5.2.5 Catalyst Center AI Network Analytics
| Item |
Details |
| Organization |
Abhavtech.com |
| Domain |
abhavtech.com |
| Version |
2.0 |
| Last Updated |
December 2025 |
1. AI Network Analytics Overview
1.1 Architecture
┌─────────────────────────────────────────────────────────────────────┐
│ Cisco AI Network Analytics Architecture │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Abhavtech Network Cisco Cloud │
│ ┌───────────────────┐ ┌───────────────────┐ │
│ │ Catalyst Center │ │ AI Analytics │ │
│ │ │ HTTPS/443 │ Cloud Platform │ │
│ │ ┌───────────────┐ │──Deidentified─▶│ │ │
│ │ │ Assurance │ │ Data │ ┌───────────────┐ │ │
│ │ │ Engine │ │ │ │ ML Models │ │ │
│ │ └───────────────┘ │◀──Insights────│ │ - Baselining │ │ │
│ │ │ │ │ - Anomaly Det │ │ │
│ │ ┌───────────────┐ │ │ │ - Prediction │ │ │
│ │ │ Network │ │ │ │ - Correlation │ │ │
│ │ │ Telemetry │ │ │ └───────────────┘ │ │
│ │ └───────────────┘ │ │ │ │
│ └───────────────────┘ └───────────────────┘ │
│ │
│ Data Flow: │
│ 1. Network events collected by Assurance │
│ 2. Data deidentified (privacy protection) │
│ 3. Sent to cloud via encrypted channel │
│ 4. ML models analyze patterns │
│ 5. Insights returned to Catalyst Center │
│ │
└─────────────────────────────────────────────────────────────────────┘
1.2 Key Capabilities
| Capability |
Description |
Benefit |
| AI-Driven Baselining |
ML learns normal network behavior |
Reduces false positives |
| Anomaly Detection |
Identifies deviations from baseline |
Proactive issue detection |
| Predictive Analytics |
Forecasts potential issues |
Preventive action |
| Machine Reasoning |
Automates root cause analysis |
Faster resolution |
| Comparative Analytics |
Compares against peer networks |
Industry benchmarking |
| AI-Enhanced RRM |
Optimizes wireless RF |
Better Wi-Fi experience |
2. Enabling AI Network Analytics
2.1 Prerequisites
Prerequisites:
Licensing:
- Catalyst Center: DNA Advantage or Premier
- ISE: Advantage (for AI Endpoint Analytics)
Connectivity:
- HTTPS (443) to Cisco AI Cloud
- Proxy supported if required
Catalyst_Center_Version:
- Minimum: 2.3.5.x
- Recommended: 2.3.7.x or later
2.2 Configuration Steps
Step 1: Enable Cloud Connection
System → Settings → Cisco AI Analytics
Cloud Connection:
☑ Enable Cisco AI Analytics
Data Sharing Consent:
☑ I agree to share anonymized network telemetry
☑ I understand data is deidentified
Proxy Configuration (if required):
Proxy Server: proxy.abhavtech.com
Port: 8080
Authentication: ☑ Required
Username: catalyst-proxy-svc
Click "Enable"
Step 2: Verify Connection
System → Settings → Cisco AI Analytics
Connection Status: ✅ Connected
Last Sync: 2 minutes ago
Data Points Sent (24h): 1,234,567
Insights Received (24h): 456
2.3 Feature Activation
Assurance → Settings → AI Analytics Features
Enable Features:
☑ AI-Driven Issue Detection
☑ Predictive Analytics
☑ Comparative Analytics
☑ Machine Reasoning Engine
☑ AI-Enhanced RRM (Wireless)
☑ Client Experience Insights
Click "Save"
3. AI-Driven Baselining
3.1 How Baselining Works
┌─────────────────────────────────────────────────────────────────────┐
│ AI Baselining Process │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Week 1-2: Learning Phase │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ Collect network metrics: │ │
│ │ • Device health (CPU, memory, temperature) │ │
│ │ • Interface utilization │ │
│ │ • Client onboarding times │ │
│ │ • RADIUS response times │ │
│ │ • Wireless RF metrics │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Week 3+: Baseline Established │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ ML models define: │ │
│ │ • Normal ranges for each metric │ │
│ │ • Time-of-day patterns │ │
│ │ • Day-of-week variations │ │
