Using Heuristics to the Controller Placement Problem in Software-Defined Multihop Wireless Networking

Solving the controller placement problem (CPP) in an SDN architecture with multiple controllers has a significant impact on control overhead in the network, especially in multihop wireless networks (MWNs). The generated control overhead consists of controller-device and inter-controller communications to discover the network topology, exchange configurations, and set up and modify flow tables in the control plane. However, due to the high complexity of the proposed optimization model to the CPP, heuristic algorithms have been reported to find near-optimal solutions faster for large-scale wired networks. In this paper, the objective is to extend those existing heuristic algorithms to solve a proposed optimization model to the CPP in software-defined multihop wireless networking (SDMWN).Our results demonstrate that using ranking degrees assigned to the possible controller placements, including the average distance to other devices as a degree or the connectivity degree of each placement, the extended heuristic algorithms are able to achieve the optimal solution in small-scale networks in terms of the generated control overhead and the number of controllers selected in the network. As a result, using extended heuristic algorithms, the average number of hops among devices and their assigned controllers as well as among controllers will be reduced. Moreover, these algorithms are able tolower the control overhead in large-scale networks and select fewer controllers compared to an extended algorithm that solves the CPP in SDMWN based on a randomly selected controller placement approach.

The controller placement problem or the controller selection problem?

In SDN,the control logic of packet processing devices is moved onto the SDN centralized controller.This decoupled networking architecture creates some critical concerns when compared to traditional distributed architectures.One primary concern in this architecture is the placement of the controller,which is commonly known as the CPP.From our extensive literature review,we identify that there is no strict placement rule that best fits every network in SDN.Dynamic addition and deletion of controllers is inevitable.Motivated from this,in this paper,we attempt to transform the CPP into a CSP.We show that,in the dynamic SDN environment,researchers need to pay attention to CSP issues.The clear advantage of CSP over CPP is that,in the dynamic SDN ecosystem,we can answer fundamental questions about the control plane performance,i.e.,minimum number of controllers,their workload distribution,and placement/locations.Furthermore,we can meet strict,application-specific,QoS constraints.

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking(SDN)algorithms are gaining increas-ing interest and are making networks flexible and agile.The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components,enabling flexible and dynamic network management.A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers.The deployment of the controller—that is,the controller placement problem(CPP)—becomes a vital model challenge.Through the advancements of blockchain technology,data integrity between nodes can be enhanced with no requirement for a trusted third party.Using the lat-est developments in blockchain technology,this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem(STFOA-CPP)with blockchain-based intrusion detection in an SDN environ-ment.The major intention of the STFOA-CPP technique is the maximization of lifetime,network connectivity,and load balancing with the minimization of latency.In addition,the STFOA-CPP technique is based on the sea turtles’food-searching characteristics of tracking the odour path of dimethyl sulphide(DMS)released from food sources.Moreover,the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic.Finally,the blockchain can inspect the data integrity,determine significantly malicious input,and improve the robust nature of developing a trust relationship between sev-eral nodes in the SDN.To demonstrate the improved performance of the STFOA-CPP algorithm,a wide-ranging experimental analysis was carried out.The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.

基于分组遗传算法的数据中心虚拟机节能映射

近年来,随着人们对云计算业务需求持续增长,数据中心能耗日益增加,由此不仅增加了运营成本,巨大的碳排放对生态环境也产生严重的影响,数据中心节能已成为当前亟须解决的重要难题。对云数据中心的虚拟机放置(Virtual Machine Placement,VMP)进行优化能有效地提高资源利用率,同时,VMP也是减少数据中心能耗的重要技术之一;针对数据中心的能耗感知VMP问题,提出一种基于分组遗传算法的节能算法EEGGA(Energy-Efficient Grouping Genetic Algorithm),算法将节能VMP问题视为装箱问题(Bin Packing Problem,BPP),并应用基于分组编码的遗传算法对其进行求解,通过减少活动物理主机的数量(装箱数量)以实现降低数据中心能耗的目标;在算法迭代过程的交叉和变异等阶段,设计了多种启发优化策略提升子代染色体的适应度,从而提高算法的节能性能和加快迭代收敛的速度;通过仿真实验,在收敛速度和求解性能等方面将提出的算法与传统的节能遗传算法进行对比,实验结果表明:提出的算法能够有效地减少数据中心的能耗,在节能性能和求解收敛速度方面均优于其他算法。

