CN113572686B - Heaven and earth integrated self-adaptive dynamic QoS routing method based on SDN - Google Patents
Heaven and earth integrated self-adaptive dynamic QoS routing method based on SDNDownload PDFInfo
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及天地一体化网络，特别是一种基于分布式SDN的天地一体化自适应动态路由方法(简称SDN-AD，Adaptive Dynamic Routing Strategy for Distributed SDN)。The present invention relates to a space-ground integration network, in particular to a distributed SDN-based space-ground integration adaptive dynamic routing method (SDN-AD for short, Adaptive Dynamic Routing Strategy for Distributed SDN).
背景技术Background technique
天地一体化网络(SGIN，The Space-Ground Integrated Network)将卫星与地面网络融合，具有覆盖面广、按需接入、通信不受地理条件制约等特点，能够支撑多样化的通信需求，是未来网络发展的新趋势。随着天地一体化网络的快速发展，网络内各类通信业务需求也发生了巨大改变，在带宽、时延、丢包率等方面都提出了明确的质量保障要求。为满足业务要求，需提供覆盖确定、连接确定、服务质量自适应的质量可保障的网络。The Space-Ground Integrated Network (SGIN, The Space-Ground Integrated Network) integrates satellite and terrestrial networks. It has the characteristics of wide coverage, on-demand access, and communication is not restricted by geographical conditions. It can support diverse communication needs and is the future network. new trends in development. With the rapid development of the integrated space-earth network, the demand for various communication services in the network has also undergone tremendous changes, and clear quality assurance requirements have been put forward in terms of bandwidth, delay, and packet loss rate. In order to meet the business requirements, it is necessary to provide a quality-guaranteed network with coverage determination, connection determination, and service quality self-adaptation.
卫星网络作为天地一体化网络中的关键部分，其组网灵活的特点使其可以有效的整合空间信息资源，承载大量确定性业务。但由于卫星网络高速运动的特性，导致其存在网络时空尺度大和空间资源受限的问题。为对网络资源进行合理规划，满足业务质量保障需求，根据数控分离思想，研究者将软件定义网络(SDN，Software-Defined Network)引入天地一体化网络。实现了在逻辑上集中的松耦合控制平面和物理上分布的数据平面，其集中管理模式为考虑多种链路状态的路由算法的实现提供了可能。但由于其产生的大量控制信令占用了本就紧缺的星间网络资源，难以适应天地一体化智能网络环境。As a key part of the integrated space-earth network, the satellite network is flexible in networking, which enables it to effectively integrate spatial information resources and carry a large number of deterministic services. However, due to the characteristics of high-speed motion of satellite networks, there are problems of large network space-time scale and limited space resources. In order to reasonably plan network resources and meet the needs of service quality assurance, according to the idea of NC separation, researchers introduce Software-Defined Network (SDN, Software-Defined Network) into the world-earth integrated network. The logically centralized loosely coupled control plane and the physically distributed data plane are realized, and its centralized management mode provides the possibility for the realization of routing algorithms considering various link states. However, due to the large amount of control signaling generated by it occupying the already scarce inter-satellite network resources, it is difficult to adapt to the integrated intelligent network environment of heaven and earth.
因此，需针对SDN架构的特点，设计更加合理的配置模型，以更好的实现服务质量(QoS，Quality of Services)路由。现有的SDN架构大多仅将控制器部署在地球同步轨道卫星(GEO，Geostationary Earth Orbit)或地面，无论将控制器放置在GEO卫星还是地面，与被控节点的距离都较远，控制器发出的信令要进行长距离传输，会产生较高的开销代价。为解决SDN架构应用于天地一体化网络控制信令开销大的问题。综合天地卫星通信SDN架构(SERvICE，Software Defined Framework for Integrated Space-TerrestrialSatellite Communication)将控制器分别部署在GEO卫星、地面卫星网关和地面数据中心，有效提升了网络服务的敏捷性，分散了计算负担，但未能有效降低控制信令开销。Therefore, it is necessary to design a more reasonable configuration model according to the characteristics of the SDN architecture to better realize Quality of Services (QoS) routing. Most of the existing SDN architectures only deploy the controller on Geostationary Earth Orbit (GEO, Geostationary Earth Orbit) or on the ground. No matter if the controller is placed on the GEO satellite or on the ground, the distance from the controlled node is long. The signaling needs to be transmitted over a long distance, which will result in higher overhead costs. In order to solve the problem that the SDN architecture is applied to the sky-earth integrated network control signaling overhead. The Integrated Space-Terrestrial Satellite Communication SDN architecture (SERvICE, Software Defined Framework for Integrated Space-Terrestrial Satellite Communication) deploys controllers in GEO satellites, terrestrial satellite gateways and terrestrial data centers, effectively improving the agility of network services and dispersing computing burdens. However, it fails to effectively reduce the control signaling overhead.
为满足天地一体化网络大时空尺度条件下的服务质量保障问题，学者们进行了大量考虑链路状态特征的路由策略研究，软件定义路由算法(SDRA，ASoftware DefinedRouting Algorithm)对分布式路由算法(DRA，distributed routing algorithm)进行改进，通过星间链路时延权重实现了拥塞控制，有效保障了低轨卫星间的传输质量。服务质量支持的路由策略，实现了多条流量最大化路径和可接受的传输延迟。但它们都只考虑了单一的服务质量要求，影响了网络的整体利用率。In order to meet the service quality assurance problem under the condition of large space-time scale of the integrated space and ground network, scholars have carried out a lot of research on routing strategies considering the characteristics of link states. , distributed routing algorithm) to improve the congestion control through the delay weight of the inter-satellite link, which effectively guarantees the transmission quality between low-orbit satellites. QoS-enabled routing strategy for multiple traffic maximizing paths and acceptable transit delays. But they all only consider a single QoS requirement, which affects the overall utilization of the network.
因此，提出了多目标决策的近地轨道卫星(LEO，Low Earth Orbit)，网络路由算法，可以计算满足QoS需求的路径。一种基于队列调度的虚拟拓扑延迟容忍网络(DTN，Delay-Tolerant Network)路由算法，可以根据不同数据类型进行队列调度，实现了弹性负载均衡路由。基于有向无环图的多约束路由优化算法，引入自适应链路综合代价函数，但都没有对约束条件的阈值给出计算。Therefore, a multi-objective decision-making Low Earth Orbit (LEO, Low Earth Orbit), network routing algorithm is proposed, which can calculate the path that meets the QoS requirements. A virtual topology Delay-Tolerant Network (DTN, Delay-Tolerant Network) routing algorithm based on queue scheduling, which can schedule queues according to different data types, and realizes flexible load balancing routing. In the multi-constraint routing optimization algorithm based on directed acyclic graph, an adaptive link comprehensive cost function is introduced, but the thresholds of the constraints are not calculated.