│ │ • Seasonal trends │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Ongoing: Anomaly Detection │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ Alert when metrics deviate significantly from baseline │ │
│ │ Example: "Client onboarding time 300% above normal" │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────┘
3.2 Baseline Metrics
| Category |
Metrics Baselined |
| Device Health |
CPU, memory, temperature, uptime |
| Interface |
Utilization, errors, discards |
| Wireless |
Client count, channel utilization, noise |
| Client |
Onboarding time, roaming latency |
| Application |
Response time, throughput |
| Security |
Auth failures, threat events |
4. Anomaly Detection
4.1 Issue Categories
AI_Detected_Issues:
Network_Issues:
- Unusual traffic patterns
- Interface flapping
- Routing instability
- High CPU/memory utilization
Wireless_Issues:
- RF interference
- Channel congestion
- Client connectivity failures
- Roaming problems
Client_Issues:
- Slow onboarding
- Authentication failures
- DHCP delays
- DNS resolution issues
Application_Issues:
- Latency spikes
- Packet loss
- Throughput degradation
4.2 Viewing AI-Detected Issues
Assurance → Issues & Events → AI-Driven
AI Issue Dashboard:
┌──────────────────────────────────────────────────────────────┐
│ AI-Detected Issues - Abhavtech │
├──────────────────────────────────────────────────────────────┤
│ │
│ Priority │ Issue │ Site │
│ ─────────┼────────────────────────────────────┼──────────── │
│ P1 │ Unusual auth failure spike │ Mumbai │
│ P2 │ Client onboarding 200% above norm │ Chennai │
│ P2 │ RF interference detected │ London │
│ P3 │ Memory utilization trending up │ New Jersey │
│ │
│ AI Confidence: Each issue shows ML confidence score │
│ Example: "95% confidence this is a DNS server issue" │
│ │
└──────────────────────────────────────────────────────────────┘
5. Machine Reasoning Engine
5.1 Automated Root Cause Analysis
┌─────────────────────────────────────────────────────────────────────┐
│ Machine Reasoning Workflow │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Issue Detected │
│ "High client authentication failures at Mumbai campus" │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ Machine Reasoning Analysis: │ │
│ │ │ │
│ │ 1. Correlate with other events │ │
│ │ • ISE PSN MUM-ISE-PSN-01 high CPU (same timeframe) │ │
│ │ • RADIUS response time increased 500% │ │
│ │ │ │
│ │ 2. Check related systems │ │
│ │ • AD server reachable ✓ │ │
│ │ • Certificate valid ✓ │ │
│ │ • Network path OK ✓ │ │
│ │ │ │
│ │ 3. Compare with baseline │ │
│ │ • Normal auth rate: 50/min │ │
│ │ • Current auth rate: 500/min (DDoS pattern?) │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Root Cause Determination: │
│ "Authentication storm from misconfigured supplicant on │
│ device MAC aa:bb:cc:dd:ee:ff causing PSN overload" │
│ │
│ Recommended Action: │
│ "Quarantine device aa:bb:cc:dd:ee:ff and investigate" │
│ │
└─────────────────────────────────────────────────────────────────────┘
Assurance → Issues → Select Issue → AI Insights
Issue: High Client Onboarding Time
Site: Mumbai Campus
Affected Clients: 234
AI Analysis:
┌────────────────────────────────────────────────────────────┐
│ Root Cause (92% confidence): │
│ DHCP server response delay │
│ │
│ Evidence: │
│ • DHCP discover-to-offer: 3.2s (baseline: 0.1s) │
│ • Infoblox Mumbai showing high query load │
│ • Correlation: New floor went live 2 hours ago │
│ │
│ Recommended Actions: │
│ 1. Check Infoblox mum-ib-01.abhavtech.com load │
│ 2. Verify DHCP scope has available addresses │
│ 3. Consider adding DHCP failover peer │
│ │
│ [Apply Fix] [Ignore] [Provide Feedback] │
└────────────────────────────────────────────────────────────┘
6. AI-Enhanced RRM (Wireless)
6.1 Overview
AI-Enhanced Radio Resource Management uses machine learning to optimize wireless RF parameters automatically.
