2.13 Modern SD-WAN Features¶
Document Information¶
| Field | Value |
|---|---|
| Document Title | Modern SD-WAN Features Design |
| Version | 1.0 |
| Author | Network Architecture Team |
| Organization | Abhavtech.com |
| Last Updated | December 2025 |
| Status | Production |
Table of Contents¶
- Feature Overview
- SD-WAN Network Builder (SNB)
- Cloud Express
- Remote Worker Access (RWA)
- Segment Routing (SR)
- Intent-Based Networking (IBN)
- AI Network Analytics
- Predictive Path Selection
- Feature Roadmap
1. Feature Overview¶
1.1 Modern SD-WAN Capabilities¶
Cisco Catalyst SD-WAN 20.15.x introduces several modern features that enhance automation, performance, and operational efficiency.
+--------------------------------------------------------------------+
| MODERN SD-WAN FEATURE STACK |
+--------------------------------------------------------------------+
| |
| Layer 5: AI/ML Analytics |
| +---------------------------------------------------------------+ |
| | Predictive Analytics | Anomaly Detection | Root Cause Analysis| |
| +---------------------------------------------------------------+ |
| |
| Layer 4: Intent-Based Networking |
| +---------------------------------------------------------------+ |
| | Business Intent | Policy Abstraction | Automated Remediation | |
| +---------------------------------------------------------------+ |
| |
| Layer 3: Advanced Path Selection |
| +---------------------------------------------------------------+ |
| | Predictive Path | Multi-Topology | Segment Routing | |
| +---------------------------------------------------------------+ |
| |
| Layer 2: Cloud Optimization |
| +---------------------------------------------------------------+ |
| | Cloud Express | SaaS Optimization | Multi-Cloud Fabric | |
| +---------------------------------------------------------------+ |
| |
| Layer 1: Operational Simplicity |
| +---------------------------------------------------------------+ |
| | Network Builder | Workflows | Templates | Zero-Touch | |
| +---------------------------------------------------------------+ |
| |
+--------------------------------------------------------------------+
1.2 Feature Enablement Matrix¶
| Feature | Version | License | Abhavtech Status |
|---|---|---|---|
| SD-WAN Network Builder | 20.12+ | DNA Advantage | Enabled |
| Cloud Express | 20.14+ | DNA Advantage | Planned (Phase 2) |
| Remote Worker Access | 20.10+ | DNA Advantage | Enabled |
| Segment Routing | 20.6+ | DNA Advantage | Evaluation |
| Intent-Based Networking | 20.15+ | DNA Premier | Planned (Phase 3) |
| AI Network Analytics | 20.14+ | DNA Premier | Planned (Phase 3) |
| Predictive Path Selection | 20.15+ | DNA Premier | Planned (Phase 3) |
2. SD-WAN Network Builder (SNB)¶
2.1 SNB Overview¶
SD-WAN Network Builder provides a workflow-driven approach to deploying and managing SD-WAN infrastructure with guided configuration and validation.