软件定义卫星网络架构和智能服务质量路由方案，智能地提供细粒度的服务质量保证。能量感知的路由方案，能够根据卫星的不同能级进行路由，考虑不同服务的特点，实现了负载均衡。基于SDN的卫星网络架构下的采用拉格朗日松弛法的多种QoS需求路由算法(MQOO,lagrangian relaxation method QoS routing)，近似算法能够得到相对精确的计算结果，但无法保证计算时间。适用于卫星网络状态和信誉自适应服务质量的路由算法(SRADR,Status and Reputation Adaptive QoS Dynamic Routing)，利用蚁群算法进行路由发现和维护，实现了服务质量的自适应，但算法收敛速度较慢。Software-defined satellite network architecture and intelligent QoS routing solutions intelligently provide fine-grained QoS assurance. The energy-aware routing scheme can perform routing according to different energy levels of satellites, and realize load balancing by considering the characteristics of different services. Lagrangian relaxation method QoS routing (MQOO) under the SDN-based satellite network architecture, the approximate algorithm can obtain relatively accurate calculation results, but cannot guarantee the calculation time. The routing algorithm (SRADR, Status and Reputation Adaptive QoS Dynamic Routing) is suitable for satellite network status and reputation adaptive service quality. It uses ant colony algorithm for route discovery and maintenance, and realizes the self-adaptation of service quality, but the algorithm convergence speed is slow. .
发明内容SUMMARY OF THE INVENTION
为了解决现有技术存在的上述问题，本发明要设计一种能够减少SDN架构控制信令开销、对网络资源进行合理分配、自适应保障多种业务的不同服务需求的基于分布式SDN的天地一体化自适应动态路由方法。In order to solve the above problems existing in the prior art, the present invention aims to design a distributed SDN-based world-earth integration capable of reducing the control signaling overhead of the SDN architecture, reasonably allocating network resources, and adaptively guaranteeing the different service requirements of various services. Adaptive dynamic routing method.
为了实现上述目的，本发明的技术方案如下：一种基于SDN的天地一体化自适应动态路由方法，包括以下步骤：In order to achieve the above object, the technical solution of the present invention is as follows: an SDN-based adaptive dynamic routing method for the integration of heaven and earth, comprising the following steps:
A、建立基于SDN的分层分簇网络模型A. Establish a hierarchical clustering network model based on SDN
根据GEO卫星覆盖面积大且对地相对静止的特点，设GEO卫星为主控制器。根据LEO卫星运动速度快且通信时延小的特点，设LEO卫星为从控制器。According to the characteristics of GEO satellites covering a large area and being relatively stationary to the ground, the GEO satellites are set as the main controller. According to the characteristics of the LEO satellite's fast movement speed and small communication delay, the LEO satellite is set as the slave controller.
A1、确定网络模型A1. Determine the network model
设基于SDN的天地一体化网络由LEO卫星节点、GEO卫星节点和地面节点组成。将基于SDN的天地一体化网络的拓扑图表示为无向加权拓扑图G＝(V,E)，并存储在GEO卫星主控制器中，其中，V＝{vG,vL,vT}代表网络中所有节点的有限集合，vG,vL,vT分别表示GEO卫星节点、LEO卫星节点和地面节点的节点集。E＝{IOL∪ISL∪UDL}代表网络中所有链路的集合，IOL表示星际链路，ISL表示星间链路，UDL表示星地链路。Suppose the SDN-based space-ground integrated network consists of LEO satellite nodes, GEO satellite nodes and ground nodes. The topological graph of the SDN-based integrated space-earth network is represented as an undirected weighted topological graph G=(V, E), and stored in the GEO satellite master controller, where V={vG , vL , vT } represents a finite set of all nodes in the network, vG , vL , and vT represent the node sets of GEO satellite nodes, LEO satellite nodes and ground nodes, respectively. E={IOL∪ISL∪UDL} represents the set of all links in the network, IOL represents the interstellar link, ISL represents the inter-satellite link, and UDL represents the satellite-ground link.
第n颗GEO卫星用Gn表示，1≤n≤NG，为了使GEO卫星覆盖全球区域，令NG≥3。根据GEO卫星的覆盖范围，将整个卫星网络划分为NG个区域，即
根据虚拟节点思想，按照经纬度将地球表面分成不同的区域，为每个区域设置一个逻辑地址。每个区域内的逻辑地址保持不变，但其中的LEO卫星成员会发生变化。当一颗LEO卫星成员离开一个逻辑区域时，下一颗LEO卫星就会进入该逻辑区域以替换其位置。根据以下条件确定GEO卫星的覆盖分簇。According to the idea of virtual nodes, the earth's surface is divided into different areas according to latitude and longitude, and a logical address is set for each area. The logical address within each region remains the same, but the membership of the LEO satellites within it changes. When a LEO satellite member leaves a logical area, the next LEO satellite enters the logical area to replace its position. The coverage clustering of GEO satellites is determined according to the following conditions.
式中，
将满足公式(3)的LEO卫星根据|G′nLh|最小原则，唯一的划到GEO卫星的簇内。由于LEO卫星覆盖范围与地面区域一一对应，GEO卫星覆盖区域内的LEO卫星成员和地面站成员得以确定。只需建立控制器集群配置策略，得到卫星动态连接关系，即可确定基于SDN的分层分簇网络模型下的整体网络拓扑。The LEO satellites that satisfy the formula (3) are uniquely assigned to the cluster of GEO satellites according to the principle of |G′n Lh | minimum. Since the LEO satellite coverage area corresponds to the ground area one-to-one, the LEO satellite members and ground station members within the GEO satellite coverage area can be determined. Only by establishing the controller cluster configuration strategy and obtaining the satellite dynamic connection relationship, the overall network topology under the SDN-based hierarchical clustering network model can be determined.
A2、配置控制器集群A2. Configure the controller cluster
设X、Y、Z分别为单颗LEO卫星的地固坐标系，地固坐标系的原点为地心，XOY平面和地球赤道平面重合，OX轴与格林威治子午线方向一致，OZ轴与地球的极轴重合。不考虑其他天体和人造卫星对LEO卫星的影响，由公式(4)至(9)得到LEO卫星Lj与LEO卫星Lk之间的实际距离
ΩG＝ωΔt (5)ΩG = ωΔt (5)
X＝H[cosμcos(Ω-ΩG)-sinμsin(Ω-ΩG)cosσ] (6)X=H[cosμcos(Ω-ΩG )-sinμsin(Ω-ΩG )cosσ] (6)
Y＝H[cosμsin(Ω-ΩG)-sinμcos(Ω-ΩG)cosσ] (7)Y=H[cosμsin(Ω-ΩG )-sinμcos(Ω-ΩG )cosσ] (7)
Z＝H(sinμsinσ) (8)Z=H(sinμsinσ) (8)
式中，μo为卫星近地点幅角，
针对每个划分的区域Vi，设计LEO卫星从控制器配置分簇策略如下：以LEO卫星从控制器个数a作为Vi内的聚类数，从控制器集合表示为LC＝{c1,c2,...,ca}，ci所管辖的簇表示为
LEO卫星从控制器接收本地子网状态信息，计算簇内路由表，最后由GEO卫星主控制器负责收集所有边缘节点聚合资源信息，实现簇间的路由计算。The LEO satellite receives the local subnet status information from the controller, calculates the routing table in the cluster, and finally the GEO satellite master controller is responsible for collecting the aggregated resource information of all edge nodes to realize the routing calculation between the clusters.
