公开(公告)号：US20190190833A1

公开(公告)日：2019-06-20

申请号：US16285796

申请日：2019-02-26

Applicant: Huawei Technologies Co., Ltd.

Inventor： Trimponias Georgios ,  Zhitang Chen ,  Hong Xu

IPC: H04L12/803 ,  H04L12/721 ,  H04L29/12 ,  H04L12/947

CPC classification number: H04L47/125 ,  H04L43/026 ,  H04L45/123 ,  H04L45/24 ,  H04L45/38 ,  H04L47/11 ,  H04L47/122 ,  H04L47/2483 ,  H04L49/25 ,  H04L61/2007

Abstract: A data packet forwarding method and apparatus, where the method includes collecting, by a source switch according to a preset sampling period, congestion extents of d sampling paths in n paths, storing congestion extent indication information of each sampling path, where d is less than n, selecting, according to the congestion extent indication information, a first target sampling path with a smallest congestion extent at a first time point, forwarding a first data packet using the first target sampling path, storing an identifier of a first data flowcell to which the first data packet belongs forwarding a second data packet using the first target sampling path when an identifier of a second data flowcell to which the second data packet belongs is the same as the identifier of the first data flowcell.

公开(公告)号：US10686672B2

公开(公告)日：2020-06-16

申请号：US16226577

申请日：2018-12-19

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Trimponias Georgios ,  Zhitang Chen ,  Yanhui Geng

IPC: G06F15/16 ,  H04L12/24 ,  G06N3/04 ,  G06N3/08 ,  H04L12/715 ,  H04L12/751 ,  H04L12/26 ,  H04L12/761

Abstract: Embodiments of this application provide a method for generating a routing control action in a software-defined network and a related device, to provide optimum control actions for the SDN. The method includes: obtaining a current network state parameter of the SDN; determining a Q function of the SDN based on the current network state parameter of the SDN and a deep neural network model, where the deep neural network model is determined based on a current topology structure of the SDN; and determining a routing control action for the SDN based on the Q function and a link state parameter of each link in the SDN. In the technical solution, the deep neural network model is combined with a Q-learning algorithm of reinforcement learning, and optimum control actions can be determined.

公开(公告)号：US20190222499A1

公开(公告)日：2019-07-18

申请号：US16362135

申请日：2019-03-22

Applicant: Huawei Technologies Co., Ltd.

Inventor： Zhitang Chen ,  Yanhui Geng ,  Trimponias Georgios

IPC: H04L12/26

CPC classification number: H04L43/0888 ,  G06K9/62 ,  H04L29/08

Abstract: A network data flow classification method related to artificial intelligence includes collecting an information set, including a plurality of pieces of dimension information, of a to-be-processed data flow, establishing a static behavior model and a dynamic behavior model of each piece of dimension information in the information set, where the static behavior model represents a value selection rule of the dimension information, and the dynamic behavior model represents a correlation relationship of the dimension information between two adjacent time moments, obtaining, using the static behavior model and the dynamic behavior model respectively, a static model distance and a dynamic model distance between the to-be-processed data flow and a data flow of each target application type, determining an application type of the to-be-processed data flow based on the static model distance and the dynamic model distance.