6.2 Configuration
Assurance → Settings → Wireless AI Features
AI-Enhanced RRM:
☑ Enable AI-Enhanced RRM
Optimization Goals:
☑ Maximize client throughput
☑ Minimize interference
☑ Optimize channel utilization
☑ Reduce client roaming issues
Automation Level:
○ Monitor Only (recommendations)
● Auto-Apply (automatic optimization)
○ Scheduled (apply during maintenance)
Schedule (if Scheduled):
Window: 02:00 - 05:00 UTC
Days: Saturday, Sunday
6.3 AI RRM Optimizations
| Optimization |
Description |
Automation |
| Channel Assignment |
ML-based channel selection |
Auto |
| Power Level |
Dynamic transmit power |
Auto |
| Client Steering |
Band steering optimization |
Auto |
| Load Balancing |
AP load distribution |
Auto |
| Coverage Hole |
Identify and compensate |
Alert |
7. Predictive Analytics
7.1 Trend Analysis
Assurance → Trends & Insights → Predictive
Predictive Dashboard:
┌──────────────────────────────────────────────────────────────┐
│ 30-Day Predictions - Abhavtech │
├──────────────────────────────────────────────────────────────┤
│ │
│ ⚠️ Warning: Mumbai Core Switch Memory │
│ Current: 72% | Predicted (30d): 89% │
│ Recommendation: Plan memory upgrade │
│ │
│ ⚠️ Warning: London AP Count Approaching Limit │
│ Current: 180/200 | Predicted (30d): 210/200 │
│ Recommendation: License expansion needed │
│ │
│ ✅ Healthy: Client Growth Trend │
│ Current: 8,500 | Predicted (30d): 9,200 │
│ Capacity: Sufficient │
│ │
└──────────────────────────────────────────────────────────────┘
7.2 Capacity Planning
Predictive_Capacity_Metrics:
Network:
- Switch port utilization trend
- Uplink bandwidth growth
- TCAM utilization forecast
Wireless:
- Client density prediction
- Airtime utilization trend
- AP coverage requirements
Security:
- ISE session growth
- RADIUS load projection
- Certificate expiration warnings
8. Comparative Analytics
8.1 Peer Comparison
Assurance → AI Insights → Comparative Analytics
Abhavtech vs Industry Peers:
┌──────────────────────────────────────────────────────────────┐
│ Metric │ Abhavtech │ Industry Avg │ Rank │
├───────────────────────────┼───────────┼──────────────┼──────┤
│ Client Onboarding Time │ 1.2s │ 2.1s │ Top 20% │
│ Wireless Client Success │ 98.5% │ 96.2% │ Top 15% │
│ Device Availability │ 99.95% │ 99.5% │ Top 10% │
│ Auth Success Rate │ 99.8% │ 98.9% │ Top 5% │
│ Mean Time to Resolve │ 12 min │ 28 min │ Top 10% │
└──────────────────────────────────────────────────────────────┘
Note: Comparison based on anonymized data from similar-sized
networks in the same industry vertical.
9. Integration with Webex
9.1 AI Alert Notifications
System → Settings → Notifications → Webex Teams
Webex Integration:
Bot Token: ************************
Space: #ai-network-alerts
Alert Types:
☑ AI-Detected Critical Issues
☑ Predictive Warnings
☑ Machine Reasoning Results
Format Example:
┌────────────────────────────────────────┐
│ 🤖 AI Network Analytics Alert │
├────────────────────────────────────────┤
│ Issue: Unusual traffic pattern │
│ Site: Mumbai Campus │
│ Confidence: 94% │
│ Root Cause: Possible DDoS attack │
│ Action: Investigate source 10.100.x.x │
│ │
│ [View in Catalyst Center] │
└────────────────────────────────────────┘
10. Best Practices
10.1 Optimization Tips
- Allow Learning Period: Wait 2-3 weeks for accurate baselines
- Review AI Suggestions: Validate before auto-applying
- Provide Feedback: Use feedback buttons to improve ML accuracy
- Monitor Trends: Check predictive analytics weekly
- Compare Regularly: Use peer comparison for benchmarking
10.2 Data Retention
| Data Type |
Catalyst Center |
AI Cloud |
| Raw Telemetry |
30 days |
Not stored |
| Baselines |
Continuous |
90 days |
| AI Insights |
90 days |
90 days |
| Trends |
1 year |
1 year |
Document Version: 2.0
Abhavtech.com - SD-Access Implementation