Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose...Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.展开更多
跨数据中心网络是处于不同地区的数据中心网络(Data Center Networks,DCNs)通过广域网(Wide-Area Network,WAN)连接组成的网络,分布式应用通常基于该网络为用户提供高质量的服务。DCNs和WAN的缓冲区大小、往返时延存在显著差异,这导致...跨数据中心网络是处于不同地区的数据中心网络(Data Center Networks,DCNs)通过广域网(Wide-Area Network,WAN)连接组成的网络,分布式应用通常基于该网络为用户提供高质量的服务。DCNs和WAN的缓冲区大小、往返时延存在显著差异,这导致现有的Cubic拥塞控制算法在跨数据中心网络场景下出现降速不准确、丢包率过高以及与其他拥塞控制算法兼容性差等问题。针对以上挑战,提出了一种通过匹配不同发送速率模式的改进Cubic算法Cubic+。具体地,Cubic+整合了网络中的时延、ECN(Explicit Congestion Notification)和丢包信号。当拥塞发生在浅缓冲交换机时,Cubic+会周期性地排空队列;当拥塞发生在深缓冲路由器时,Cubic+会快速减少堆积的数据包。基于大规模NS3仿真实验结果表明,在跨数据中心网络流量模型下,Cubic+与现有流行算法相比,平均流完成时间最多减少了20.77%,第99百分位流完成时间最多减少了15.88%,为跨数据中心网络提供了一种高吞吐的拥塞控制算法。展开更多
基金co-supported by the National Science Foundation for Young Scientists of China(No.61401011)the National Key Technologies R&D Program of China(No.2015BAG15B01)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61521091)
文摘Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.
