highlights and lowlights of the daily business of network engineers

Purpose

This note summarizes the main points about iMaster NCE-CampusInsight:

• Network Health View
• User Experience
• Application Experience View
• Data collection layer and analytics pipeline
• comparison between classic SNMP and telemetry streaming

Basic Idea of CampusInsight

• iMaster NCE-CampusInsight is the analyzer/analytics component in the CloudCampus environment.
• Its goal is to make operational data, user experience, and application experience visible and analyzable.
• According to Huawei, the platform uses big data and AI-driven analytics to detect network problems faster and improve user experience.
• In the dedicated CampusInsight product documentation, CampusInsight is explicitly positioned as an intelligent O&M platform that extends classic rule-based monitoring by using O&M data for intelligent troubleshooting.

Digital Map / One-Map as the Central View

• The Digital Map is the digital twin of the campus network.
• It collects large amounts of real-time data and mirrors the physical network in a digital view.
• This makes network status, user experience, and application experience visible.

Huawei highlights three especially important views:

1) Network View / Network Health View

• Displays device information, topology information, and basic service information per site.
• Shows metric trends for a site in order to quickly identify the network health status.
• Allows drill-down for root cause analysis when abnormalities occur.

Typical metrics:

• Wireless Device Sending Traffic
• Wireless Device Receiving Traffic
• Signal Strength
• Number of Users
• Interference Rate
• Channel Usage
• Energy Consumption
• Personnel Detection

In practice, this means the health view is the operational overview used to quickly see whether a site behaves normally or whether performance or RF issues exist.

Function highlights:

• KPI and trend view per site instead of isolated values only
• drill-down from the GIS/site level into detailed root causes
• fast detection of RF, traffic, and capacity issues
• combination of metrics, time axis, and location context

Typical operational strengths:

• early detection of interference or channel usage problems
• visibility into whether too many users or weak signal strength are degrading experience
• good entry point for root cause analysis before switching to deeper detail pages

Additional highlights from the dedicated CampusInsight documentation:

• clear separation between Issue Analysis, Network Metric Analysis, and Energy Consumption Analysis
• network issues are correlated based on metrics and logs
• M-LAG risks can be displayed explicitly
• M-LAG relationships and related issues can be shown in the view
• energy consumption analysis can recommend energy-saving periods and identify devices suitable for energy saving

License and prerequisite notes:

• Issue Analysis and Network Metric Analysis require the Basic Package
• Energy Consumption Analysis additionally requires the related value-added package
• devices must report logs and performance metrics through HTTP/2, UDP, or Telemetry

2) User Experience View

• Displays the clients and applications used by users.
• Detects VIP user experience in real time.
• Analyzes the causes of poor QoE based on experience scores.
• Supports playback of the user journey across time and space.

Important user journey values:

• Access Latency
• Bandwidth
• Packet Loss

Note:

• According to the documentation, experience scoring applies only to wireless authenticated users, not to wired-auth users.

Function highlights:

• user journey based on the floor plan or space view
• timeline playback of the journey
• experience analysis model with root cause analysis by dimension
• focus on individual users instead of only global averages

What you can actually see:

• user access details
• experience exception analysis
• logout information
• trends for each selected experience dimension
• journey playback for a specific point in time or period

Practical value:

• very useful for helpdesk and VIP cases
• shows not only that a problem exists, but also when and where it occurred
• helps trace user complaints down to the client, AP, and location level

Additional highlights from the dedicated CampusInsight documentation:

• Client Journey replays the network path of a client per location and point in time
• Poor-QoE correlation analysis links experience degradation to the most relevant network metrics
• VIP user events help proactively monitor VIP users
• the user list includes items such as username, MAC, access type, user type, and last experience score

License and prerequisite notes:

• the base function belongs to the Basic Package
• application information in the user context additionally requires the application-analysis value-added package
• APs must be assigned to sites according to the network plan

3) Application Experience View

• Displays application traffic and quality information.
• Detects the experience of key applications in real time.
• Shows the traffic path of an application per user.
• Locates faulty nodes that degrade application experience.
• Provides handling suggestions.

Typical visible items:

• number of applications
• assured services
• service flows
• traffic
• quality / health index
• analysis of poor-QoE flows

With iPCA 2.0, fault localization for flows can become even more accurate.

Function highlights:

• real-time visibility for critical applications
• traffic and quality analysis per application
• visibility into affected sites and individual flows
• fault demarcation for abnormal flows
• linkage with automatically orchestrated assurance policies

Huawei especially highlights two subfunctions:

Key Application Assurance

• makes application experience visible
• supports traffic analysis and network path display
• adaptively orchestrates fault demarcation and service assurance policies
• can use prioritization or collaborative office optimization to improve the experience of important applications

Typical technical building blocks:

• iPCA for in-band flow measurement
• iflow for single-ended TCP detection
• Dial Test for proactive / simulated quality detection
• MQC for protocol tracing
• Preferential forwarding for DSCP-based prioritization

Key Service Assurance

• creates assurance objects for specific clients and time periods
• monitors individual service sessions in a targeted way, for example a conference
• shows poor-QoE indicators such as delay and packet loss per flow

Practical value:

• very useful for business-critical apps such as meetings, collaboration, or cloud applications
• links the application view with the network path and the fault location
• significantly speeds up troubleshooting because not only traffic, but also quality and fault location become visible

Additional highlights from the dedicated CampusInsight documentation:

• CampusInsight can identify more than 1000 mainstream applications, with Teams and Zoom explicitly mentioned
• AQM is described as a single-end measurement technology that dynamically adjusts the sending frequency based on network quality and simulates application behavior
• the Application Experience Map shows topology, total number of applications, total traffic, and top-N applications
• the detail pages display packet loss, delay, abnormal/service flows, and involved sites
• Intelligent Dialing Test can provide additional trend data for loss and latency

License and prerequisite notes:

• the function requires the Application Analysis value-added package
• devices must report traffic statistics, poor-QoE data, and packet-loss/delay data

Data Collection Layer and Analytics Pipeline

Basic principle

• Devices deliver data to iMaster NCE-Campus and CampusInsight.
• The data is parsed, forwarded, stored, and analyzed.
• It is then visualized as trends, KPI views, user journeys, or application experience.

