Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,t...Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.展开更多
Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing...Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).展开更多
Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a ...Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.展开更多
The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the O...The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.展开更多
The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in...The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.展开更多
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 a...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.展开更多
To provide ubiquitous Internet access under the explosive increase of applications and data traffic,the current network architecture has become highly heterogeneous and complex,making network management a challenging ...To provide ubiquitous Internet access under the explosive increase of applications and data traffic,the current network architecture has become highly heterogeneous and complex,making network management a challenging task.To this end,software-defined networking(SDN) has been proposed as a promising solution.In the SDN architecture,the control plane and the data plane are decoupled,and the network infrastructures are abstracted and managed by a centralized controller.With SDN,efficient and flexible network control can be achieved,which potentially enhances network performance.To harvest the benefits of SDN in wireless networks,the software-defined wireless network(SDWN) architecture has been recently considered.In this paper,we first analyze the applications of SDN to different types of wireless networks.We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks.Finally,we present possible future research directions of SDWN.展开更多
Controllers play a critical role in software-defined networking(SDN).However,existing singlecontroller SDN architectures are vulnerable to single-point failures,where a controller's capacity can be saturated by fl...Controllers play a critical role in software-defined networking(SDN).However,existing singlecontroller SDN architectures are vulnerable to single-point failures,where a controller's capacity can be saturated by flooded flow requests.In addition,due to the complicated interactions between applications and controllers,the flow setup latency is relatively large.To address the above security and performance issues of current SDN controllers,we propose distributed rule store(DRS),a new multi-controller architecture for SDNs.In DRS,the controller caches the flow rules calculated by applications,and distributes these rules to multiple controller instances.Each controller instance holds only a subset of all rules,and periodically checks the consistency of flow rules with each other.Requests from switches are distributed among multiple controllers,in order to mitigate controller capacity saturation attack.At the same time,when rules at one controller are maliciously modified,they can be detected and recovered in time.We implement DRS based on Floodlight and evaluate it with extensive emulation.The results show that DRS can effectively maintain a consistently distributed rule store,and at the same time can achieve a shorter flow setup time and a higher processing throughput,compared with ONOS and Floodlight.展开更多
Software-defined networking (SDN) decouples the data and control planes. However, attackers can lead catastrophic results to the whole network using manipulated flooding packets, called the data-to-control-plane satur...Software-defined networking (SDN) decouples the data and control planes. However, attackers can lead catastrophic results to the whole network using manipulated flooding packets, called the data-to-control-plane saturation attacks. The existing methods, using centralized mitigation policies and ignoring the buffered attack flows, involve extra network entities and make benign traffic suffer from long network recovery delays. For these purposes, we propose LFSDM, a saturation attack detection and mitigation system, which solves these challenges by leveraging three new techniques: 1) using linear discriminant analysis (LDA) and extracting a novel feature called control channel occupation rate (CCOR) to detect the attacks, 2) adopting the distributed mitigation agents to reduce the number of involved network entities and, 3) cleaning up the buffered attack flows to enable fast recovery. Experiments show that our system can detect the attacks timely and accurately. More importantly, compared with the previous work, we save 81% of the network recovery delay under attacks ranging from 1,000 to 4,000 packets per second (PPS) on average, and 87% of the network recovery delay under higher attack rates with PPS ranging from 5,000 to 30,000.展开更多
Currently,the Internet of Things(IoT)is revolutionizing communi-cation technology by facilitating the sharing of information between different physical devices connected to a network.To improve control,customization,f...Currently,the Internet of Things(IoT)is revolutionizing communi-cation technology by facilitating the sharing of information between different physical devices connected to a network.To improve control,customization,flexibility,and reduce network maintenance costs,a new Software-Defined Network(SDN)technology must be used in this infrastructure.Despite the various advantages of combining SDN and IoT,this environment is more vulnerable to various attacks due to the centralization of control.Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service(DDoS)attacks,but they often lack mechanisms to mitigate their severity.This paper proposes a Multi-Attack Intrusion Detection System(MAIDS)for Software-Defined IoT Networks(SDN-IoT).The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms.First,a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets:the Network Security Laboratory Knowledge Discovery in Databases(NSL-KDD)and the Canadian Institute for Cyberse-curity Intrusion Detection Systems(CICIDS2017),to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems.The algorithms evaluated include Extreme Gradient Boosting(XGBoost),K-Nearest Neighbor(KNN),Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR).Second,an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems(IDS)was developed to enable effective comparison between the datasets used in the development of the security scheme.The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system,with average accuracies of 99.88%and 99.89%,respectively.Furthermore,the proposed security scheme reduced the false alarm rate by 33.23%,which is a significant improvement over prevalent schemes.Finally,tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset,making it the best for IDS compared to the NSL-KDD dataset.展开更多
Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively ...Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively manage,optimize,and maintain these systems.Due to their distributed nature,machine learning models are challenging to deploy in traditional networks.However,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes.SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis.While the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to cyberattacks.To address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments.This paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging issues.We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks.Finally,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.展开更多
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 generat...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-<span>defined multihop wireless networking</span><span> (SDMWN).</span>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<span "=""> </span>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.展开更多
As a new networking paradigm,Software-Defined Networking(SDN)enables us to cope with the limitations of traditional networks.SDN uses a controller that has a global view of the network and switch devices which act as ...As a new networking paradigm,Software-Defined Networking(SDN)enables us to cope with the limitations of traditional networks.SDN uses a controller that has a global view of the network and switch devices which act as packet forwarding hardware,known as“OpenFlow switches”.Since load balancing service is essential to distribute workload across servers in data centers,we propose an effective load balancing scheme in SDN,using a genetic programming approach,called Genetic Programming based Load Balancing(GPLB).We formulate the problem to find a path:1)with the best bottleneck switch which has the lowest capacity within bottleneck switches of each path,2)with the shortest path,and 3)requiring the less possible operations.For the purpose of choosing the real-time least loaded path,GPLB immediately calculates the integrated load of paths based on the information that receives from the SDN controller.Hence,in this design,the controller sends the load information of each path to the load balancing algorithm periodically and then the load balancing algorithm returns a least loaded path to the controller.In this paper,we use the Mininet emulator and the OpenDaylight controller to evaluate the effectiveness of the GPLB.The simulative study of the GPLB shows that there is a big improvement in performance metrics and the latency and the jitter are minimized.The GPLB also has the maximum throughput in comparison with related works and has performed better in the heavy traffic situation.The results show that our model stands smartly while not increasing further overhead.展开更多
To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-...To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking(SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.展开更多
Software-defined networking(SDN)plays a critical role in transforming networking from traditional to intelligent networking.The increasing demand for services from cloud users has increased the load on the network.An ...Software-defined networking(SDN)plays a critical role in transforming networking from traditional to intelligent networking.The increasing demand for services from cloud users has increased the load on the network.An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service(QoS).Themultiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms.Moreover,the requests need to be transferred to reliable network elements.To address SDN’s current and future challenges,there is a need to know how artificial intelligence(AI)optimization techniques can efficiently balance the load.This study aims to explore two artificial intelligence optimization techniques,namely Ant Colony Optimization(ACO)and Particle Swarm Optimization(PSO),used for load balancing in SDN.Further,we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing.Finally,we propose a conceptual framework for SDN futurology by evaluating node and link reliability,which can balance the load efficiently and improve QoS in SDN.展开更多
Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging ...Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput.展开更多
Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some o...Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.展开更多
In software-defined networking(SDN)networks,unlike traditional networks,the control plane is located separately in a device or program.One of the most critical problems in these networks is a controller placement prob...In software-defined networking(SDN)networks,unlike traditional networks,the control plane is located separately in a device or program.One of the most critical problems in these networks is a controller placement problem,which has a significant impact on the network’s overall performance.This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing.The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem.It demonstrates through careful computational studies that the formulations can design networks with much less installation cost while accepting a general connected topology among controllers and user-defined survivability parameters.The researchers used the proposed framework on six different topologies then analyzed and compared with Iterated Local Search(ILS)and Expansion model for the controller placement problem(EMCPP)along with considering several evaluation criteria.The results show that the proposed framework outperforms the ILS and EMCPP.Thus,the proposed framework has a 38.53%and 38.02%improvement in reducing network implementation costs than EMCPP and ILS,respectively.展开更多
Software-defined network(SDN)is a new form of network architecture that has programmability,ease of use,centralized control,and protocol independence.It has received high attention since its birth.With SDN network arc...Software-defined network(SDN)is a new form of network architecture that has programmability,ease of use,centralized control,and protocol independence.It has received high attention since its birth.With SDN network architecture,network management becomes more efficient,and programmable interfaces make network operations more flexible and can meet the different needs of various users.The mainstream communication protocol of SDN is OpenFlow,which contains aMatch Field in the flow table structure of the protocol,which matches the content of the packet header of the data received by the switch,and completes the corresponding actions according to the matching results,getting rid of the dependence on the protocol to avoid designing a new protocol.In order to effectively optimize the routing forSDN,this paper proposes a novel algorithm based on reinforcement learning.The proposed technique canmaximize numerous objectives to dynamically update the routing strategy,and it has great generality and is not reliant on any specific network state.The control of routing strategy is more complicated than many Q-learning-based algorithms due to the employment of reinforcement learning.The performance of the method is tested by experiments using the OMNe++simulator.The experimental results reveal that our PPO-based SDN routing control method has superior performance and stability than existing algorithms.展开更多
