥塞检测因子等因素相结合选出最优父节点;再通过调整溪流计时器,使节点根据网络拓扑密度调整自身抑制机制,避免了抑制不公平性导致负载不均衡。仿真结果表明,所提算法与现有算法相比较,能够有效实现负载均衡。其中,数据包投递率提升了14.6%,根节点吞吐量提升了28.5%,网络平均寿命提升了8.96%。     

软件定义承载网中基于负载均衡的虚拟网络资源分配算法

在具有集中式控制特点的carrier-SDN（软件定义承载网）中应用网络虚拟化技术为虚拟网络分配资源,实现承载网资源分配是解决承载网结构僵化的重要途径。提出carrier-SDN中基于负载均衡的虚拟网络资源分配算法。首先,建立carrier-SDN多层模型;其次,根据虚拟网络映射算法的特点,二值化粒子群优化算法;最后,以负载均衡为优化目标,求解虚拟网络映射问题。仿真结果表明,与已有方法相比,所提算法在虚拟网络负载均衡性、请求接受率和网络收益方面性能优越。     

软件定义网络中基于效率区间的负载均衡在线优化算法

在大型复杂软件定义网络中,为提高网络负载均衡,减少控制器与交换机间的传播时延,该文提出一种基于效率区间的负载均衡在线优化算法。在初始静态网络中,通过贪心算法选择初始控制器集合,并以其为根节点构建M棵改进代价的最小生成树(MST),确定初始M个负载均衡的子网;当网络流量发生变化时,通过广度优先搜索(BFS)调整子网间交换机映射关系使其满足效率区间,保证任意时刻网络的负载均衡。算法均以网络连通性为基础,且均以传播时延为目标重新更新控制器集合。仿真实验表明,该算法在保证任意时刻网络负载均衡的同时,可以保证较低的传播时延,与Pareto模拟退火算法、改进的K-Means算法等相比,可以使网络负载均衡情况平均提高40.65%。     

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.     

软件定义网络中基于时延和负载的多控制器部署策略研究

多控制器体系结构的出现,解决了经典软件定义网络(SDN)架构控制层以单一集中控制器为主,在大规模网络环境中的可扩展性问题.在多控制器体系结构中,由于生成转发规则并将其填充到交换机的任务被委托给了控制器,网络的性能在很大程度上取决于控制器的放置.该文以降低总时延和均衡控制器间负载为目标,提出了一种基于子网划分的多控制器部...     

Balanced multiple controllers placement with latency and capacity bound in software-defined network

Software-defined network (SDN) used a network architecture which separates the control plane and data plane. The control logic of SDN was implemented by the controller. Because controller's capacity was limited, in large scale SDN networks, single controller can not satisfy the requirement of all switches. Multiple controllers were needed to han-dle all data flows. By the reason that the latency between controller and switch would significantly affect the forwarding of new data flow, the rational placement of controllers would effectively improve the performance of entire network. By partition the network into multiple sub domains, on the base of spectral clustering, a method that added a balanced de-ployment object function into k-means was given and a balanced multiple controllers placement algorithm in SDN net-works which has the latency and capacity limitations was proposed. In this approach, a penalty function was introduced in the algorithm to avoid isolation nodes appearing. The simulations show that this algorithm can balance partition the net-work, keep the latency between controller and switch small and keep loads balancing between controllers.     

HeS‐CoP: Heuristic switch‐controller placement scheme for distributed SDN controllers in data center networks

Software‐defined networking (SDN) has been widely researched and used to manage large‐scale networks such as data center networks (DCNs). An early stage of SDN controller experienced low responsiveness, low scalability, and low reliability. To solve these problems, distributed SDN controllers have been proposed. The concept of distributed SDN controllers distributes control messages among multiple SDN controllers. However, distributed SDN controllers must assign a master controller for each networking devices. Most previous studies, however, did not consider the characteristics of DCNs. Thus, they are not suitable to operate in DCNs. In this paper, we propose HeS‐CoP, a heuristic switch‐controller placement scheme for distributed SDN controllers in DCNs. With the control traffic load and CPU load, HeS‐CoP decides when our scheme should be performed in DCNs. To show the feasibility of HeS‐CoP, we designed and implemented an orchestrator that contains our proposed scheme and then evaluated our proposed scheme. As a result, our proposed scheme well distributes the control traffic load, decreases the average CPU load, and reduces the packet delay.     