B、建立网络资源映射B. Establish network resource mapping
B1、建立传输成本模型B1. Establish a transmission cost model
结合GEO/LEO双层卫星网络通信范围广但传输时延长且丢包率高的特点，通过分布式SDN模型，选取实时获取的链路延迟、丢包率、链路剩余带宽和节点负载作为度量参数，建立传输成本模型，步骤如下：Combining the characteristics of GEO/LEO double-layer satellite network with wide communication range but prolonged transmission and high packet loss rate, the distributed SDN model is used to select the link delay, packet loss rate, remaining link bandwidth and node load obtained in real time as metrics. parameters to establish a transmission cost model, the steps are as follows:
计算链路延迟：延迟指数据业务从源节点到目的节点所需要的时间，是衡量QOS的主要性能指标之一。任意两个节点m,n间链路lm,n的延迟用以下公式表示：Calculate link delay: Delay refers to the time required for data services to travel from the source node to the destination node, and is one of the main performance indicators for measuring QOS. The delay of the link lm,n between any two nodes m,n is expressed by the following formula:
式中，
计算剩余带宽：剩余带宽指通信中链路未被占用的数据传输量。在传输业务数据时，链路两端的两个节点相连的两个端口，一个端口的字节发送率与另一个端口的字节接收率相等，因此链路lm,n的剩余带宽只需用节点m的端口数据表示：Calculate the remaining bandwidth: The remaining bandwidth refers to the amount of data transmission that is not occupied by the link in the communication. When transmitting service data, the two ports connected to the two nodes at both ends of the link, the byte sending rate of one port is equal to the byte receiving rate of the other port, so the remaining bandwidth of the link lm,n only needs to use The port data of node m represents:
式中，currspeed表示节点指定端口的带宽，inbytes(m,p)表示节点m的p端口的字节接收率，outbytes(m,p)表示节点m的p端口的字节发送率。In the formula, currspeed represents the bandwidth of the specified port of the node, inbytes(m, p) represents the byte reception rate of the p port of the node m, and outbytes(m, p) represents the byte transmission rate of the p port of the node m.
计算丢包率：丢包率
式中，rxpacket(i,j)表示节点i发送的数据包总数，txpacket(i,j)表示节点j收到的数据包总数。In the formula, rxpacket(i, j) represents the total number of data packets sent by node i, and txpacket(i, j) represents the total number of data packets received by node j.
计算传输成本：
式中，
因此，根据簇内邻接矩阵R*，建立簇内节点的时延矩阵
B2、聚合簇资源B2. Aggregate cluster resources
基于步骤A建立的基于SDN的分层分簇网络模型，为主控制器建立聚合簇资源集合视图。设边缘节点集合为u，邻接矩阵为U，为了在不同簇之间有效的传输数据，通过簇资源聚合算法，计算出任意两个边缘节点i、j的累积时延
簇资源聚合算法的具体步骤如下：The specific steps of the cluster resource aggregation algorithm are as follows:
Step1:初始化任何一对边缘节点的时延、丢包率和带宽。Step1: Initialize the delay, packet loss rate and bandwidth of any pair of edge nodes.
Step2:计算出任意两个边缘节点的累积时延。Step2: Calculate the cumulative delay of any two edge nodes.
Step3:计算出任意两个边缘节点的累积丢包率。Step3: Calculate the cumulative packet loss rate of any two edge nodes.
Step4:计算出任意两个边缘节点之间的最小带宽，使用MIN-MAX原理构造有效带宽。Step4: Calculate the minimum bandwidth between any two edge nodes, and use the MIN-MAX principle to construct the effective bandwidth.
Step5:计算出任意两个边缘节点的累积时延、实际丢包率、有效带宽。Step5: Calculate the cumulative delay, actual packet loss rate, and effective bandwidth of any two edge nodes.
Step6:构造簇间的时延矩阵
C、建立多约束QOS自适应动态路由算法SDN-ADC. Establish a multi-constraint QOS adaptive dynamic routing algorithm SDN-AD
为实现基于多约束QoS的自适应动态路由算法，将多约束QoS问题公式化为目标为最低传输成本的优化问题。为了提供个性化服务并更好地适应网络变化，使用Adam(Adaptive moment estimation)优化算法实现约束阈值的自适应。为了更好地适应SIGN的高动态特性并提高算法收敛速度，使用基于逻辑回归和禁忌搜索的改进蜂群算法，以解决最优解问题。自适应路由是通过找到一个满足约束条件并在簇内和簇之间具有最小传输成本的路径来实现的，具体步骤如下：To implement an adaptive dynamic routing algorithm based on multi-constraint QoS, the multi-constraint QoS problem is formulated as an optimization problem aiming at the lowest transmission cost. In order to provide personalized services and better adapt to network changes, the Adam (Adaptive moment estimation) optimization algorithm is used to realize the adaptation of the constraint threshold. In order to better adapt to the high dynamic characteristics of SIGN and improve the convergence speed of the algorithm, an improved bee colony algorithm based on logistic regression and tabu search is used to solve the optimal solution problem. Adaptive routing is achieved by finding a path that satisfies the constraints and has the smallest transmission cost within and between clusters. The specific steps are as follows:
C1、问题定义C1. Problem Definition
为了在满足时延、剩余带宽、丢包率阈值约束下找到一条从源节点s到目的节点d具有最小传输成本的路径
Subjectto：Subjectto:
Q＝{b1,b2,...,bi} (21)Q={b1 ,b2 ,...,bi } (21)
公式(15)定义了目标函数为选择总传输成本最小的路径。
多约束服务质量路由问题，考虑加性度量参数即时延、凹性约束即带宽、乘性约束即丢包率三种QoS约束参数，公式(16)-(18)根据QoS路由度量性能指标，制定了从源节点s到目的节点d的路径的约束条件，公式(19)中的xk表示源节点和目的节点之间是否存在可达路径。公式(20)要求涵盖集合
C2、多优先级阈值Adma求解C2, multi-priority threshold Adma solution
为了在天地一体化网络中更好的利用网络资源，支持不同业务的不同需求，提供个性化的服务，在路由计算中应考虑不同业务的优先级。依据SDN的可编程特性将网络协议拆包，识别不同的数据业务，根据业务对QoS的不同需求，赋予以下不同的优先级：In order to make better use of network resources, support different needs of different services, and provide personalized services in the integrated network of space and earth, the priority of different services should be considered in routing calculation. According to the programmable characteristics of SDN, the network protocol is unpacked, different data services are identified, and the following different priorities are given according to the different requirements of the service for QoS:
优先级1:实时性、抖动敏感、高交互性的业务。Priority 1: Real-time, jitter-sensitive, and highly interactive services.
优先级2:事务办理数据、交互性业务。Priority 2: Transaction handling data, interactive business.
优先级3:仅要求低丢包率的业务、视频流。Priority 3: Only services and video streams that require a low packet loss rate are required.