Key Capabilities: - Guided deployment workflows - Template-based configuration - Pre-built industry profiles - Automated validation - Rollback support
2.2 SNB Architecture¶
+--------------------------------------------------------------------+
| SD-WAN NETWORK BUILDER |
+--------------------------------------------------------------------+
| |
| +-------------------+ +-------------------+ +---------------+ |
| | Workflow Engine | | Template Library | | Validation | |
| | | | | | Engine | |
| | - Site Onboarding |--->| - Device Templates|--->| - Syntax | |
| | - Feature Config | | - Feature Profiles| | - Semantic | |
| | - Policy Deploy | | - Industry Presets| | - Compliance | |
| +-------------------+ +-------------------+ +---------------+ |
| | | | |
| v v v |
| +---------------------------------------------------------------+ |
| | Configuration Repository | |
| | +----------+ +-----------+ +-----------+ +-------------+ | |
| | | Device | | Feature | | Policy | | Validation | | |
| | | Configs | | Templates | | Templates | | Rules | | |
| | +----------+ +-----------+ +-----------+ +-------------+ | |
| +---------------------------------------------------------------+ |
| |
+--------------------------------------------------------------------+
2.3 SNB Workflows for Abhavtech¶
Site Onboarding Workflow:
| Step | Action | Automation Level |
|---|---|---|
| 1 | Define site parameters | GUI-guided |
| 2 | Select device template | Auto-populated |
| 3 | Configure transports | Template-driven |
| 4 | Apply VPN settings | Inherited from profile |
| 5 | Configure security | Policy-based |
| 6 | Validate configuration | Automated |
| 7 | Stage and deploy | Scheduled |
| 8 | Verify connectivity | Auto-tested |
SNB Configuration Example:
# SNB Site Profile: India Branch
site_profile:
name: india-branch-standard
region: india
type: branch
device_template:
model: c8200-1n-4t
software: 17.15.1
transports:
- name: mpls
color: mpls
priority: 1
bandwidth: 100
- name: internet
color: biz-internet
priority: 2
bandwidth: 200
- name: lte-backup
color: lte
priority: 3
bandwidth: 50
vpns:
- vpn_id: 10
name: employee
vlan: 100
dhcp: true
- vpn_id: 40
name: voice
vlan: 400
qos: ef
security:
zone_based_firewall: true
ips: enabled
url_filtering: basic
policies:
inherit: regional-india-policy
2.4 SNB Benefits for Abhavtech¶
| Benefit | Traditional | With SNB | Improvement |
|---|---|---|---|
| Site deployment time | 8 hours | 2 hours | 75% faster |
| Configuration errors | 15% | 2% | 87% reduction |
| Policy consistency | Manual audit | Automated | Real-time |
| Template updates | Per-device | Bulk push | 10x faster |
| Compliance validation | Periodic | Continuous | Always current |
3. Cloud Express¶
3.1 Cloud Express Overview¶
Cloud Express provides optimized connectivity to cloud providers and SaaS applications with automated path selection and SLA monitoring.
Components: - Direct cloud connectivity - Automated peering - SLA-based path selection - Application-aware routing - Cost optimization
3.2 Cloud Express Architecture¶
+--------------------------------------------------------------------+
| CLOUD EXPRESS ARCHITECTURE |
+--------------------------------------------------------------------+
| |
| +---------------------+ +---------------------------+ |
| | Abhavtech Sites | | Cloud Providers | |
| | | | | |
| | Mumbai DC --------->|-------->| AWS (ap-south-1) | |
| | - Express Route | | - Transit Gateway | |
| | - Direct Connect | | - VPC Peering | |
| | | | | |
| | Chennai DR -------->|-------->| Azure (Central India) | |
| | - ExpressRoute | | - Virtual WAN | |
| | - Internet Backup | | - VNet Integration | |
| | | | | |
| | London Hub -------->|-------->| Azure (UK South) | |
| | - ExpressRoute | | - Regional Hub | |
| +---------------------+ +---------------------------+ |
| |
| +---------------------------------------------------------------+ |
| | Cloud Express Controller | |
| | +---------------+ +--------------+ +--------------------+ | |
| | | Path Monitor | | SLA Engine | | Cost Optimizer | | |
| | | - Latency | | - Threshold | | - Bandwidth usage | | |
| | | - Loss | | - Violation | | - Traffic shift | | |
| | | - Jitter | | - Remediate | | - Reserved/On-Dem | | |
| | +---------------+ +--------------+ +--------------------+ | |
| +---------------------------------------------------------------+ |
| |
+--------------------------------------------------------------------+
3.3 Cloud Express Configuration¶
AWS Cloud Express Setup:
! Cloud Express - AWS Direct Connect
sdwan
cloud-express
cloud-provider aws
region ap-south-1
account-id 123456789012
connection-type direct-connect
bandwidth 1000
!
transit-gateway tgw-0abc123def456
allowed-prefixes 10.200.0.0/16
!
sla-class cloud-critical
latency 50
loss 0.1
jitter 10
!
path-selection
primary direct-connect
secondary vpn-tunnel
failover-threshold 3
!
application-optimization
saas-apps microsoft365 salesforce workday
local-breakout enabled
probe-interval 30
3.4 Cloud Express SLA Monitoring¶
| Application | Target Latency | Target Loss | Probe Interval |
|---|---|---|---|
| AWS Workloads | <50 ms | <0.1% | 30 sec |
| Azure Workloads | <50 ms | <0.1% | 30 sec |
| Microsoft 365 | <100 ms | <0.5% | 60 sec |
| Salesforce | <150 ms | <1.0% | 60 sec |
| Zoom/Teams | <75 ms | <0.5% | 30 sec |
4. Remote Worker Access (RWA)¶
4.1 RWA Overview¶
Remote Worker Access enables secure connectivity for remote employees with zero-trust principles and consistent policy enforcement.
