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iMaster NCE-CampusInsight

Delivering a superb network experience with big data, ML, and real-time visualization.

iMaster NCE-CampusInsight

iMaster NCE-CampusInsight

Huawei iMaster Network Cloud Engine (NCE)-CampusInsight is an intelligent network analysis platform that has totally transformed traditional network resource monitoring. The platform collects network data in real-time through telemetry, learns network behavior, and identifies fault patterns based on big data analytics and Machine Learning (ML) algorithms. This transforms Operations and Maintenance (O&M) — making it predictive and proactive — identifying 85% of faults before they occur, to elevate the overall user experience to new levels.

Kategorie:

Real-Time Experience Visibility

Fault Location Within Minutes

Intelligent Network Optimization

Features

Feature

Description

Multi-dimensional network status visualization and client experience awareness throughout the journey

Accurately identifies more than 1000 mainstream applications through application identification, including Zoom, Microsoft Teams, DingTalk, and WeChat.

Identifies administrator-defined applications.

Analyzes the network-wide application traffic and number of users based on applications and displays the application usage of each user on the client journey page.

Collects statistics on application traffic from dimensions of interfaces, devices, and hosts.

   Constraints:

Non-encrypted RTP and TCP applications are supported in IPv4 scenarios.

Switches and ACs (excluding native ACs) of V200R020C10 and later versions are supported. In addition, this version supports only AC tunnel forwarding scenarios, and application identification or NetStream must be enabled on devices. For details about the specifications, refer to the CampusInsight specification query tool.

Application quality insights and poor-QoE analysis

Uses exclusive eMDI technology and AI algorithms to detect the quality of mainstream applications in real-time and identify poor-QoE applications.

Uses iPCA 2.0 to implement network quality measurement based on actual service flows and display the path of service flows in real-time, including the devices at both ends and the devices and ports through which each service flow passes; Performs fault mode analysis over the paths to intelligently locate the faulty devices or ports in a short period of time.

   Constraints:

Non-encrypted RTP and TCP applications are supported in IPv4 scenarios.

Switches, ACs (excluding native ACs), and APs of V200R020C10 and later versions are supported, and eMDI and iPCA 2.0 must be enabled on devices. For details about the specifications, refer to the CampusInsight specification query tool.

Feature

Description

Intelligent radio calibration

Real-time simulation feedback: CampusInsight evaluates wireless network channel conflicts based on the neighbor and radio information of devices on each floor and provides calibration suggestions. (Simulation feedback is not supported for regions for which no floor is planned.)

Big data-powered predictive calibration and post-calibration gain display: CampusInsight identifies highly loaded APs and edge APs through AI algorithms based on historical big data, drives devices to perform differentiated radio calibration based on the big data analytics results, and intuitively displays all calibration records and calibration gains. The records include both intelligent radio calibration and local calibration records.

Feature

Description

Application traffic analysis

Accurately identifies more than 1000 mainstream applications through application identification, including Zoom, Microsoft Teams, DingTalk, and WeChat.

Identifies administrator-defined applications.

Analyzes the network-wide application traffic and number of users based on applications and displays the application usage of each user on the client journey page.

Collects statistics on application traffic from dimensions of interfaces, devices, and hosts.

   Constraints:

Non-encrypted RTP and TCP applications are supported in IPv4 scenarios.

Switches and ACs (excluding native ACs) of V200R020C10 and later versions are supported. In addition, this version supports only AC tunnel forwarding scenarios, and application identification or NetStream must be enabled on devices. For details about the specifications, refer to the CampusInsight specification query tool.

Application quality insights and poor-QoE analysis

Uses exclusive eMDI technology and AI algorithms to detect the quality of mainstream applications in real-time and identify poor-QoE applications.

Uses iPCA 2.0 to implement network quality measurement based on actual service flows and display the path of service flows in real-time, including the devices at both ends and the devices and ports through which each service flow passes; Performs fault mode analysis over the paths to intelligently locate the faulty devices or ports in a short period of time.

   Constraints:

Non-encrypted RTP and TCP applications are supported in IPv4 scenarios.

Switches, ACs (excluding native ACs), and APs of V200R020C10 and later versions are supported, and eMDI and iPCA 2.0 must be enabled on devices. For details about the specifications, refer to the CampusInsight specification query tool.

Edge intelligence

Supports full-packet analysis on specified TCP/UDP service flows and proactively identifies information such as packet loss and delay during flow interaction.
Supports network quality analysis of multicast services, including packet loss and delay measurement, and performs quick fault locating.

Feature

Description

RSSI-based wireless positioning

Displays the client distribution heat map based on the specified time period.

Allows users to view locations of all terminals with Wi-Fi enabled, location of a single user, and available paths within a specified period.

Anonymizes terminal MAC addresses.

Locates Wi-Fi and non-Wi-Fi interference sources, including identifying and displaying the locations of interference sources.

Supports Wi-Fi user location analysis, including new and old user detection statistics, frequency distribution, detection duration distribution, user capture rate, and associated user ratio.

   Constraints:

Only Wi-Fi RSSI-based network positioning is supported.

Only indoor wireless positioning is supported.

Positioning accuracy: < 10 m, 60% accuracy (independent radio scanning), 50% accuracy (non-independent radio scanning); Positioning delay: < 20s

Wireless positioning data can be stored for a maximum of seven days.

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