根据服务QoS要求的特征，为三种不同优先级服务的延迟、带宽和丢包率阈值设置以下不同的目标计算函数：According to the characteristics of service QoS requirements, the following different target calculation functions are set for the delay, bandwidth and packet loss rate thresholds of three different priority services:
式中，Pri表示业务的优先级类型，由此三类业务根据需求选择不同的路由线路，优先级越高，获得更快的处理。1类优先级的业务，要求时延更低，允许丢包率相对较高。3类优先级的业务，要求丢包率更低，允许时延相对较高。In the formula, Pri represents the priority type of the service, so that the three types of services select different routing lines according to their needs. The higher the priority, the faster the processing.
为求解不同业务优先级下的自适应阈值，通过Adam算法，设f(θ)为目标函数，θ∈{bm,n,dm,n,plm,n}为要求解的参数，设gt表示每次下降梯度，即在时间步长t上计算ft(θ)对于θ的偏导：In order to solve the adaptive threshold under different business priorities, through the Adam algorithm, let f(θ) be the objective function, θ∈{bm,n, dm,n ,plm,n } be the parameters to be solved, let gt represents the gradient of each descent, that is, the partial derivative of ft (θ) with respect to θ is calculated at time step t:
t＝t+1。 (25)t=
mt＝β1·mt-1+(1-β1)·gt (27)mt =β1 ·mt-1 +(1-β1 )·gt (27)
式中，mt和vt分别为gt的一阶矩和二阶矩，相当于梯度gt和
因此，通过上述算法得出阈值，作为路由算法的约束条件，通过路由算法选择满足不同阈值约束的最佳路径。Therefore, the threshold is obtained through the above algorithm, which is used as the constraint condition of the routing algorithm, and the optimal path that satisfies the constraints of different thresholds is selected by the routing algorithm.
C3、基于改进人工蜂群算法即IABC的路由算法SDN-ADC3. Routing Algorithm SDN-AD Based on Improved Artificial Bee Colony Algorithm (IABC)
为了解决上述多约束目标优化问题，对传统人工蜂群算法即ABC算法进行改进。为避免蜜源开采的盲目性，在新蜜源的产生上采用logistic混沌映射，生成更加接近最优解的解集。为实现对路径的约束，根据禁忌搜索思路，引入禁忌搜索表。将每次寻优的节点记入禁忌表1，直到找到目的节点，得到一条从原节点到目的节点的路径。将不满足约束的路径记入禁忌表2，下次不再访问。In order to solve the above-mentioned multi-constraint objective optimization problem, the traditional artificial bee colony algorithm, ie ABC algorithm, is improved. In order to avoid the blindness of nectar mining, logistic chaotic mapping is used in the generation of new nectar to generate a solution set that is closer to the optimal solution. In order to realize the constraint on the path, according to the idea of tabu search, a tabu search table is introduced. Record each optimized node into taboo table 1 until the destination node is found, and a path from the original node to the destination node is obtained. The paths that do not meet the constraints are recorded in the taboo table 2, and will not be visited next time.
为实现基于IABC算法的多约束QoS路由算法，以前述多约束优化模型为IABC算法模型，把时延、带宽、丢包率三个性能指标作为参数吸收到同一个目标函数，传输成本为优化目标。最终选择源节点s到目的节点d之间，满足约束条件且传输成本最小的路径。SDN-AD算法按以下阶段进行计算：In order to realize the multi-constraint QoS routing algorithm based on the IABC algorithm, the aforementioned multi-constraint optimization model is used as the IABC algorithm model, and the three performance indicators of delay, bandwidth and packet loss rate are absorbed into the same objective function as parameters, and the transmission cost is the optimization goal. . Finally, select the path between the source node s and the destination node d that satisfies the constraints and has the smallest transmission cost. The SDN-AD algorithm is calculated in the following stages:
C31、初始阶段C31. Initial stage
设定雇佣蜂、跟随蜂和侦查蜂数量，雇佣蜂最大迭代次数limit，算法最大循环迭代次数MCN。使用簇内邻接矩阵R*来完成蜜源的初始化。确定搜索空间范围，即源节点到所有的可连通下一跳节点。在搜索空间中随机生成初始解xi,i＝1,2,...,SN，即初始化链路li，SN为蜜源个数。每个解xi就相当于一个蜜源，是一个D维的向量，D是问题的维数。Set the number of employed bees, follower bees and scout bees, the maximum number of iterations of employed bees, and the maximum number of iterations of the algorithm MCN. The initialization of the nectar is done using the intra-cluster adjacency matrix R* . Determine the search space range, that is, from the source node to all connectable next-hop nodes. Randomly generate initial solutions xi ,i =1, 2, . Each solution xi is equivalent to a nectar, a D-dimensional vector, where D is the dimension of the problem.
C32、雇佣蜂阶段C32, hiring bee stage
每个雇佣蜂由下式产生一个新解即新蜜源:Each hired bee generates a new solution, the new nectar source, by the following formula:
式中，
其中μ是控制参数，n＝1,2,......，根据映射理论初始混沌变量为：where μ is the control parameter, n=1,2,..., according to the mapping theory, the initial chaotic variables are:
将最小路链路价作为适应度，当生成一个新蜜源时，则根据适应度评估新蜜源的质量，采用贪婪选择法进行选择，若优于旧蜜源则雇佣蜂选择新蜜源，否则将保留旧蜜源，对新蜜源适应度进行记录，交给跟随蜂。适应度公式如下所示，fi表示解的函数值：The minimum link price is taken as the fitness. When a new nectar source is generated, the quality of the new nectar source is evaluated according to the fitness, and the greedy selection method is used for selection. If it is better than the old nectar source, bees are hired to select the new nectar source, otherwise the old nectar source will be kept. Nectar source, record the fitness of the new nectar source and give it to the follower bees. The fitness formula is as follows, where fi represents the function value of the solution:
通过上述策略使搜索在进化前期扩大搜索有能力跳出局部最优解并在进化末期有效的提高搜索精度，加速收敛。Through the above strategy, the search can be expanded in the early stage of evolution and can jump out of the local optimal solution, and at the end of evolution, the search accuracy can be effectively improved, and the convergence can be accelerated.
C33、跟随蜂阶段C33, follow the bee stage
跟随蜂获得由雇佣蜂传递的蜜源信息后，按照可行解的适应度值进行选择概率计算。概率计算公式如下：After the follower bee obtains the nectar source information transmitted by the employed bee, the selection probability is calculated according to the fitness value of the feasible solution. The probability calculation formula is as follows:
适应度越高，即代价越小的链路越容易被选到，并对其深度搜索。再由式(33)得到蜜源附近的新蜜源，通过新旧蜜源优劣对比，保留质量更优的蜜源。The higher the fitness, the easier the link with the lower cost is to be selected and searched deeply for it. Then, the new nectar source near the nectar source can be obtained by formula (33).
为实现步骤C1所述问题模型，存储搜寻路径并对搜寻到的路径进行约束条件判断，下面在借鉴禁忌搜索的基础上，加入两个禁忌表。In order to realize the problem model described in step C1, store the search path and judge the constraint conditions of the searched path, two taboo tables are added below based on the tabu search.