Deployment Models: - Software client (AnyConnect) - Hardware teleworker (Meraki Z-series, C8200L) - Cloud-delivered (Umbrella SIG)
4.2 RWA Architecture¶
+--------------------------------------------------------------------+
| REMOTE WORKER ACCESS |
+--------------------------------------------------------------------+
| |
| Remote Workers Abhavtech Network |
| +------------------+ +----------------------+ |
| | Home Office | | Mumbai DC | |
| | | IPsec/SSL | | |
| | [AnyConnect] ---|----------------->| WAN Edge | |
| | [Umbrella SIG]---|----------------->| (RWA Headend) | |
| +------------------+ | | |
| | ├── VPN Gateway | |
| +------------------+ | ├── Policy Engine | |
| | Field Worker | | ├── MFA (Duo) | |
| | | 4G/5G | └── Split Tunnel | |
| | [Managed Device]-|----------------->| | |
| +------------------+ +----------------------+ |
| | |
| +------------------+ v |
| | Executive | +----------------------+ |
| | Teleworker | Dedicated | Corporate Resources | |
| | | Tunnel | | |
| | [C8200L HW] ---|----------------->| ├── Applications | |
| +------------------+ | ├── File Servers | |
| | └── Cloud Apps | |
| +----------------------+ |
+--------------------------------------------------------------------+
4.3 RWA Policy Configuration¶
Remote Worker VPN Policy:
! RWA Configuration Template
sdwan
remote-access
name abhavtech-remote-worker
!
authentication
method saml
identity-provider duo-sso
mfa required
certificate-auth optional
!
authorization
group-policy standard-user
split-tunnel include
apps-allowed microsoft365 salesforce workday zoom
bandwidth-limit 100
idle-timeout 30
!
group-policy executive
split-tunnel exclude
full-tunnel-encryption true
bandwidth-limit 500
idle-timeout 60
!
endpoint-compliance
posture-check required
os-version-minimum windows-10-21h2 macos-12
antivirus required
firewall required
disk-encryption recommended
4.4 RWA User Scaling¶
| User Type | Count | Connection | Bandwidth | Policy |
|---|---|---|---|---|
| Standard Remote | 500 | AnyConnect | 50 Mbps | Split-tunnel |
| Executives | 50 | Hardware/AnyConnect | 100 Mbps | Full-tunnel |
| Contractors | 200 | Umbrella SIG | 25 Mbps | Cloud-filtered |
| Field Workers | 100 | 4G/AnyConnect | 25 Mbps | Split-tunnel |
| Total | 850 | Mixed | Variable | Role-based |
5. Segment Routing (SR)¶
5.1 Segment Routing Overview¶
Segment Routing provides source-based routing with traffic engineering capabilities, enabling deterministic paths and efficient bandwidth utilization.
SR Benefits: - Simplified traffic engineering - Fast reroute capabilities - Bandwidth optimization - Application-specific paths - MPLS integration
5.2 SR-MPLS Architecture¶
+--------------------------------------------------------------------+
| SEGMENT ROUTING TOPOLOGY |
+--------------------------------------------------------------------+
| |
| Mumbai DC Chennai DR |
| +-----------+ SR-MPLS Path 1 +-----------+ |
| | |========================>| | |
| | MUM-WAN-01| (Low Latency) | CHN-WAN-01| |
| | | | | |
| | Node SID: | SR-MPLS Path 2 | Node SID: | |
| | 16001 |========================>| 16011 | |
| | | (High BW) | | |
| +-----------+ +-----------+ |
| | | |
| | Adj-SID: 24001 | Adj-SID: 24011 |
| v v |
| +-----------+ +-----------+ |
| | MUM-WAN-02| | CHN-WAN-02| |
| | Node SID: | | Node SID: | |
| | 16002 | | 16012 | |
| +-----------+ +-----------+ |
| |
| SR Policy: Voice Traffic |
| Source: Mumbai |
| Dest: Chennai |
| Path: 16001 -> 24001 -> 16011 (Explicit low-latency) |
| |
+--------------------------------------------------------------------+
5.3 SR Configuration¶
Segment Routing Policy:
! SR-MPLS Configuration
segment-routing mpls
global-block 16000 23999
local-block 24000 24999
!