车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策

车联网环境下,边缘服务器的放置位置与部署数量直接影响到边缘计算的效率。由于在宏基站或基站上部署大型边缘服务器的成本较高,可以在微基站上部署一个小型边缘服务器作为补充,并通过优化大型边缘服务器的放置位置来降低成本。为了最小化边缘服务器的部署代价和服务延迟、最大化运营商的收入和服务器负载均衡度,把边缘服务器放置问题与车联网用户应用服务放置问题联合建模为一个多目标优化问题,并提出了基于改进型NSGA-Ⅱ算法的放置方案。实验结果表明,提出的边缘服务器放置方案能够降低约44%的边缘服务器部署成本,降低约14.2%的时延,提升24.2%的运营商收入,具有较好的应用价值。

Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.

Adaptive Controller Placement in Software Defined Wireless Networks

Controller placement problem(CPP)is a critical issue in software defined wireless networks(SDWN).Due to the limited power of wireless devices,CPP is facing the challenge of energy efficiency in SDWN.Nevertheless,the related research on CPP in SDWN hasn’t modeled the energy consumption of controllers so far.To prolong the lifetime of SDWN and improve the practicability of research,we rebuilt a CPP model considering the minimal transmitted power of controllers.An adaptive controller placement algorithm(ACPA)is proposed with the following two stages.First,data field method is adopted to determine sub-networks for different network topologies.Second,for each sub-network we adopt an exhaustive method to find the optimal location which meets the minimal average transmitted power to place controller.Compared with the other algorithms,the effectiveness and efficiency of the proposed scheme are validated through simulation.

POLE PLACEMENT CONTROLLERS FOR LINEAR TIME-DELAY SYSTEMS WITH COMMENSURATE POINT DELAYS

This paper investigates the exact and approximate spectrum assignment properties associated with realizable output-feedback pole-placement type controllers for single-input single-output linear time-invariant time-delay systems with commensurate point delays. The controller synthesis problem is discussed through the solvability of a set of coupled diophantine equations of polynomials. An extra complexity is incorporated to the above design to cancel extra unsuitable dynamics being generated when solving the above diophantine equations. Thus, the complete controller tracks any arbitrary prefixed (either finite or delay-dependent) closed-loop spectrum. However, if the controller is simplified by deleting the above mentioned extra complexity, then the robust stability and approximated spectrum assignment are still achievable for a certain sufficiently small amount of delayed dynamics. Finally, the approximate spectrum assignment and robust stability problems are revisited under plant disturbances if the nominal controller is maintained. In the current approach, the finite spectrum assignment is only considered as a particular case to the designer's choice of a (delay-dependent) arbitrary spectrum assignment objective.

基于状态估计方法的相量测量单元电压稳定性研究

简要介绍电网的电压稳定性,并提出了基于状态估计的相量测量单元(PMU)测量电压稳定性的方法,该方法主要针对由于负载率变化引起的电压变化,并对不同负载情况和不同电压值情况下的电压稳定性进行了说明。提出通过采用加权最小二乘(WLS)方法对电力系统电压状态进行状态估计,同时通过使用PMU直接生成测量结果。为了在电力系统中达到PMU的最大利用效率,采用贪婪算法求解PMU的优化配置问题,利用电压稳定性指数和其他电压分析工具,推导出了PMU配置的最优位置。通过在电力系统中使用PMU,优化了电力系统中电压状态估计中繁琐的迭代过程,并且解决了电力系统中PMU的最优配置问题(OPP)。

网络诊断技术研究综述

网络诊断是一种高效且方便的通过端到端路径的性能测量来推断网络内部状态的方法,在有线和无线网络的链路测量和故障定位中得到了广泛应用。对网络诊断的4种基础模型布尔网络诊断技术、可加网络诊断技术、带宽网络诊断技术、随机网络诊断技术的模型基础进行了总结分析。系统梳理了网络诊断技术中的关键问题,主要包括监视器放置、信标服务放置、路径构建和数据分析、网络诊断中可识别性,以及不保证识别性时的网络诊断攻击等。同时梳理了基于网络编码的网络诊断、基于神经网络的网络诊断和网络功能虚拟化中节点故障定位等网络诊断领域的新研究和新问题。最后,基于网络诊断技术的发展现状,分析探讨了未来网络诊断技术的发展趋势。

