Software-defined networks （SDN） have attracted much attention recently because of their flexibility in terms of network management. Increasingly, SDN is being introduced into wireless networks to form wireless SDN. ...Software-defined networks （SDN） have attracted much attention recently because of their flexibility in terms of network management. Increasingly, SDN is being introduced into wireless networks to form wireless SDN. One enabling technology for wireless SDN is network virtualization, which logically divides one wireless network element, such as a base station, into multiple slices, and each slice serving as a standalone virtual BS. In this way, one physical mobile wireless network can be partitioned into multiple virtual networks in a software-defined manner. Wireless virtual networks comprising virtual base stations also need to provide QoS to mobile end-user services in the same context as their physical hosting networks. One key QoS parameter is delay. This paper presents a delay model for software-defined wireless virtual networks. Network calculus is used in the modelling. In particular, stochastic network calculus, which describes more realistic models than deterministic network calculus, is used. The model enables theoretical investigation of wireless SDN, which is largely dominated by either algorithms or prototype implementations.展开更多
Software defined networking（SDN） has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a ...Software defined networking（SDN） has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a successful carrier grade network with SDN, in terms of scalability, reliability, Qo S and service management. In this paper, graph theory and traffic engineering are used to model the network state transitions and path assignment problem at first. Next, we present a quantitative assessment model on path assignment for a centralized controller to assess different kinds of path assignments, considering optimal path utilization, network load balance, network load volatility, and resource utilization simultaneously. In the end, an example forquantitatively assessing existing different path assignments is detailed to illustrate our proposed model.展开更多
Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks...Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.展开更多
为解决高密度无线局域网中接入拥塞、资源失衡及流量混传导致的性能瓶颈问题,从接入层面、资源层面及流量层面分析无线局域网接入拥塞问题,并从3个层面提出基于软件定义网络(Software Defined Network,SDN)流量调度的应对策略。实践案...为解决高密度无线局域网中接入拥塞、资源失衡及流量混传导致的性能瓶颈问题,从接入层面、资源层面及流量层面分析无线局域网接入拥塞问题,并从3个层面提出基于软件定义网络(Software Defined Network,SDN)流量调度的应对策略。实践案例验证了该策略在提升网络吞吐量、降低传输时延方面具有明显成效。展开更多
基金supported in part by the grant from the National Natural Science Foundation of China (60973129)
文摘Software-defined networks （SDN） have attracted much attention recently because of their flexibility in terms of network management. Increasingly, SDN is being introduced into wireless networks to form wireless SDN. One enabling technology for wireless SDN is network virtualization, which logically divides one wireless network element, such as a base station, into multiple slices, and each slice serving as a standalone virtual BS. In this way, one physical mobile wireless network can be partitioned into multiple virtual networks in a software-defined manner. Wireless virtual networks comprising virtual base stations also need to provide QoS to mobile end-user services in the same context as their physical hosting networks. One key QoS parameter is delay. This paper presents a delay model for software-defined wireless virtual networks. Network calculus is used in the modelling. In particular, stochastic network calculus, which describes more realistic models than deterministic network calculus, is used. The model enables theoretical investigation of wireless SDN, which is largely dominated by either algorithms or prototype implementations.
基金Supported by the National Natural Science Foundation of China(61373040,61173137)the Ph.D.Programs Foundation of Ministry of Education of China(20120141110002)the Key Project of Natural Science Foundation of Hubei Province(2010CDA004)
文摘Software defined networking（SDN） has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a successful carrier grade network with SDN, in terms of scalability, reliability, Qo S and service management. In this paper, graph theory and traffic engineering are used to model the network state transitions and path assignment problem at first. Next, we present a quantitative assessment model on path assignment for a centralized controller to assess different kinds of path assignments, considering optimal path utilization, network load balance, network load volatility, and resource utilization simultaneously. In the end, an example forquantitatively assessing existing different path assignments is detailed to illustrate our proposed model.
文摘Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.
文摘为解决高密度无线局域网中接入拥塞、资源失衡及流量混传导致的性能瓶颈问题,从接入层面、资源层面及流量层面分析无线局域网接入拥塞问题,并从3个层面提出基于软件定义网络(Software Defined Network,SDN)流量调度的应对策略。实践案例验证了该策略在提升网络吞吐量、降低传输时延方面具有明显成效。