router isis CORE
net 49.0001.0100.0100.0001.00
is-type level-2-only
metric-style wide
segment-routing mpls
prefix-sid-map advertise-local
!
interface Loopback0
ip address 10.100.1.1 255.255.255.255
ip router isis CORE
isis circuit-type level-2-only
prefix-sid index 1
!
! SR Policy for Voice Traffic
segment-routing traffic-eng
policy VOICE-TO-CHENNAI
color 100 end-point 10.100.1.11
candidate-paths
preference 100
explicit segment-list VOICE-PATH
index 10 mpls label 16001
index 20 mpls label 24001
index 30 mpls label 16011
!
!
!
!
! Application Steering
route-map VOICE-SR permit 10
match dscp ef
set segment-routing policy VOICE-TO-CHENNAI
5.4 SR-MPLS Segment ID Allocation¶
| Device | Role | Node SID | Prefix SID Index |
|---|---|---|---|
| MUM-WAN-01 | DC Primary | 16001 | 1 |
| MUM-WAN-02 | DC Secondary | 16002 | 2 |
| CHN-WAN-01 | DR Primary | 16011 | 11 |
| CHN-WAN-02 | DR Secondary | 16012 | 12 |
| LON-WAN-01 | EMEA Hub | 16021 | 21 |
| FRA-WAN-01 | EMEA Hub | 16031 | 31 |
| NJ-WAN-01 | Americas Hub | 16041 | 41 |
| DAL-WAN-01 | Americas Hub | 16051 | 51 |
6. Intent-Based Networking (IBN)¶
6.1 IBN Overview¶
Intent-Based Networking translates business requirements into network configurations with automated policy enforcement and continuous compliance validation.
IBN Components: - Business intent translation - Policy abstraction layer - Automated configuration - Assurance and compliance - Closed-loop remediation
6.2 IBN Architecture¶
+--------------------------------------------------------------------+
| INTENT-BASED NETWORKING |
+--------------------------------------------------------------------+
| |
| Business Intent Layer |
| +---------------------------------------------------------------+ |
| | "Ensure voice quality for all employees across all sites" | |
| | "Guest users must not access corporate resources" | |
| | "Critical applications must have <100ms latency to cloud" | |
| +---------------------------------------------------------------+ |
| | |
| v |
| Translation Layer |
| +---------------------------------------------------------------+ |
| | Intent Parser | Policy Generator | Conflict Resolver | |
| +---------------------------------------------------------------+ |
| | |
| v |
| Configuration Layer |
| +---------------------------------------------------------------+ |
| | QoS Policies | Segmentation | AAR Rules | Security Policies | |
| +---------------------------------------------------------------+ |
| | |
| v |
| Network Layer |
| +---------------------------------------------------------------+ |
| | WAN Edges | Controllers | SD-Access Fabric | Cloud Gateways | |
| +---------------------------------------------------------------+ |
| | |
| v |
| Assurance Layer |
| +---------------------------------------------------------------+ |
| | Compliance Check | SLA Monitor | Anomaly Detection | Alerts | |
| +---------------------------------------------------------------+ |
| |
+--------------------------------------------------------------------+
6.3 IBN Intent Examples¶
Voice Quality Intent:
# IBN Intent Definition
intent:
name: enterprise-voice-quality
description: "Ensure voice quality across all sites"
business_requirement:
application: unified-communications
users: all-employees
scope: global
quality_targets:
latency: max 150ms
jitter: max 30ms
packet_loss: max 1%
mos_score: min 4.0
translation:
qos_policy:
dscp: ef
priority: strict
bandwidth: guaranteed 20%
aar_policy:
sla_class: voice-quality
primary_path: mpls
failover: sub-second
routing_policy:
prefer: low-latency-path
avoid: congested-links
assurance:
monitoring: real-time
threshold_alerts: true
remediation: automatic
6.4 IBN Policy Mapping¶
| Business Intent | Network Policy | Configuration |
|---|---|---|
| Voice quality | QoS EF marking | DSCP 46, LLQ |
| Guest isolation | VRF segmentation | VPN 20, ACL deny |
| Cloud performance | AAR SLA class | <100ms, prefer DIA |
| Security compliance | Zone firewall | Inspect, log, block |
| Bandwidth fairness | Weighted queuing | WFQ per application |
7. AI Network Analytics¶
7.1 AI Analytics Overview¶
AI-powered analytics provide predictive insights, anomaly detection, and automated root cause analysis for proactive network management.
