The Software Defined Networking(SDN) paradigm separates the control plane from the packet forwarding plane, and provides applications with a centralized view of the distributed network state. Thanks to the flexibility...The Software Defined Networking(SDN) paradigm separates the control plane from the packet forwarding plane, and provides applications with a centralized view of the distributed network state. Thanks to the flexibility and efficiency of the traffic flow management, SDN based traffic engineering increases network utilization and improves Quality of Service(QoS). In this paper, an SDN based traffic scheduling algorithm called CATS is proposed to detect and control congestions in real time. In particular, a new concept of aggregated elephant flow is presented. And then a traffic scheduling optimization model is formulated with the goal of minimizing the variance of link utilization and improving QoS. We develop a chaos genetic algorithm to solve this NP-hard problem. At the end of this paper, we use Mininet, Floodlight and video traces to simulate the SDN enabled video networking. We simulate both the case of live video streaming in the wide area backbone network and the case of video file transferring among data centers. Simulation results show that the proposed algorithm CATS effectively eliminates network congestions in subsecond. In consequence, CATS improves the QoS with lower packet loss rate and balanced link utilization.展开更多
文中提出了一种基于路段关联度的城市交通流量Apriori-LSTM(Apriori-long short term memory network)预测模型.处理卡口检测器数据,统计交通量并提取车辆轨迹,采用Apriori算法分析预测时段内目标路段与关联路段的时空相关性,计算关联...文中提出了一种基于路段关联度的城市交通流量Apriori-LSTM(Apriori-long short term memory network)预测模型.处理卡口检测器数据,统计交通量并提取车辆轨迹,采用Apriori算法分析预测时段内目标路段与关联路段的时空相关性,计算关联路段支持度;求解预测时段内关联路段到目标路段的流入量,构建LSTM预测的输入矩阵、并采用LSTM预测路段短时流量.采用实例进行验证,对迭代次数、隐藏层神经元个数和步长进行参数灵敏度分析,并与单一的LSTM预测结果进行比较.结果表明:Apriori-LSTM的平均绝对误差降至3.8%,平均绝对百分误差和平均均方误差均有显著降低,均等系数有所提高,模型稳定性更好,达到了更好预测效果.展开更多
基金partly supported by NSFC under grant No.61371191 and No.61472389
文摘The Software Defined Networking(SDN) paradigm separates the control plane from the packet forwarding plane, and provides applications with a centralized view of the distributed network state. Thanks to the flexibility and efficiency of the traffic flow management, SDN based traffic engineering increases network utilization and improves Quality of Service(QoS). In this paper, an SDN based traffic scheduling algorithm called CATS is proposed to detect and control congestions in real time. In particular, a new concept of aggregated elephant flow is presented. And then a traffic scheduling optimization model is formulated with the goal of minimizing the variance of link utilization and improving QoS. We develop a chaos genetic algorithm to solve this NP-hard problem. At the end of this paper, we use Mininet, Floodlight and video traces to simulate the SDN enabled video networking. We simulate both the case of live video streaming in the wide area backbone network and the case of video file transferring among data centers. Simulation results show that the proposed algorithm CATS effectively eliminates network congestions in subsecond. In consequence, CATS improves the QoS with lower packet loss rate and balanced link utilization.
文摘文中提出了一种基于路段关联度的城市交通流量Apriori-LSTM(Apriori-long short term memory network)预测模型.处理卡口检测器数据,统计交通量并提取车辆轨迹,采用Apriori算法分析预测时段内目标路段与关联路段的时空相关性,计算关联路段支持度;求解预测时段内关联路段到目标路段的流入量,构建LSTM预测的输入矩阵、并采用LSTM预测路段短时流量.采用实例进行验证,对迭代次数、隐藏层神经元个数和步长进行参数灵敏度分析,并与单一的LSTM预测结果进行比较.结果表明:Apriori-LSTM的平均绝对误差降至3.8%,平均绝对百分误差和平均均方误差均有显著降低,均等系数有所提高,模型稳定性更好,达到了更好预测效果.
