Huawei CloudCampus CampusInsight Analytics Engine New!
With the development of network technologies, wireless networks have become an indispensable part of enterprise operation. From mobile office to HD video conference to guest reception, high-quality wireless network is closely related to employee efficiency and customer satisfaction. However, as the enterprise scale expands, wireless terminals and application scenario complexity increase explosively, which makes the network environment that is difficult to measure become more complex than ever. Traditional O&M methods cannot proactively detect issues, resulting in low efficiency in fault rectification and bringing heavy burden to IT O&M personnel.
Huawei 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 Wi-Fi service experience.
Built based on the FusionInsight platform, CampusInsight uses the Streaming Telemetry technology to collect device data in quasi-real time, analyzes features and calculates baselines based on machine learning algorithms, automatically identifies network faults, and displays analysis results on a graphical user interface (GUI).
1. Full-Journey Experience Visualization for Each User at Each Moment
• Each Moment: Dynamically capture network KPI data in seconds using the Telemetry technology, realizing fault tracing.
• Each User: Collect data from multiple dimensions, display network profiles of all users in real time, and visualize network experience (who, when, which AP to connect, experience, and issue) throughout the journey.
2. Automatic Identification and Proactive Prediction of Network Issues
• Use Big Data and AI technologies to automatically identify connectivity, air interface performance, roaming, and device issues, improving the identification rate of potential issues by 85%.
• Learn historical data through machine learning to dynamically generate a baseline, and compare and analyze the baseline with real-time data to predict possible faults.
3. Intelligent Demarcation and Root Cause Analysis of Network Issues
• Intelligently identify fault patterns and impact scopes based on the network O&M expert system and various AI algorithms, helping administrators demarcate faults.
• Analyze possible fault causes based on the Big Data platform and provide rectification suggestions.
|Categories||Networking > Control & Management Software|
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