Layer 2 network technology is extending beyond its traditional local area implementation and finding wider acceptance in provider＇s metropolitan area networks and large-scale cloud data center networks. This is mainl...Layer 2 network technology is extending beyond its traditional local area implementation and finding wider acceptance in provider＇s metropolitan area networks and large-scale cloud data center networks. This is mainly due to its plug-and-play capability and native mobility support. Many efforts have been put to increase the bisection bandwidth in layer 2 network, which has been constrained by the spanning tree protocol （STP） that layer 2 network uses for preventing looping. The recent trend is to incorporate layer 3＇s routing approach into layer 2 network so that multiple paths can be used for forwarding traffic between any source-destination （S-D） node pair. Equal cost multipath （ECMP） is one such example. However, ECMP may still be limited in generating multiple paths due to its shortest path （lowest cost） requirement. In this paper, we consider a non-shortest-path routing approach, called equal preference multipath （EPMP） based on ordered semi group theory, which can generate more paths than ECMP. In EPMP routing, all the paths with different traditionally-defined costs, such as hops, bandwidth, etc., can be determined equally now and thus they become equal candidate paths. By the comparative tests with ECMP, EPMP routing not only generates more paths, provides 15% higher bisection bandwidth, but also identifies bottleneck links in a hierarchical network when different traffic patterns are applied EPMP is more flexible in controlling the number and length of multipath generation. Simulation results indicate the effectiveness of the proposed algorithm. It is a good reference for non-blocking running of big datacenter networks.展开更多
非侵入式负荷监测对开展节能工作,提供负荷调峰信息以及建设智能电网具有重要的现实意义。针对目前非侵入式负荷监测和分解方法在低频采样条件下识别准确度低的问题,提出了一种考虑状态概率因子和状态修正的非侵入式负荷分解方法。首先...非侵入式负荷监测对开展节能工作,提供负荷调峰信息以及建设智能电网具有重要的现实意义。针对目前非侵入式负荷监测和分解方法在低频采样条件下识别准确度低的问题,提出了一种考虑状态概率因子和状态修正的非侵入式负荷分解方法。首先,对预先获取的电力数据进行meanshift聚类,构建负荷不同状态下的功率模板;然后,在传统功率特征的基础上,将状态概率因子(state probability factor,SPF)作为新特征引入目标函数,通过改进的多特征遗传优化迭代实现负荷分解;最终对分解结果进行异常状态修正。在AMPds数据集上对算法性能进行了评估,仿真结果显示上述方法可有效提高负荷分解的准确性。展开更多
基金supported by the National Natural Science Foundation of China(61363047)the Open Research Fund of Guangdong Key Laboratory of Big Data Analysis and Processing(2017007)the Foshan Science and Technology Innovation Project(2016AG100792)
文摘Layer 2 network technology is extending beyond its traditional local area implementation and finding wider acceptance in provider＇s metropolitan area networks and large-scale cloud data center networks. This is mainly due to its plug-and-play capability and native mobility support. Many efforts have been put to increase the bisection bandwidth in layer 2 network, which has been constrained by the spanning tree protocol （STP） that layer 2 network uses for preventing looping. The recent trend is to incorporate layer 3＇s routing approach into layer 2 network so that multiple paths can be used for forwarding traffic between any source-destination （S-D） node pair. Equal cost multipath （ECMP） is one such example. However, ECMP may still be limited in generating multiple paths due to its shortest path （lowest cost） requirement. In this paper, we consider a non-shortest-path routing approach, called equal preference multipath （EPMP） based on ordered semi group theory, which can generate more paths than ECMP. In EPMP routing, all the paths with different traditionally-defined costs, such as hops, bandwidth, etc., can be determined equally now and thus they become equal candidate paths. By the comparative tests with ECMP, EPMP routing not only generates more paths, provides 15% higher bisection bandwidth, but also identifies bottleneck links in a hierarchical network when different traffic patterns are applied EPMP is more flexible in controlling the number and length of multipath generation. Simulation results indicate the effectiveness of the proposed algorithm. It is a good reference for non-blocking running of big datacenter networks.
文摘非侵入式负荷监测对开展节能工作,提供负荷调峰信息以及建设智能电网具有重要的现实意义。针对目前非侵入式负荷监测和分解方法在低频采样条件下识别准确度低的问题,提出了一种考虑状态概率因子和状态修正的非侵入式负荷分解方法。首先,对预先获取的电力数据进行meanshift聚类,构建负荷不同状态下的功率模板;然后,在传统功率特征的基础上,将状态概率因子(state probability factor,SPF)作为新特征引入目标函数,通过改进的多特征遗传优化迭代实现负荷分解;最终对分解结果进行异常状态修正。在AMPds数据集上对算法性能进行了评估,仿真结果显示上述方法可有效提高负荷分解的准确性。
