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iMaster NCE-CampusInsight

Huawei iMaster NCE-CampusInsight, a network intelligent analysis platform, overrides traditional resource monitoring. It collects network data in real time using the Telemetry technology, learns network behavior and identify fault patterns based on Big Data analysis and machine learning algorithms, helping O&M personnel proactively discover 85% network issues and build excellent network service experience.

Huawei

Features


Feature Description
Multi-dimensional network status visualization and client experience awareness throughout the journey
  • Allows users to view multi-dimensional data statistics views based on different levels and regions.

  • Allows users to view issues about network access, network congestion, device status, and error packets from the perspective of buildings.

  • Allows users to be searched from the perspective of buildings and information about buildings that users pass by in a period of time is displayed.

  • Allows users to import topology views and plan AP locations to intuitively view fault location distribution.

  • Allows users to view the radio heat map by AP location.

  • Allows users to import network planning data and compare the planning data with the actual network running data to display the differences between them.

  • Allows users to view the full-journey experience, including who, when, which AP to connect, experience, and issues.

  • Supports device profiles and allows users 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 authentication, Portal authentication, and MAC address authentication), and DHCP phases. The protocol information includes the interaction result and time consumed. If the interaction fails, the failure causes are also displayed.

  • Correlatively analyzes poor-experience clients. When experience of a client 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 analysis and machine learning 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 client prefers 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. You can view the dynamic baseline comparison between the local region and other regions for each indicator. CampusInsight provides associated root cause indicators, enabling in-depth root cause analysis. It allows you to select different time or areas for comparison and analysis and sends network health reports to the administrator in real time or periodically by emails.
Intelligent demarcation and root cause analysis of network issues
  • Supports the issue distribution view, allowing users to view the number of issues on different devices and the number of affected clients. This helps users quickly focus on the devices where and time range when many issues occur.

  • Supports issue impact analysis views, allowing users 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.
Feature Description
Audio and video service analysis
  • Detects the setup and teardown of audio and video sessions in real time, automatically analyzes the quality of the audio and video streams using the eMDI and awareness technologies, and displays session information, uplink and downlink poor-quality durations, and MOS values of the initiator and responder of audio and video calls.
    The packet path trace function can visualize the traffic path, including the devices at both ends, intermediate devices, and ports that packets pass through. In addition, fault mode analysis is performed on the path to quickly and intelligently locate the faulty device or port. Through poor-quality correlation analysis, root causes can be rapidly identified and rectification suggestions can be provided to effectively improve fault locating efficiency.
    Constraints:
  • This feature is supported only for audio and video applications that use non-encrypted SIP signaling and are carried by the RTP in the IPv4 scenario, for example, HUAWEI Video Phone 8950.
  • Audio and video service analysis is supported for switches, and audio service analysis is supported for APs. For details about the models, see the CampusInsight Specifications List.
  • Only switches of V200R013C00 or a later version and APs of V200R010C00 or a later version are supported.
  • Path analysis is supported only on cloud devices.
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.

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