高可靠性软件定义航空光信息网络的控制器分布优化策略

针对未来航空光信息网络中控制平面的可扩展性问题,基于软件定义网络思想,提出了软件定义航空光信息网络的网络架构,研究了软件定义航空光信息网络控制器部署策略。以网络节点及链路中断概率为参量,以航空网络的全网可靠性为优化目标,建立基于网络可靠性的整数规划模型;提出融合人工免疫策略、小生境思想和改进遗传算法的混合优化算法,获得控制器部署的最佳部署方案。仿真结果表明,基于可靠性的整数规划模型可实现对软件定义航空信息网络中的控制器部署问题准确建模,为实现控制器最优化部署提供了依据。

基于整数编码遗传算法的传感器优化配置研究

动态测试中,为了将传感器配置在合理的自由度上,以便充分反映结构的动力特性,需对传感器进行优化配置。分别以模态置信度矩阵、Fisher信息阵和它们的组合为优化准则,采用整数编码遗传算法,探讨了单目标和多目标优化的传感器优化配置问题。通过与现有的模态动能法、有效独立法及基于QR分解的逐步累积法进行比较,传感器优化配置的结果表明整数编码遗传算法优于上述三种方法。

基于遗传算法的点状要素注记的整体最优配置

提出了一种点状要素自动注记的整体最优解的解决方案 ,其核心算法采用具有全局搜索特性的遗传算法 ,可以获取点状要素注记配置的 (近似 )

一种基于蜂窝网格的传感器节点部署算法

传感器节点的部署是无线传感器网络研究的一个重要分支。证明了传感器节点覆盖圆盘与其邻居节点覆盖圆盘的交点构成正六边形蜂窝时,有效覆盖面积最大;提出了一种基于蜂窝网格的传感器节点部署算法,通过仿真与已有的基于方形网格的部署算法进行比较,结果证明:本算法能更有效地控制成本。

基于遗传算法的贴片机贴装顺序优化

元件贴装顺序是决定贴片机生产效率的关键问题。针对拱架型贴片机,采用了一种遗传算法。该遗传算法有其独特的染色体编码解码方式和交叉算子。算法中的染色体根据被贴装的印刷电路板由一条或多条子链组成,染色体的一个基因代表一个取贴循环。实验结果表明,该算法可以有效解决元件贴装顺序问题。同时,分析比较了三种传统交叉算子和该交叉算子的优化结果,表明这些传统交叉算子不能有效解决该问题。

求解带有预放置矩形块的布局问题的启发式算法

在超大规模集成电路设计中,一些特别重要的部件,如RAM、ROM、CPU等经常被优先放置,而其他元件则被两两互不重叠地放置在芯片的剩余区域。这类问题能被形式化为带有预放置矩形块的布局问题。基于占角和最大穴度优先的放置策略,为该问题的快速求解提供了一种高效的启发式算法。算法的高效性通过应用于标准电路MCNC得到了验证。

Overlay网络上的服务部署问题的研究(二)

给出了一对多单因子服务部署问题的数学模型和求解该问题的遗传算法,并通过数值模拟表明了算法的有效性。

Overlay网络上的服务部署问题的研究(一)

论文形式化描述了Overlay网络上的服务部署问题,给出了一对一单因子服务部署问题的数学模型和求解该问题的启发式算法,并通过数值模拟表明了算法的有效性。

SDN控制器部署中的可靠性优化研究

软件定义网络(SDN)将传统网络结构中的控制层和转发层解耦,其将所有转发设备与一个逻辑集中的控制器相连接。为避免网络规模不断扩大引起的单点失效,向分布式控制结构发展成为Open Flow广域网部署的趋势,其中控制层多控制器的部署问题是SDN设计中的一个关键环节。提出基于控制路径连通度的控制器部署方案来最大化SDN控制器部署的可靠性,并使用3种不同的算法对比控制器部署性能。仿真结果表明,该方案可以在可接受时延范围内提升部署SDN控制器的可靠性。

动态吸引子在布局求解中的应用

在研究分析现有布局启发算法的基础上,提出了动态吸引子的概念,并据此建立了动态的定位函数和布局求解算法.分析了定位函数中各参数和坐标点的含义.通过调整定位函数中参数的取值,可得到满足不同条件和要求的优化布局方案.最后通过实例验证了该算法的合理性.