每只蜜蜂都有自己的内存，内存中用禁忌表1即Tabu1存储该蜜蜂已经访问过的最优解节点，在其后的搜索中将不能再访问这些节点。当目的节点被包含进Tabu1中时，蜜蜂就得到一条从源节点到目的节点的可行路径
C34、侦察蜂阶段C34, scout bee stage
若在limit最大迭代次数阈值内搜索后蜜源没有更新，则该蜜源宣告枯竭，雇佣蜂转为侦察蜂，在解空间内重新寻找新的蜜源。If the nectar source is not updated after the search within the maximum iterations threshold of limit, the nectar source is declared exhausted, and the hired bee turns into a scout bee to search for a new nectar source in the solution space.
与现有技术相比，本发明具有以下有益效果：Compared with the prior art, the present invention has the following beneficial effects:
1、为了适应SIGN中网络资源有限和业务需求多样的问题。本发明首先提出了一种基于分布式SDN的分层分簇网络模型，可以通过减少控制包的长距离传输和最短距离分簇有效地减少控制开销，提高传输效率。1. In order to adapt to the problems of limited network resources and diverse business needs in SIGN. The invention first proposes a layered clustering network model based on distributed SDN, which can effectively reduce control overhead and improve transmission efficiency by reducing long-distance transmission and shortest-distance clustering of control packets.
首先，为了在高动态的卫星网络环境下，部署一个相对稳定的控制方案，减少控制部署的频繁切换。本发明根据GEO卫星覆盖面积大且对地相对静止的特点，设其为主控制器。根据LEO卫星运动速度快且通信时延小的特点，设其为从控制器。First, in order to deploy a relatively stable control scheme in a highly dynamic satellite network environment, the frequent switching of control deployment is reduced. According to the characteristics of the GEO satellite covering a large area and being relatively stationary to the ground, the present invention sets it as the main controller. According to the characteristics of fast movement speed and small communication delay of LEO satellite, it is set as the slave controller.
其次，本发明进行主从控制器部署设置。一方面，作为主控制器的GEO卫星仅与LEO卫星从控制器执行控制传输，减少了控制数据包的长距离星际链路的传输。通过本发明提出的分层分簇的网络模型，设一条路径上
2、为了在确保服务质量的同时最大化网络吞吐量，将多约束QoS问题公式化为以最小传输成本为目标的优化问题。与传统的考虑单个因素的传输成本相比，提出了一种考虑了时延、带宽、丢包率和节点负载的传输成本函数。基于星间链路、星际链路和星地链路的传输延迟和传输质量差异很大的特性，本发明可以有效的计算不同类别的链路的传输成本，屏蔽不同层级的卫星和地面网络之间的差异。2. In order to maximize the network throughput while ensuring the quality of service, the multi-constrained QoS problem is formulated as an optimization problem with the goal of minimizing the transmission cost. Compared with the traditional transmission cost considering a single factor, a transmission cost function considering delay, bandwidth, packet loss rate and node load is proposed. Based on the characteristics that the transmission delay and transmission quality of the inter-satellite link, the interplanetary link and the satellite-to-ground link are very different, the present invention can effectively calculate the transmission cost of different types of links, and shield the difference between satellites and terrestrial networks at different levels. difference between.
同时，为了适应天地一体化网络高动态的特点，并在不消耗大量计算资源的前提下，根据不同服务的优先级自适应地更新阈值，提出了基于Adam的自适应阈值作为约束条件。Adam算法是随机梯度下降的一阶优化算法，可以有效地求解目标函数的最大值或最小值。该算法结合了AdaGrad(自适应梯度算法)算法和RMSProp(均方根传播)算法的优点。Adam算法首先在计算梯度的一阶矩时将当前更新梯度和最后更新梯度之间的差值保持在很小的范围内，实现平滑的梯度过渡，从而适应不稳定的目标函数。然后，设置梯度的二阶矩阵，通过计算当前梯度平方和过去梯度平方的平均值来更新学习率。因此，可以根据不同的优先级自适应不同的阈值，并且算法简单高效，不会占用大量的计算资源和存储资源。传统的手动设置方法相比，本发明可以更好地适应网络变化，从而提供满足不同服务质量需求的服务。At the same time, in order to adapt to the high dynamic characteristics of the integrated space-earth network and to update the threshold adaptively according to the priority of different services without consuming a lot of computing resources, an adaptive threshold based on Adam is proposed as a constraint. Adam algorithm is a first-order optimization algorithm of stochastic gradient descent, which can effectively solve the maximum or minimum value of the objective function. The algorithm combines the advantages of the AdaGrad (Adaptive Gradient Algorithm) algorithm and the RMSProp (Root Mean Square Propagation) algorithm. The Adam algorithm first keeps the difference between the current update gradient and the last update gradient within a small range when calculating the first-order moment of the gradient, so as to achieve a smooth gradient transition and adapt to the unstable objective function. Then, a second-order matrix of gradients is set, and the learning rate is updated by computing the average of the current gradient squared and the past gradient squared. Therefore, different thresholds can be adapted according to different priorities, and the algorithm is simple and efficient, and does not occupy a lot of computing resources and storage resources. Compared with the traditional manual setting method, the present invention can better adapt to network changes, thereby providing services that meet different service quality requirements.
最后，为了提高算法收敛速度，提出了一种基于logistic混沌映射和禁忌搜索的改进蜂群算法。采用logistic混沌映射对蜜源初始化进行改进，与传统ABC取[-1，1]的随机数相比，通过
计算结果表明，与现有技术相比，本发明在控制开销、网络服务质量和算法收敛速度上具有更好的性能。综上，本发明的SDN架构下的天地一体化网络路由策略具有良好的应用前景。The calculation results show that, compared with the prior art, the present invention has better performance in control overhead, network service quality and algorithm convergence speed. To sum up, the network routing strategy of the integration of space and earth under the SDN architecture of the present invention has a good application prospect.
附图说明Description of drawings
图1是本发明的业务转发流程图。Fig. 1 is a flow chart of service forwarding of the present invention.
图2是路由开销曲线。Figure 2 is a routing cost curve.
图3是延迟约束阈值吞吐量曲线。Figure 3 is a delay-constrained threshold throughput curve.
图4是带宽约束阈值吞吐量曲线。Figure 4 is a bandwidth constrained threshold throughput curve.
图5是丢包率约束阈值吞吐量曲线。Figure 5 is the packet loss rate constraint threshold throughput curve.
图6是算法收敛曲线。Figure 6 is the algorithm convergence curve.
图7是平均端到端时延曲线。Figure 7 is the average end-to-end delay curve.
图8是吞吐量曲线。Figure 8 is a throughput curve.
图9是丢包率曲线。Figure 9 is the packet loss rate curve.
具体实施方式Detailed ways
下面结合附图和实施例对本发明作进一步地说明。The present invention will be further described below with reference to the accompanying drawings and embodiments.