Main channels

Huawei describes three main channels for the Digital Map:

• Management channel
  • for basic configuration and device information
• Performance channel
  • for LLDP topology, real-time performance data, and application/client data
• Authentication channel
  • for information about authenticated online users and clients

Typical data sources

• performance metrics from devices
• application data
• client monitoring data
• authentication information
• LLDP topology information
• syslog-related data for certain historical reports
• in-band flow measurement / IFIT data

Big-data / analytics stack

The Huawei materials suggest this simplified pipeline:

• Devices report performance data through HTTP/2 or Telemetry.
• The NE driver parses the data.
• The data is then forwarded to the big data analytics platform.
• There, the data is aggregated, calculated, and stored by time and space.

Mentioned components:

• Kafka
  • distributed message queue / transport bus
• HDFS
  • file management / distributed storage
• HBase
  • storage for reported performance data
• Spark
  • distributed big-data computing and statistical analysis
• Druid
  • analytics/storage component for data analysis and storage
• AI engine
  • framework for AI/ML-based analysis and typical algorithm libraries

Important:

• In the dedicated CampusInsight documentation, Druid is now explicitly mentioned.
• This makes Kafka, Spark, HDFS, Druid, Telemetry, Syslog, and AI engine confirmed architecture points for CampusInsight itself.

Data Collection Model in CampusInsight

The dedicated CampusInsight documentation clearly separates the data model into three layers:

• SNMP
  • for device addition / device management
• Telemetry
  • for metric data collection
  • according to the documentation, metric data is reported through Telemetry and the transmitted packets are encoded with ProtoBuf
• Syslog
  • for log data collection

This refines the comparison significantly:

• SNMP is mainly important in CampusInsight for onboarding and managing devices
• Telemetry is the actual modern channel for performance and metric data
• Syslog provides the log and event perspective for issue analysis

AI Engine / ML

• Huawei generally refers to big data, AI algorithms, and advanced analytics.
• In the product materials, AI engines, model training, and inference also appear.
• In practice for CampusInsight, this means the platform does not only collect data, but also evaluates and correlates it to expose root causes and experience problems.

Telemetry Streaming vs. Classic SNMP

Core difference

Classic SNMP

• Typically pull-based.
• The management system actively queries data from the device.
• Usually done through polling at fixed intervals.

Telemetry Streaming

• Typically push-based.
• The device actively sends data to the controller or analyzer.
• Data can be streamed continuously or near events.

Advantages of SNMP

• very widely used and well known across vendors
• often sufficient for basic monitoring tasks
• well suited for classic brownfield networks
• simple for basic values such as interface status, CPU, memory, and traffic counters

Disadvantages of SNMP

• polling creates regular queries and therefore overhead
• values are often only visible as interval-based samples
• short spikes or micro-events can be missed between polling intervals
• limited for highly granular real-time analysis
• scales less efficiently when heavy polling is used in large networks

Advantages of Telemetry Streaming

• the push model reduces the need for constant polling
• better suited for near-real-time data
• allows finer granularity
• better for analytics, trend detection, and root cause analysis
• especially useful for user experience and application experience
• better aligned with big-data and AI workflows

Disadvantages of Telemetry Streaming

• newer and not equally supported on all devices/models
• often more vendor- or platform-specific
• places higher demands on backend, storage, and analytics
• with many streams, data volume and processing capacity must be planned carefully

Practical takeaway

• SNMP = pull, simple, classic, but coarser and slower.
• Telemetry = push, more modern, more detailed, and better for real-time analytics.

Meaning for CampusInsight

• CampusInsight depends heavily on continuous and detailed data collection.
• Therefore, streaming telemetry is better suited than pure SNMP polling for modern health, user, and application analytics.
• SNMP remains useful for classic device integration and basic monitoring, but is less strong for experience-oriented analytics.
• In the specific CampusInsight documentation, this role split is explicit: SNMP for device addition, Telemetry for metrics, and Syslog for logs.

Functional Comparison of the Three Main Views

Network Health View

• mainly answers: Is the site healthy?
• focus on metrics, trends, RF, load, and site status
• typical starting point for NOC and O&M staff

User Experience View

• mainly answers: How does one specific user experience the network?
• focus on journey, access latency, bandwidth, packet loss, and experience anomalies
• especially good for individual-case and VIP analysis

Application Experience View

• mainly answers: How well is a specific application performing?
• focus on app traffic, quality, health index, flow path, and fault demarcation
• especially good for business-critical applications and service assurance

Key Takeaways

• CampusInsight is the analyzer layer for network, user, and application experience.
• The most important interface is the Digital Map with the network, user, and application views.
• The data foundation comes from device performance, client, authentication, and application data.
• In the backend architecture, Kafka, HDFS, HBase, and Spark are especially relevant.
• For modern analytics, telemetry streaming is usually superior to classic SNMP polling.

Sources

• 001_Docs/IMasterNCE/resources/en-us_topic_0000001751338252.html
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• 001_Docs/IMasterNCE/iMaster NCE-Campus V300R025C00 Product Overview.pptx