Under the background of the rapid development of ground mobile communication,the advantages of high coverage,survivability,and flexibility of satellite communication provide air support to the construction of space in...Under the background of the rapid development of ground mobile communication,the advantages of high coverage,survivability,and flexibility of satellite communication provide air support to the construction of space information network.According to the requirements of the future space information communication,a software-defined Space-Air-Ground Integrated network architecture was proposed.It consisted of layered structure satellite backbone network,deep space communication network,the stratosphere communication network and the ground network.The Space-Air-Ground Integrated network was supported by the satellite backbone network.It provided data relay for the missions such as deep space exploration and controlled the deep-space spacecraft when needed.In addition,it safeguarded the anti-destructibility of stratospheric communication and assisted the stratosphere to supplement ground network communication.In this paper,algorithm requirements of the congestion control and routing of satellite backbone protocols for heterogeneous users’services were proposed.The algorithm requirements of distinguishing different service objects for the deep space communication and stratospheric communication network protocols were described.Considering the realistic demand for the dynamic coverage of the satellite backbone network and node cost,the multi-layer satellite backbone network architecture was constructed.On this basis,the proposed Software-defined Space-Air-Ground Integrated network architecture could be built as a large,scalable and efficient communication network that could be integrated into space,air,and ground.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities(2021RC239)the Postdoctoral Science Foundation of China(2021 M690338)+3 种基金the Hainan Provincial Natural Science Foundation of China(620RC562,2019RC096,620RC560)the Scientific Research Setup Fund of Hainan University(KYQD(ZR)1877)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(61802092,62162021).
文摘Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.
基金supported by the National Natural Science Foundation of China for Innovative Research Groups (61521003)the National Natural Science Foundation of China (61872382)+1 种基金the National Key Research and Development Program of China (2017YFB0803204)the Research and Development Program in Key Areas of Guangdong Province (No.2018B010113001)
文摘Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).
基金This work was supported by National Natural Science Foundation of China(No.61802080 and 61802077)Guangdong General Colleges and Universities Research Project(2018GkQNCX105)+1 种基金Zhongshan Public Welfare Science and Technology Research Project(2019B2044)Keping Yu was supported in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044.
文摘Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.
基金This research was supported partially by LIG Nex1It was also supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning Evaluation).
文摘The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.
文摘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.
基金supported by the US National Science Foundation(Nos.CNS-1247955 and CNS-1320664)the Wireless Engineering Research and Education Center(WEREC)at Auburn University,Auburn,USA
文摘To provide ubiquitous Internet access under the explosive increase of applications and data traffic,the current network architecture has become highly heterogeneous and complex,making network management a challenging task.To this end,software-defined networking(SDN) has been proposed as a promising solution.In the SDN architecture,the control plane and the data plane are decoupled,and the network infrastructures are abstracted and managed by a centralized controller.With SDN,efficient and flexible network control can be achieved,which potentially enhances network performance.To harvest the benefits of SDN in wireless networks,the software-defined wireless network(SDWN) architecture has been recently considered.In this paper,we first analyze the applications of SDN to different types of wireless networks.We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks.Finally,we present possible future research directions of SDWN.
基金supported by the National Natural Science Foundation of China(Nos.61402357,61272459,and 61402357)the China Postdoctoral Science Foundation(No.2015M570835)+2 种基金the Fundamental Research Funds for the Central Universities,Chinathe Program for New Century Excellent Talents in Universitythe CETC 54 Project(No.ITD-U14001/KX142600008)
文摘Controllers play a critical role in software-defined networking(SDN).However,existing singlecontroller SDN architectures are vulnerable to single-point failures,where a controller's capacity can be saturated by flooded flow requests.In addition,due to the complicated interactions between applications and controllers,the flow setup latency is relatively large.To address the above security and performance issues of current SDN controllers,we propose distributed rule store(DRS),a new multi-controller architecture for SDNs.In DRS,the controller caches the flow rules calculated by applications,and distributes these rules to multiple controller instances.Each controller instance holds only a subset of all rules,and periodically checks the consistency of flow rules with each other.Requests from switches are distributed among multiple controllers,in order to mitigate controller capacity saturation attack.At the same time,when rules at one controller are maliciously modified,they can be detected and recovered in time.We implement DRS based on Floodlight and evaluate it with extensive emulation.The results show that DRS can effectively maintain a consistently distributed rule store,and at the same time can achieve a shorter flow setup time and a higher processing throughput,compared with ONOS and Floodlight.
