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CampusInsight – Campus Network Analyzer

The CampusInsight is a campus network analyzer developed by Huawei. It uses big data analytics technologies to provide excellent network service experience by analyzing data of each user at each moment.
The CampusInsight uses the Telemetry technology to dynamically capture fault data in seconds, perceiving the full-journey experience for each user. It proactively identifies four types of network problems, helping administrators quickly demarcate faults and eliminate risks and build excellent network service experience.


Features Descriptions
Multi-dimensional network status visualization and user experience awareness throughout the journey
  • Allows users to view multi-dimensional data statistics views based on different levels and regions.
  • Allows users to import topology views and plan AP locations to intuitively view fault location distribution.
  • Allows users to view the full-journey experience, including who, when, which AP to connect, experience, and issues.
  • Allows users to draw device profiles for APs and view the AP health status.
  • Traces the network access process of a client, including detailed protocol information at the association, authentication (supporting only the Dot1x authentication mode), 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 four types of network issues based on Big Data analysis and machine learning algorithms: connectivity, air interface performance, roaming, and device issues. The issues include authentication failure, weak-signal coverage, dual band capable client prefers 2.4G, and PoE power supply failure.
  • Supports learning and dynamic baseline drawing on network behavior to predict the change trend and detect exceptions through data comparison.
Intelligent demarcation and root cause analysis of network issues
  • Supports issue distribution views, allowing users to view number of issues on different devices and 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. 

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