Pluggable SDN framework for managing heterogeneous SDN networks

Software‐defined networking (SDN) is a new network paradigm that is separating the data plane and the control plane of the network, making one or more centralized controllers to supervise the behaviour of the entire network. Different types of SDN controller software exist, and research dealing with the difficulties of consistently integrating these different controller types has mostly been declared future work. In this paper, the Domino framework is proposed, a pluggable SDN framework for managing heterogeneous SDN networks. In contrast to related work, the proposed framework allows research into SDN networks controlled by different types of SDN controllers attempting to standardize the northbound API of them. Domino implements a microservice plugin architecture where users can link different SDN networks to a processing algorithm. Such an algorithm allows for, eg, adapting the flows by building a pipeline using plugins that either invoke other SDN operations or generic data processing algorithms. The Domino framework is evaluated by implementing a proof‐of‐concept implementation, which is tested on the initial requirements. It achieves the modifiability and the interoperability with an average successful exchange ratio of 99.99%. The performance requirements are met for the frequently used commands with an average response time of 0.26 seconds, and the framework can handle at least 72 plugins simultaneously depending on the available amount of RAM. The proposed framework is evaluated by means of the implementation of a shortest path routing algorithm between heterogeneous SDN networks.     

Dynamic deployment algorithm for multi-controllers based on bidirectional matching in software defined networking

Aiming at the controller load imbalance problem in distributed SDN,a multi-controller dynamic deployment algorithm based on bidirectional matching was proposed.Through collecting hop counts,delay and flow information in the network periodically,match lists of switch and controller was built respectively.According to the principle of optimal queuing,switches and controllers were selected from two match lists for implementing bidirectional matching,and the relationship of matching with the help of simulated annealing algorithm was optimized,which achieved dynamic deployment for multi-controller in distributed network.Results show that,compared with the existing approaches,this algorithm can match the connections between switches and controllers reasonably,and reduce the queue delay of flow request effectively.Moreover,and the controller load balancing rate has increased by 17.9% at least.     

Research on switch migration method based on minimum cost path

In order to protect the controller,especially the controller in backbone network,from security threats and attacks,improve the security of the software-defined network (SDN) control plane,a switch migration algorithm based on minimum cost path was proposed.A load prediction module was added to the migration model,which executed a controller load prediction algorithm to obtain a load prediction matrix,and then a migration-target controller set was determined according to the load prediction matrix.The improved Dijkstra algorithm was used to determine the minimum cost path.According to the load state of the controller and the traffic priority of the switch to be migrated,the optimal migration switch set was determined.The problem of isolated nodes was solved that may occur during the migration process.The experimental results show that the migration timing of the algorithm is more reasonable,the selection of the migration controller and the target controller is more reasonable,the load balancing of the control plane is realized,the number of migrations and cost are reduced,and the performance of the controller is improved.     

基于SDN的多控制器部署策略的研究

随着SDN在大型网络以及广域网中的需求,如何合理、高效地部署SDN控制器,从而以较低的部署成本,获得较好的网络性能,是当前研究的热点.针对控制器数量、部署位置问题以及交换机与控制器之间的映射关系的问题,首先给出一种分布式的控制器部署方式并设定了网络相关参数,以流建立请求代价为优化目标,提出了一种基于贪心算法的控制器部署策略方案.最后通过仿真实验将所提出方案与ACL方案进行对比,证明了所提方案在性能方面有一定的提升.     

DSL: Dynamic and Self‐Learning Schedule Method of Multiple Controllers in SDN

For the reliability of controllers in a software defined network (SDN), a dynamic and self‐learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers’ combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi‐controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.