实施例：本发明使用STK与MATLAB进行联合仿真，STK软件可模拟动态卫星网络场景，使用三颗可覆盖全球的高轨卫星和30颗类铱星星座作为低轨卫星，同时选取20个非均匀分布的地面节点作为地面站。卫星坐标每1s更新一次，根据卫星位置计算出坐标，输出位置及轨道参数信息，生成动态拓扑矩阵，将生成的动态拓扑导入MATLAB后，使用MATLAB进行域内域间路径建立和基于路径传输成本的动态路由策略仿真模拟。仿真数据包在地面站发送，经过卫星节点传输，最终由地面站接收。仿真卫星参数和网络参数设置如表1-2所示：Embodiment: The present invention uses STK and MATLAB to perform co-simulation, STK software can simulate dynamic satellite network scenarios, use three high-orbit satellites that can cover the world and 30 Iridium-like constellations as low-orbit satellites, and select 20 non-uniform satellites at the same time. The distributed ground nodes act as ground stations. The satellite coordinates are updated every 1s, the coordinates are calculated according to the satellite position, the position and orbit parameter information are output, and the dynamic topology matrix is generated. After importing the generated dynamic topology into MATLAB, MATLAB is used to establish intra-domain and inter-domain paths and dynamically based on path transmission costs. Routing policy simulation simulation. The simulation data packet is sent at the ground station, transmitted through the satellite node, and finally received by the ground station. The simulated satellite parameters and network parameter settings are shown in Table 1-2:
表1：卫星参数设置Table 1: Satellite parameter settings
表2：网络参数设置Table 2: Network parameter settings
一、分层分簇网络模型性能测试1. Performance test of hierarchical clustering network model
为验证本发明所述分层分簇网络模型的性能，构建了三种SDN架构进行仿真，为了减少其他干扰因素，在相同的拓扑上构建了它们。In order to verify the performance of the hierarchical clustering network model of the present invention, three SDN architectures are constructed for simulation, and they are constructed on the same topology in order to reduce other interference factors.
它们之间的区别是控制器的数量和位置，分别为高轨卫星作为控制器的模型，高轨卫星和地面控制节点作为联合控制器的网络模型和本发明所述分层分簇网络模型。设路由代价为信令传输时延和计算资源开销之和。The difference between them is the number and location of the controllers, which are the model of the high-orbit satellite as the controller, the network model of the high-orbit satellite and the ground control node as the joint controller, and the hierarchical clustering network model of the present invention. Let the routing cost be the sum of signaling transmission delay and computing resource overhead.
设单条路径经过的总节点数
图2体现了分层分簇算法对路由代价的优化。随着节点数量的增加，本发明模型下的路由，增加的大部分是星地星间的短距离控制包传输，仅在跨簇时增加两条星际链路的长距离传输。openSAN架构下的路由开销会随着GEO卫星控制器向越来越多的节点发送长距离控制包而持续增大。SERvICE架构下的路由开销，会随着星地间控制包的，产生较大的波动。仿真结果证明，节点数量越多，本模型优化效果越好。Figure 2 shows the optimization of the routing cost by the hierarchical clustering algorithm. With the increase of the number of nodes, most of the routes under the model of the present invention are short-distance control packet transmissions between the satellite, the earth, and the satellite, and only the long-distance transmission of two interstellar links is added when crossing clusters. The routing overhead under the openSAN architecture will continue to increase as the GEO satellite controller sends long-distance control packets to more and more nodes. The routing overhead under the SERvICE architecture will fluctuate greatly with the control packets between the satellite and the ground. The simulation results show that the more the number of nodes, the better the optimization effect of this model is.
二、分层分簇网络模型性能测试2. Performance test of hierarchical clustering network model
本发明所述多优先级阈值求解方案通过对不同业务的细粒度服务质量需求进行阈值精准设置，可以在保障服务质量的前提下，通过阈值设置在算法收敛速度和网络整体性能间找到平衡。阈值设置过低，将导致选路成功率较低，进而造成算法收敛速度过慢或无法收敛，影响服务质量；阈值设置过高，将导致业务产生局部拥塞现象，网络资源无法得到充分使用，影响网络性能。The multi-priority threshold solution solution of the present invention can accurately set thresholds for fine-grained service quality requirements of different services, and can find a balance between algorithm convergence speed and overall network performance through threshold setting on the premise of ensuring service quality. If the threshold is set too low, the success rate of route selection will be low, which will cause the algorithm to converge too slowly or fail to converge, which will affect the quality of service. network performance.
使用网络吞吐量指标对阈值设置方案进行检验。网络吞吐量是单位时间内网络可以处理的最大任务数量，吞吐量越高，业务完成速度越快，对网络资源利用越充分。设置路由算法的时延阈值选择范围为1-200，带宽阈值选择范围为0-600，丢包率阈值选择范围为0.001-0.05。将三种优先级的业务，共计1000个，以相同速率注入网络中，其吞吐量随时延、带宽、丢包率阈值的波动情况如图3-5，由图3-5可以看出，当阈值设置过低时，选路成功率较低，影响了最大网络服务量，当阈值设置过高时，由于其路由方案选择集中在某几条链路，仅仅提升了局部网络负载，依然无法达到最佳网络性能。与此同时，本发明使用的多优先级阈值求解方案始终处于最优阈值或其附近，证明了阈值设置的必要性和本发明求解方法的有效性。Use network throughput metrics to test threshold setting scenarios. Network throughput is the maximum number of tasks that the network can handle per unit time. The higher the throughput, the faster the service is completed and the more fully utilized network resources. Set the delay threshold selection range of the routing algorithm to 1-200, the bandwidth threshold selection range to 0-600, and the packet loss rate threshold selection range to 0.001-0.05. A total of 1,000 services with three priorities are injected into the network at the same rate, and the fluctuations of their throughput over time delay, bandwidth, and packet loss rate threshold are shown in Figure 3-5. It can be seen from Figure 3-5 that when When the threshold is set too low, the success rate of route selection is low, which affects the maximum network service volume. When the threshold is set too high, because the routing scheme selection is concentrated on certain links, it only increases the local network load, but still cannot reach the Best network performance. At the same time, the multi-priority threshold solution scheme used in the present invention is always at or near the optimal threshold, which proves the necessity of threshold setting and the effectiveness of the solution method of the present invention.
三、IABC算法性能测试3. IABC algorithm performance test
多约束问题启发式算法较难从理论角度证明其优化性能，进而采用测试函数的优化效果来验证算法性能。在保持其他条件一致的情况下，均对搭建的卫星网络模型环境中生成的QoS请求进行仿真实验，QoS请求参数根据表3随机生成，统计在本发明算法、原始IABC算法、文献[20]MABC算法在达到2000次迭代时间的收敛情况。设置φm＝0.4，W＝0.2×D，蜂群群体规模为20，Limit值为100。It is difficult to prove the optimal performance of the heuristic algorithm for multi-constraint problems from a theoretical point of view, and then the optimization effect of the test function is used to verify the performance of the algorithm. Under the condition that other conditions are kept the same, simulation experiments are carried out on the QoS requests generated in the built satellite network model environment. The QoS request parameters are randomly generated according to Table 3. Convergence of the algorithm up to 2000 iterations. Set φm =0.4, W = 0.2×D, the colony size is 20, and the Limit value is 100.