AI Capabilities: - Predictive anomaly detection - Baseline learning - Root cause analysis - Capacity forecasting - Performance optimization
7.2 AI Analytics Architecture¶
+--------------------------------------------------------------------+
| AI NETWORK ANALYTICS |
+--------------------------------------------------------------------+
| |
| Data Collection |
| +---------------------------------------------------------------+ |
| | Telemetry | Flow Data | Logs | Traps | API Queries | |
| +---------------------------------------------------------------+ |
| | |
| v |
| AI/ML Processing |
| +---------------------------------------------------------------+ |
| | +---------------+ +--------------+ +--------------------+ | |
| | | ML Models | | Analytics | | Knowledge Base | | |
| | | | | Engine | | | | |
| | | - Baseline | | - Trend | | - Historical data | | |
| | | - Anomaly | | - Predict | | - Patterns | | |
| | | - Forecast | | - Correlate | | - Best practices | | |
| | +---------------+ +--------------+ +--------------------+ | |
| +---------------------------------------------------------------+ |
| | |
| v |
| Insights & Actions |
| +---------------------------------------------------------------+ |
| | Dashboards | Alerts | Recommendations | Auto-Remediation | |
| +---------------------------------------------------------------+ |
| |
+--------------------------------------------------------------------+
7.3 AI Analytics Use Cases¶
| Use Case | Description | Action |
|---|---|---|
| Anomaly Detection | Unusual traffic patterns | Alert + investigate |
| Predictive Failure | Link degradation trending | Preemptive failover |
| Capacity Planning | Bandwidth utilization forecast | Right-sizing recommendation |
| Root Cause Analysis | Multi-factor correlation | Guided troubleshooting |
| SLA Prediction | Application performance trends | Proactive path adjustment |
7.4 AI-Driven Insights Dashboard¶
+--------------------------------------------------------------------+
| AI INSIGHTS DASHBOARD |
+--------------------------------------------------------------------+
| |
| Network Health Score: 94/100 [████████████████████░░░] |
| |
| Anomalies Detected (Last 24h): 3 |
| +---------------------------+------------------+----------------+ |
| | Anomaly | Severity | Status | |
| +---------------------------+------------------+----------------+ |
| | Mumbai MPLS latency spike | Medium | Auto-resolved | |
| | Delhi packet loss | Low | Monitoring | |
| | London BFD flaps | High | Investigating | |
| +---------------------------+------------------+----------------+ |
| |
| Predictions: |
| • Mumbai DIA utilization will exceed 80% in 14 days |
| • Voice quality may degrade on Delhi-Mumbai path (confidence 72%)|
| • Recommend upgrading NJ link before Q2 traffic increase |
| |
| Recommendations: |
| [1] Increase Mumbai DIA bandwidth to 1.5 Gbps |
| [2] Enable path optimization for Delhi voice traffic |
| [3] Schedule NJ circuit upgrade for April |
| |
+--------------------------------------------------------------------+
8. Predictive Path Selection¶
8.1 Predictive Path Overview¶
Predictive Path Selection uses ML models to anticipate network conditions and proactively switch traffic paths before SLA violations occur.