基金The work was supported in part by the National Natural Science Foundation of China under Grant Nos.61972371,U19B2023 and U19B2044the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant No.Y202093.
文摘Software-defined networking (SDN) decouples the data and control planes. However, attackers can lead catastrophic results to the whole network using manipulated flooding packets, called the data-to-control-plane saturation attacks. The existing methods, using centralized mitigation policies and ignoring the buffered attack flows, involve extra network entities and make benign traffic suffer from long network recovery delays. For these purposes, we propose LFSDM, a saturation attack detection and mitigation system, which solves these challenges by leveraging three new techniques: 1) using linear discriminant analysis (LDA) and extracting a novel feature called control channel occupation rate (CCOR) to detect the attacks, 2) adopting the distributed mitigation agents to reduce the number of involved network entities and, 3) cleaning up the buffered attack flows to enable fast recovery. Experiments show that our system can detect the attacks timely and accurately. More importantly, compared with the previous work, we save 81% of the network recovery delay under attacks ranging from 1,000 to 4,000 packets per second (PPS) on average, and 87% of the network recovery delay under higher attack rates with PPS ranging from 5,000 to 30,000.
文摘Currently,the Internet of Things(IoT)is revolutionizing communi-cation technology by facilitating the sharing of information between different physical devices connected to a network.To improve control,customization,flexibility,and reduce network maintenance costs,a new Software-Defined Network(SDN)technology must be used in this infrastructure.Despite the various advantages of combining SDN and IoT,this environment is more vulnerable to various attacks due to the centralization of control.Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service(DDoS)attacks,but they often lack mechanisms to mitigate their severity.This paper proposes a Multi-Attack Intrusion Detection System(MAIDS)for Software-Defined IoT Networks(SDN-IoT).The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms.First,a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets:the Network Security Laboratory Knowledge Discovery in Databases(NSL-KDD)and the Canadian Institute for Cyberse-curity Intrusion Detection Systems(CICIDS2017),to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems.The algorithms evaluated include Extreme Gradient Boosting(XGBoost),K-Nearest Neighbor(KNN),Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR).Second,an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems(IDS)was developed to enable effective comparison between the datasets used in the development of the security scheme.The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system,with average accuracies of 99.88%and 99.89%,respectively.Furthermore,the proposed security scheme reduced the false alarm rate by 33.23%,which is a significant improvement over prevalent schemes.Finally,tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset,making it the best for IDS compared to the NSL-KDD dataset.
文摘Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively manage,optimize,and maintain these systems.Due to their distributed nature,machine learning models are challenging to deploy in traditional networks.However,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes.SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis.While the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to cyberattacks.To address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments.This paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging issues.We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks.Finally,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.
文摘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-<span>defined multihop wireless networking</span><span> (SDMWN).</span>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<span "=""> </span>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.
文摘As a new networking paradigm,Software-Defined Networking(SDN)enables us to cope with the limitations of traditional networks.SDN uses a controller that has a global view of the network and switch devices which act as packet forwarding hardware,known as“OpenFlow switches”.Since load balancing service is essential to distribute workload across servers in data centers,we propose an effective load balancing scheme in SDN,using a genetic programming approach,called Genetic Programming based Load Balancing(GPLB).We formulate the problem to find a path:1)with the best bottleneck switch which has the lowest capacity within bottleneck switches of each path,2)with the shortest path,and 3)requiring the less possible operations.For the purpose of choosing the real-time least loaded path,GPLB immediately calculates the integrated load of paths based on the information that receives from the SDN controller.Hence,in this design,the controller sends the load information of each path to the load balancing algorithm periodically and then the load balancing algorithm returns a least loaded path to the controller.In this paper,we use the Mininet emulator and the OpenDaylight controller to evaluate the effectiveness of the GPLB.The simulative study of the GPLB shows that there is a big improvement in performance metrics and the latency and the jitter are minimized.The GPLB also has the maximum throughput in comparison with related works and has performed better in the heavy traffic situation.The results show that our model stands smartly while not increasing further overhead.
基金supported by the State Key Program of National Natural Science Foundation of China(Grant No.61233003)National Natural Science Foundation of China(Grant No.61503358)
文摘To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking(SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.
基金The authors received Excellent Graduate Assistant funding from Universiti Kuala Lumpur for this study.