根据图6收敛曲线图可以看出，本发明算法相对于IABC、MABC在早期收敛速度慢，但随着迭代次数的增加，在搜索后期收敛速度加快，这是因为IABC算法有跳出局部最优解的性能，在开发和开采间找到了一个平衡，即本发明改进搜索公式和概率选择公式的作用。本发明算法在迭代800次时已经达到收敛，完成2000次迭代的时间为11.5s。ABC算法和MABC算法则需要更多的迭代次数才能达到收敛，完成2000次迭代需要更长的运行时间。According to the convergence curve in Fig. 6, it can be seen that the algorithm of the present invention has a slower convergence speed in the early stage than IABC and MABC, but with the increase of the number of iterations, the convergence speed is accelerated in the later stage of the search. This is because the IABC algorithm jumps out of the local optimal solution. A balance is found between development and mining, that is, the present invention improves the function of the search formula and the probability selection formula. The algorithm of the present invention has reached convergence in 800 iterations, and the time to complete 2000 iterations is 11.5s. The ABC algorithm and the MABC algorithm require more iterations to reach convergence, and it takes longer to complete 2000 iterations.
四、SDN-AD路由算法性能测试4. Performance Test of SDN-AD Routing Algorithm
为了评估本发明提出的SDN-AD路由算法性能，本发明实现了以下三个相关算法，经典分布式卫星网络路由算法DRA，通过近似算法拉格朗日松弛法解决多约束QoS问题的MQOO算法；软件定义路由算法SDRA。并从以下几个指标进行验证，QoS质量，网路吞吐量，ISL平均流量以及路由开销。为了模拟真实的天地一体化网络应用，随机生成100个不同业务优先级的数据包，平均数据包大小设置为10Mbit并随机分布在三层网络中，数据包传输速率从每秒1Mbps到4.5Mbps不等。In order to evaluate the performance of the SDN-AD routing algorithm proposed by the present invention, the present invention implements the following three related algorithms, the classical distributed satellite network routing algorithm DRA, and the MQOO algorithm for solving the multi-constrained QoS problem by the approximate algorithm Lagrangian relaxation method; Software Defined Routing Algorithm SDRA. And it is verified from the following indicators, QoS quality, network throughput, ISL average traffic and routing overhead. In order to simulate the real world-earth integrated network application, 100 data packets with different business priorities are randomly generated. The average data packet size is set to 10Mbit and randomly distributed in the three-layer network. The data packet transmission rate varies from 1Mbps to 4.5Mbps per second. Wait.
(1)平均端到端时延：(1) Average end-to-end delay:
端到端时延是衡量QoS质量的重要指标。图7显示了业务流量从1Mbps到4Mbps时四种不同算法平均端到端时延的变化状况。由于DRA算法采用分布式路由方案，容易陷入局部最优，导致其平均时延较高。在全局业务量较小时，各类算法都能得到较好的发挥，本发明SDN-AD算法优势不明显，平均端到端时延略高于SDRA、MQOO两种算法。随着网络负载的增大，其它算法开始出现拥塞，由于SDN-AD算法在设计QoS传输成本模型时，根据数据流的优先级设置了阈值，并且始终可以找到全局最低时延路径，可以一直维持较为优秀的表现。从图7可以看出，在传输速率达到最大时，SDN-AD算法依然可以保持较低平均端到端时延，比次优算法MQOO提升了17.2％，在较高负载下依然保持了较好的性能。End-to-end delay is an important indicator to measure QoS quality. Figure 7 shows the change of the average end-to-end delay of four different algorithms when the traffic flow is from 1Mbps to 4Mbps. Since the DRA algorithm adopts a distributed routing scheme, it is easy to fall into a local optimum, resulting in a high average delay. When the global traffic volume is small, all kinds of algorithms can be well exerted. The advantages of the SDN-AD algorithm of the present invention are not obvious, and the average end-to-end delay is slightly higher than that of the SDRA and MQOO algorithms. As the network load increases, other algorithms begin to experience congestion. When designing the QoS transmission cost model, the SDN-AD algorithm sets the threshold according to the priority of the data flow, and can always find the global minimum delay path, which can be maintained for a long time. Better performance. As can be seen from Figure 7, when the transmission rate reaches the maximum, the SDN-AD algorithm can still maintain a low average end-to-end delay, which is 17.2% higher than the sub-optimal algorithm MQOO, and still maintains a good performance under higher loads. performance.
(2)网络吞吐量：(2) Network throughput:
图8显示了业务流量从1Mbps到4Mbps时吞吐量的变化状况，当业务流量小于2Mbps时，每个算法对网络吞吐量的负载能力基本相同。随着业务流量的增加，SDN-AD吞吐量的增加更为迅速并且始终拥有更高的吞吐量。当网络吞吐量达到30MBPS时，SDRA和MQOO算法接近饱和，而SDN-AD路由算法基于其分层分域的网络模型可及时收集网络QoS状态，链路代价优化目标中，考虑了链路可用带宽和路径上节点的工作负载，以及星间链路和地面链路之间的差异，将优先设置工作量较少的链路作为传输路径，从而使吞吐量更晚接近饱和。可以看出，SDN-AD算法平均吞吐量与SDRA相比增加了11.1％，与MQOO相比增加了13.7％，与DRA算法相比提升了22.4％。Figure 8 shows the change of throughput when the business flow is from 1Mbps to 4Mbps. When the business flow is less than 2Mbps, the load capacity of each algorithm to the network throughput is basically the same. As business traffic increases, SDN-AD throughput increases more rapidly and always has higher throughput. When the network throughput reaches 30MBPS, the SDRA and MQOO algorithms are close to saturation, while the SDN-AD routing algorithm can collect the network QoS status in time based on its hierarchical and domain-based network model. In the link cost optimization goal, the available bandwidth of the link is considered. And the workload of the nodes on the path, as well as the difference between the inter-satellite link and the terrestrial link, will preferentially set the link with less workload as the transmission path, so that the throughput will approach saturation later. It can be seen that the average throughput of the SDN-AD algorithm increases by 11.1% compared with SDRA, 13.7% compared with MQOO, and 22.4% compared with DRA algorithm.
(3)平均系统丢包率：(3) Average system packet loss rate:
图9显示了业务流量从1Mbps到4Mbps时丢包率的变化状况。平均系统丢包率反应了网络传输方案的可靠性，并揭示了其对环境的适应性。随着传输速率的增加，链路逐渐变得拥塞，当业务流量大于3.5Mbps时，丢包率开始明显增加。而SDN-AD路由评价了链路代价，并限制了丢包率的自适应阈值，因此相比dijistra算法，能更好的适应全局流量的增长并进行处理，可以在多层卫星网络中平衡工作量。如图所示，SDN-AD算法数据包丢失率能够稳定保持最低，比MQOO算法低7.9％，比SDRA、DRA算法低约25％。Figure 9 shows the change of the packet loss rate when the traffic flow is from 1Mbps to 4Mbps. The average system packet loss rate reflects the reliability of the network transmission scheme and reveals its adaptability to the environment. As the transmission rate increases, the link gradually becomes congested, and when the traffic flow is greater than 3.5Mbps, the packet loss rate begins to increase significantly. SDN-AD routing evaluates the link cost and limits the adaptive threshold of the packet loss rate. Therefore, compared with the dijistra algorithm, it can better adapt to the growth of global traffic and process it, and can balance work in a multi-layer satellite network. quantity. As shown in the figure, the packet loss rate of the SDN-AD algorithm can be kept at the lowest level, which is 7.9% lower than that of the MQOO algorithm, and about 25% lower than that of the SDRA and DRA algorithms.
