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  • iMaster NCE-CampusInsight

    iMaster NCE-CampusInsight

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

  • Overview
  • Features
  • Specification
  • Resources
  • Support
iMaster NCE-CampusInsight

iMaster NCE-CampusInsight

Huawei iMaster NCE-CampusInsight (CampusInsight for short), a Huawei campus network analyzer, changes the traditional monitoring mode that focuses on resource status. It collects network data in real time through Telemetry, identifies network behaviors and fault patterns based on big data analytics and intelligent algorithms. This helps O&M personnel proactively detect 85% of potential network issues and build E2E high-quality Wi-Fi->LAN->WAN network experience.

Network health view, improving network performance by 58%

Network health view, improving network performance by 58%

• Multi-dimensional network health evaluation: Periodically push reports for LAN-WAN network health analysis.
• Minute-level fault demarcation and locating: Proactively identify 200+ issues of eight types and predict potential risks in advance.
• Automatic and intelligent calibration: Perform predictive calibration without manual intervention.
• Energy saving: Intelligently recommend energy-saving policies, reducing the energy consumption by 30%.
• Spatial Perception:CSI intelligently detects intrusions for camera-free space protection.

Application experience view, shortening fault locating time by 95%

Application experience view, shortening fault locating time by 95%

• Intelligent identification and awareness of applications: Intelligently identify 6,000+ mainstream applications and proactively detect the application quality.
• Minutes-level application fault locating: Use the exclusive IFIT technology to demarcate and locate faults from Wi-Fi, LAN, to WAN in an E2E manner in minutes.

User experience view, reducing user complaints by 90%

User experience view, reducing user complaints by 90%

• VIP user experience assurance: Intuitively display VIP user experience scores and proactively report alarms for VIP exceptions.
• Full-journey user experience visualization: Visualize user experience at each moment, enabling fault backtracking.
• Protocol-level trace of access issues: Provide protocol-level trace of user access issues, root cause analysis, and rectification suggestions.
• RSSI-based Wireless Positioning:Refined O&M Based on Terminal Locations.

NetMaster, 80% of typical wireless network faults are automatically resolved

NetMaster, 80% of typical wireless network faults are automatically resolved

• Network O&M Copilot: Interoperate with communication professional model and implement interactive O&M in natural languages.
• Wi-Fi Optimization Agent: Auto-sensing, auto-analysis, automated decision-making, and auto-execution 7 categories and 30 sub-categories of wireless faults are automatically closed.

Specifications

Feature Description
Multi-dimensional network
status visualization
and client experience
awareness throughout the journey
  • Allows administrators to view multi-dimensional data statistics based on different levels and regions.
  • Allows administrators to view issues about network access, network congestion, device status, and error packets from the perspective of buildings.
  • Network users can be searched based on buildings, displaying information about buildings that users passed by in a specified period of time.
  • Allows administrators to import topology views and plan AP locations to intuitively view fault location distribution.
  • Allows administrators to view the radio heat map by AP location.
  • Allows administrators to import network planning data to be compared with the actual network data, displaying the differences between them.
  • Displays spectrum analysis results based on APs, including full-channel status monitoring, Wi-Fi interference sources, and non-Wi-Fi interference sources.
  • Generates dialing test reports for multi-vendor network comparison in real-time and allows administrators to intuitively learn the Wi-Fi network experience through dialing tests on apps.
  • Allows administrators to view the full-journey experience, including who, when, and which AP to connect, experience, and issues.
  • Supports device profiles and allows administrators to view the health status of switches and APs.
  • Traces the network access process of a client, including detailed protocol information at the association, authentication (supporting 802.1X, portal, and MAC address), and DHCP phases. The protocol information includes the interaction result and time used. If the interaction fails, the failure causes are also displayed.
  • Correlatively analyzes poor-experience network users. When the experience of a user deteriorates, CampusInsight identifies quantified correlation KPIs based on the KPI similarity analysis algorithm, which effectively improves the accuracy of root cause identification.
Automatic identification
and proactive prediction
of network issues
  • Supports automatic identification of common network issues based on big data analytics and ML algorithms: connectivity, air interface performance, roaming, device environment, device capacity, network performance, and network status issues. The issues include authentication failure, weak-signal coverage, dual band-capable clients prioritizing 2.4G, and network congestion.
  • Supports learning and dynamic baseline drawing on network behavior to predict the change trend and detect exceptions through data comparison.
  • Intelligently analyzes data reported at the second level and establishes a network health evaluation system from multiple dimensions. CampusInsight evaluates and ranks regions based on indicator weights, driving continuous improvement from poor experience to good experience and gradually improving the network quality. The dynamic baseline comparison between the local region and other regions for each indicator can be viewed. CampusInsight provides associated root cause indicators, enabling in-depth root cause analysis. Different time or areas can be selected for comparison and analysis and network health reports are sent to administrators in real-time or periodically by email.
Intelligent demarcation
and root cause analysis
of network issues
  • Issue distribution view allows administrators to view the number of issues on different devices and the number of affected clients. This helps administrators quickly focus on the affected devices and the time range when many issues occur.
  • Issue impact analysis view allows administrators to filter impact factors from multiple dimensions and drill down layer by layer to quickly locate the issue root cause.
  • Analyzes the root causes and provides rectification suggestions to assist quick issue closure.
Open northbound APIs,
providing various data
for intelligent analysis
  • Supports different secondary development capabilities based on data characteristics. Three types of interfaces can open the raw data and analyzed data to third-party systems, including network O&M and IT service systems, thereby offering richer intelligent analysis data.

(1) RESTful NBI: Opens resource data (device, interface, link, and board data), health data (health issue and health evaluation data) and terminal session data to external systems.

(2) SNMP NBI: Reports alarm data to a third-party system through SNMP.

(3) Kafka NBI: Consumes data collected by CampusInsight using telemetry through the consumer API provided by Kafka.

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.
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.
Feature Description
Network Energy Consumption Analysis
  • Based on the historical network data reported by devices, the system calculates the devices that need to be energy-saving and the time periods for energy saving. Users can refer to the commands to configure and issue commands to control the power-on and power-off of APs through the controller.
  • Energy consumption data and trends are displayed by device. The energy consumption of switches, ACs, and APs can be visualized. Users can select top 5 data or all data.
  • Energy consumption data and trends are displayed by site and region. Users can select top 5 data or all data.
  • Supports selecting comparison time periods to view energy.
Feature Description
NetwSpatialork Spatial Perception
  • Based on the historical data of people presence reported by devices, the system can collect statistics on whether there are people presence or not in the planned floor areas.
  • The system can display the presence or absence of people and the trend at the site, area, and floor levels based on the planned area.
  • The system supports setting intrusion detection high-entry configurations based on the planned area and pushing intrusion alarms when people intrude.
Feature Description
The Network Operation Copilot value-added package
  • Integrate with digital maps to quickly resolve network issues. Through conversational operations and maintenance, perform intelligent O&M for network health checks, user troubleshooting, application quality queries, and device monitoring metrics.
Feature Description
Wi-Fi Optimization Agent
  • Supports creating optimization targets for site areas, selecting optimization strategies, and setting monitoring thresholds.
  • Displays created target tasks and details on task achievement. The overall service quality status is shown as the target status, with compliance conditions displayed separately across four dimensions: signal strength, downlink bandwidth, roaming success rate, and interference rate. The cockpit automatically monitors the timeline to show events during different time periods.

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