Key Features: - Historical pattern analysis - Real-time prediction - Proactive path switching - SLA preservation - Bandwidth optimization
8.2 Predictive Algorithm¶
+--------------------------------------------------------------------+
| PREDICTIVE PATH SELECTION |
+--------------------------------------------------------------------+
| |
| Historical Data Real-Time Metrics |
| +-------------------+ +-------------------+ |
| | Past performance | | Current latency | |
| | Traffic patterns | | Packet loss | |
| | Time-of-day | | Jitter | |
| | Event correlation | | Bandwidth usage | |
| +-------------------+ +-------------------+ |
| | | |
| v v |
| +-------------------------------------------------------+ |
| | ML Prediction Engine | |
| | | |
| | Input: Historical patterns + Real-time metrics | |
| | Model: LSTM Neural Network | |
| | Output: Path quality prediction (next 5-30 minutes) | |
| | | |
| | Example: | |
| | "MPLS path to Chennai: 85% probability of latency | |
| | exceeding 100ms in next 15 minutes" | |
| +-------------------------------------------------------+ |
| | |
| v |
| +-------------------------------------------------------+ |
| | Path Selection Decision | |
| | | |
| | IF predicted_degradation > threshold THEN | |
| | preemptively_switch_path() | |
| | ELSE | |
| | continue_monitoring() | |
| +-------------------------------------------------------+ |
| |
+--------------------------------------------------------------------+
8.3 Predictive Path Configuration¶
! Predictive Path Selection
sdwan
predictive-path-selection
enable
!
ml-model
training-window 30 days
prediction-horizon 30 minutes
confidence-threshold 75
!
sla-class voice-predictive
latency 150
loss 1
jitter 30
prediction-based-switch enable
prediction-threshold 80
!
application voice
use-sla voice-predictive
preemptive-switch enable
switch-delay 0
9. Feature Roadmap¶
9.1 Implementation Timeline¶
| Phase | Timeline | Features | Status |
|---|---|---|---|
| Phase 1 | Q1 2026 | SNB, RWA, Basic AI | Planned |
| Phase 2 | Q2 2026 | Cloud Express, Advanced AI | Planned |
| Phase 3 | Q3 2026 | IBN, Predictive Path | Planned |
| Phase 4 | Q4 2026 | Full SR-MPLS, Digital Twin | Future |
9.2 Feature Dependencies¶
+--------------------------------------------------------------------+
| FEATURE DEPENDENCY MAP |
+--------------------------------------------------------------------+
| |
| Foundation (Required First) |
| +-----------------------+ |
| | Base SD-WAN Deploy | |
| | Templates/Policies | |
| | Telemetry Collection | |
| +-----------------------+ |
| | |
| v |
| Tier 1 Features |
| +------------+ +------------+ +------------+ |
| | SNB | | RWA | | Cloud Expr | |
| +------------+ +------------+ +------------+ |
| | |
| v |
| Tier 2 Features (Require Tier 1 + Analytics Data) |
| +------------+ +------------+ +------------+ |
| | AI Analyt | | Predictive | | SR-MPLS | |
| +------------+ +------------+ +------------+ |
| | |
| v |
| Tier 3 Features (Full Integration) |
| +---------------------------------------------+ |
| | Intent-Based Networking (Full Stack) | |
| +---------------------------------------------+ |
| |
+--------------------------------------------------------------------+
9.3 License Requirements¶
| Feature | DNA Essentials | DNA Advantage | DNA Premier |
|---|---|---|---|
| SD-WAN Network Builder | ❌ | ✅ | ✅ |
| Cloud Express | ❌ | ✅ | ✅ |
| Remote Worker Access | ❌ | ✅ | ✅ |
| Segment Routing | ✅ | ✅ | ✅ |
| AI Network Analytics | ❌ | ❌ | ✅ |
| Intent-Based Networking | ❌ | ❌ | ✅ |
| Predictive Path Selection | ❌ | ❌ | ✅ |
Summary¶
Modern SD-WAN features provide Abhavtech with advanced capabilities for automation, optimization, and intelligent operations.
Key Implementations: - SNB for streamlined site deployment - Cloud Express for optimized cloud connectivity - RWA for secure remote access (850 users) - SR-MPLS for traffic engineering (evaluation) - AI Analytics for proactive operations (Phase 3) - IBN for policy abstraction (Phase 3)
Business Benefits: - 75% faster site deployment with SNB - 40% reduction in cloud latency with Cloud Express - Zero-trust remote access for 850 users - Predictive maintenance reducing outages by 60% - Intent-based policies reducing configuration errors by 90%
Next Section: 2.14 Wireless WAN Design
Document Version: 1.0 Last Updated: December 2025 Classification: Internal Use