文摘Software-defined networking(SDN)plays a critical role in transforming networking from traditional to intelligent networking.The increasing demand for services from cloud users has increased the load on the network.An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service(QoS).Themultiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms.Moreover,the requests need to be transferred to reliable network elements.To address SDN’s current and future challenges,there is a need to know how artificial intelligence(AI)optimization techniques can efficiently balance the load.This study aims to explore two artificial intelligence optimization techniques,namely Ant Colony Optimization(ACO)and Particle Swarm Optimization(PSO),used for load balancing in SDN.Further,we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing.Finally,we propose a conceptual framework for SDN futurology by evaluating node and link reliability,which can balance the load efficiently and improve QoS in SDN.
基金This work was funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.5199990914048)this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2020R1I1A3066543)In addition,this work was supported by the Soonchunhyang University Research Fund.
文摘Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput.
基金supported by the China Postdoctoral Fund Project (No.44603)the National Natural Science Foundation of China (No.61309020)+1 种基金the National key Research and Development Program of China (No.2016YFB0800100, 2016YFB0800101)the National Natural Science Fund for Creative Research Groups Project(No.61521003)
文摘Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.
文摘In software-defined networking(SDN)networks,unlike traditional networks,the control plane is located separately in a device or program.One of the most critical problems in these networks is a controller placement problem,which has a significant impact on the network’s overall performance.This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing.The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem.It demonstrates through careful computational studies that the formulations can design networks with much less installation cost while accepting a general connected topology among controllers and user-defined survivability parameters.The researchers used the proposed framework on six different topologies then analyzed and compared with Iterated Local Search(ILS)and Expansion model for the controller placement problem(EMCPP)along with considering several evaluation criteria.The results show that the proposed framework outperforms the ILS and EMCPP.Thus,the proposed framework has a 38.53%and 38.02%improvement in reducing network implementation costs than EMCPP and ILS,respectively.
基金The researchers would like to thank the Deanship of Scientific Research,Qassim University for funding the publication of this project.
文摘Software-defined network(SDN)is a new form of network architecture that has programmability,ease of use,centralized control,and protocol independence.It has received high attention since its birth.With SDN network architecture,network management becomes more efficient,and programmable interfaces make network operations more flexible and can meet the different needs of various users.The mainstream communication protocol of SDN is OpenFlow,which contains aMatch Field in the flow table structure of the protocol,which matches the content of the packet header of the data received by the switch,and completes the corresponding actions according to the matching results,getting rid of the dependence on the protocol to avoid designing a new protocol.In order to effectively optimize the routing forSDN,this paper proposes a novel algorithm based on reinforcement learning.The proposed technique canmaximize numerous objectives to dynamically update the routing strategy,and it has great generality and is not reliant on any specific network state.The control of routing strategy is more complicated than many Q-learning-based algorithms due to the employment of reinforcement learning.The performance of the method is tested by experiments using the OMNe++simulator.The experimental results reveal that our PPO-based SDN routing control method has superior performance and stability than existing algorithms.
基金This work is supported by Fundamental Research Funds for the Central Universities of China(328201911)C.G.(Chao Guo),the Open Project Program of National Engineering Laboratory for Agri-product Quality Traceability,C.G.(Chao Guo)+2 种基金Beijing Technology and Business University(BTBU)No.AQT-2018Y-B4,C.G.(Chao Guo)Higher Education Department of the Ministry of Education Industry-university Cooperative Education Project,C.G.(Chao Guo)Education and Teaching Reform Project of Beijing Electronic and Technology Institute,C.G.(Chao Guo).
文摘Under the background of the rapid development of ground mobile communication,the advantages of high coverage,survivability,and flexibility of satellite communication provide air support to the construction of space information network.According to the requirements of the future space information communication,a software-defined Space-Air-Ground Integrated network architecture was proposed.It consisted of layered structure satellite backbone network,deep space communication network,the stratosphere communication network and the ground network.The Space-Air-Ground Integrated network was supported by the satellite backbone network.It provided data relay for the missions such as deep space exploration and controlled the deep-space spacecraft when needed.In addition,it safeguarded the anti-destructibility of stratospheric communication and assisted the stratosphere to supplement ground network communication.In this paper,algorithm requirements of the congestion control and routing of satellite backbone protocols for heterogeneous users’services were proposed.The algorithm requirements of distinguishing different service objects for the deep space communication and stratospheric communication network protocols were described.Considering the realistic demand for the dynamic coverage of the satellite backbone network and node cost,the multi-layer satellite backbone network architecture was constructed.On this basis,the proposed Software-defined Space-Air-Ground Integrated network architecture could be built as a large,scalable and efficient communication network that could be integrated into space,air,and ground.