本发明步骤B中的簇资源聚合算法程序如下：The cluster resource aggregation algorithm program in step B of the present invention is as follows:
Input：R,u，dm,n，plm,n，bm,nInput: R,u, dm,n , plm,n , bm,n
Output：边缘节点的时延矩阵，带宽矩阵，丢包率矩阵Output: Delay matrix, bandwidth matrix, packet loss rate matrix of edge nodes
Input:dl，bl，pl，Qm，Qn，s,dInput:dl,bl,pl,Qm,Qn ,s ,d
Output：
本发明不局限于本实施例，任何在本发明披露的技术范围内的等同构思或者改变，均列为本发明的保护范围。The present invention is not limited to this embodiment, and any equivalent ideas or changes within the technical scope disclosed in the present invention are included in the protection scope of the present invention.
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Families Citing this family (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116094927B (en)* | 2021-11-05 | 2025-09-16 | 中国移动通信有限公司研究院 | Network topology updating method and device and communication equipment |
| CN114268575B (en)* | 2021-12-23 | 2023-07-14 | 苏州全时空信息技术有限公司 | Self-adaptive three-dimensional transmission method and system in space-earth integrated information network |
| CN114301791B (en)* | 2021-12-29 | 2024-12-13 | 中国电信股份有限公司 | Data transmission method and device, storage medium, and electronic device |
| CN114499639B (en)* | 2022-01-19 | 2024-02-09 | 马爱 | Ant colony optimization routing method with multiple QoS constraints in low-orbit satellite network |
| CN114745321B (en)* | 2022-03-21 | 2023-06-16 | 南京邮电大学 | QoS perception routing method of satellite network based on SDN |
| CN114698117B (en)* | 2022-03-21 | 2024-12-03 | 西安交通大学 | A resource allocation method for real-time services in a space-ground network |
| CN114938235B (en)* | 2022-04-08 | 2023-05-30 | 北京邮电大学 | Satellite network data transmission method and device integrating multipath and network coding |
| CN115276755B (en)* | 2022-06-20 | 2023-05-02 | 哈尔滨工业大学（威海） | Inter-satellite link and power distribution method for satellite network communication |
| CN115277394B (en)* | 2022-07-26 | 2024-10-01 | 江南大学 | A fitness-based edge server deployment method in mobile edge networks |
| CN114978979B (en)* | 2022-07-27 | 2022-10-14 | 网络通信与安全紫金山实验室 | Route generation method, apparatus and storage medium |
| CN115801093B (en)* | 2022-10-14 | 2024-07-09 | 西安空间无线电技术研究所 | Path planning method for guaranteeing end-to-end deterministic time delay of satellite network |
| CN115765840B (en)* | 2022-11-09 | 2025-03-25 | 北京邮电大学 | A QoS-oriented on-board communication resource allocation method based on joint clustering and intra-cluster beam hopping |
| CN115733542B (en)* | 2022-11-24 | 2025-04-22 | 北京信息科技大学 | Intersatellite link path planning method, device and medium |
| CN115632701B (en)* | 2022-12-21 | 2023-04-18 | 中南大学 | A low-orbit satellite network data scheduling method, system, device and storage medium |
| CN116016278B (en)* | 2022-12-22 | 2024-07-02 | 四川九州电子科技股份有限公司 | Dynamic adjustment method for EDCA parameters |
| CN116015417B (en)* | 2022-12-27 | 2025-06-27 | 西安交通大学 | Adaptive link state updating method and system based on DQN in LEO satellite network |
| CN116015424B (en)* | 2023-01-10 | 2025-05-02 | 广州大学 | Artificial bee colony low-orbit satellite network routing method, system, device and medium |
| CN116318329B (en)* | 2023-02-02 | 2025-07-01 | 西安电子科技大学 | Joint planning method of satellite network topology and routing based on time-extended graph |
| CN115967435B (en)* | 2023-02-16 | 2025-04-08 | 重庆邮电大学 | Method for establishing cross-layer link in multi-layer low-orbit satellite network |
| CN116346196B (en)* | 2023-02-20 | 2025-09-09 | 西安电子科技大学 | Multi-layer constellation interlayer link design method with enhanced service coverage capability |
| CN116545853B (en)* | 2023-07-04 | 2023-10-13 | 南京理工大学 | Integrated network multi-objective optimized resource management method based on quantum particle swarm |
| CN116600352B (en)* | 2023-07-19 | 2023-09-15 | 北京最终前沿深空科技有限公司 | Space-earth integrated QoS consistency processing method, qoS convergent and QoS orchestrator |
| CN117544220B (en)* | 2023-11-08 | 2024-05-07 | 中国人民解放军军事科学院系统工程研究院 | Routing control method and device for high-low orbit satellite communication network |
| CN117675679B (en)* | 2023-11-24 | 2024-08-30 | 航天恒星科技有限公司 | Satellite time-sensitive routing scheduling method based on domain optimization |
| CN118157746B (en)* | 2024-03-27 | 2025-06-27 | 哈尔滨工业大学 | Service chain embedding method based on context awareness in space-air-ground integrated network |
| CN118870463B (en)* | 2024-07-19 | 2025-09-26 | 北京邮电大学 | A smart integrated collaborative transmission method for space-ground-ground three-dimensional heterogeneous networks |
| CN119485577A (en)* | 2024-11-11 | 2025-02-18 | 重庆邮电大学 | A clustering networking strategy for mobile ad hoc networks based on GWO in satellite-ground integration scenarios |
| CN119865433B (en)* | 2025-03-25 | 2025-07-01 | 重庆朗奕迪实业有限公司 | Dynamic optimization method, device and medium for central Mesh network |
| CN119921843B (en)* | 2025-04-02 | 2025-07-08 | 杭州电子科技大学 | Dynamic scheduling method, device and medium for multipath route of satellite communication system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108307435A (en)* | 2018-01-29 | 2018-07-20 | 大连大学 | A kind of multitask route selection method based on SDSIN |
| CN110493131A (en)* | 2019-09-24 | 2019-11-22 | 大连大学 | A kind of design method of Information Network routing policy under SDN framework |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110730131B (en)* | 2019-10-22 | 2020-07-17 | 电子科技大学 | SDN satellite network multi-QoS constraint routing method based on improved ant colony |
- 2021
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108307435A (en)* | 2018-01-29 | 2018-07-20 | 大连大学 | A kind of multitask route selection method based on SDSIN |
| CN110493131A (en)* | 2019-09-24 | 2019-11-22 | 大连大学 | A kind of design method of Information Network routing policy under SDN framework |
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