公开(公告)号：US20250088466A1

公开(公告)日：2025-03-13

申请号：US18959827

申请日：2024-11-26

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yuechen LI ,  Huiying XU ,  Shengming CAI ,  Bin XIA ,  Hao SHI

IPC: H04L47/24 ,  H04L47/80

Abstract: A cloud resource recommendation method and a related device are provided. The cloud resource recommendation device obtains a requirement of a user on a cloud resource and obtains information about the cloud node, where the requirement of the user includes a quality of service requirement of the user on a routing path between a cloud node and an access device of the user, where the information includes quality of service information of a routing path between the cloud node and the access device of the user. The cloud resource recommendation device determines a cloud resource recommendation result based on the quality of service requirement and the quality of service information, where the cloud resource recommendation result includes a recommended cloud node, and quality of service information of at least one routing path between the recommended cloud node and the access device of the user meets the quality of service requirement.

公开(公告)号：US20230076178A1

公开(公告)日：2023-03-09

申请号：US18056450

申请日：2022-11-17

Applicant: Huawei Technologies Co., Ltd.

Inventor： Zied BEN HOUIDI ,  Hao SHI

IPC: G06N3/04 ,  H04L41/08

Abstract: The present disclosure relates to the field of computer networks. More specifically, a solution for machine learning-based classification of host identifiers in encrypted network traffic is provided. The classification can, in particular, include natural language processing capabilities. The present disclosure provides a network device for host identifier classification. The network device is configured to obtain a sequence of host identifiers, each host identifier corresponding to a flow of encrypted network traffic, apply an unsupervised learning technique to the sequence of host identifiers to learn a vector of a high-dimensional space for each host identifier in the sequence, obtain a labelled ground truth comprising labels corresponding to a host identifier, and apply a supervised learning technique to each vector, based on the labelled ground truth, to classify the corresponding host identifier.