Intelligent Operations (AIOps)

Based on operations data and intelligent analysis, achieve intelligent operations from passive response to active prevention. Through technologies such as machine learning and big data analysis, intelligently analyze and process massive operations data, achieving intelligent capabilities such as anomaly detection, root cause analysis, capacity prediction, and fault prediction, improving operations efficiency and service quality.

Core Capabilities

Multi-dimensional Data Unified Collection

Infrastructure monitoring data (CPU, memory, disk, network, etc.)
Application performance data (response time, throughput, error rate, etc.)
Log data (application logs, system logs, audit logs, etc.)
Business data (transaction volume, user count, business metrics, etc.)
Network traffic data

Alert Noise Reduction & Correlation Analysis

Alert aggregation and noise reduction, reducing alert storms
Alert correlation analysis, identifying related alerts
Intelligent alert priority sorting
Alert root cause analysis
Alert prediction and prevention

Anomaly Detection

Machine learning-based anomaly detection
Time series anomaly detection
Multi-dimensional metric anomaly detection
Anomaly pattern recognition
Anomaly trend prediction

Root Cause Analysis

Intelligent root cause localization
Fault propagation path analysis
Impact scope assessment
Correlation analysis
Historical case matching

Capacity Prediction & Planning

Resource usage trend prediction
Capacity demand prediction
Expansion timing recommendations
Cost optimization recommendations
Capacity planning support

Intelligent Operations Scenarios

Fault prediction and prevention
Automated fault handling
Intelligent inspection
Performance optimization recommendations
Security threat detection

Technical Features

Big Data Processing Capability

Support collection, storage, processing, and analysis of massive operations data, meeting the operations needs of large-scale IT environments

Machine Learning Algorithms

Adopt various machine learning algorithms, including time series analysis, clustering analysis, classification algorithms, etc., improving analysis accuracy

Real-time & Offline Analysis

Support real-time data stream analysis and offline batch processing analysis, meeting different scenario analysis needs

Extensible AI Capabilities

Support AI model extension and customization, can train and optimize models based on actual enterprise scenarios

Solution Value

From Passive to Active

Transform from passive fault response to active prevention and prediction, reducing fault occurrence rate

Improve Operations Efficiency

Quickly locate problems through intelligent analysis, shortening fault handling time

Reduce Operations Costs

Reduce alert storms, optimize resource allocation, lower operations labor costs

Ensure Business Stability

Ensure stable operation of business systems through prediction and prevention