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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.



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.

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