Network Intelligence
Problem Statement
SD-WAN deployments connect branch offices, cloud environments, and remote users over multiple transport links, including MPLS, broadband, and 5G. While SD-WAN centralizes control, performance tuning often remains reactive. Network teams manually adjust path selection, QoS policies, and failover thresholds after users report degraded application performance. Static policies cannot fully adapt to fluctuating latency, packet loss, or jitter across diverse links. As application traffic shifts to SaaS and cloud platforms, inconsistent path optimization increases downtime risk and degrades user experience.
AI Solution Overview
AI-driven SD-WAN performance automation continuously analyzes link health, application sensitivity, and traffic demand to dynamically optimize path selection and policy enforcement. Instead of relying solely on predefined thresholds, machine learning models predict degradation trends and automatically reroute traffic to maintain service-level objectives.
Core capabilities
These AI capabilities enhance performance consistency and operational efficiency across distributed networks.
- Real-time link quality modeling: Continuously evaluate latency, jitter, packet loss, and throughput across all available transport links.
- Application-aware path optimization: Automatically steer traffic based on application performance requirements and business criticality.
- Predictive failover management: Anticipate link degradation and trigger rerouting before user impact occurs.
- Dynamic policy adjustment: Refine QoS, prioritization, and traffic shaping rules in response to evolving network conditions.
- Closed-loop performance validation: Measure post-routing performance and refine models to improve future decision-making.
Together, these capabilities transform SD-WAN from centralized control to intelligent, self-optimizing infrastructure.
Integration points
Effective automation requires integration across network and application monitoring systems.
- SD-WAN controllers: Interface with platforms such as Cisco SD-WAN, VMware SD-WAN, or Fortinet Secure SD-WAN for centralized policy execution.
- Cloud service monitoring tools: Integrate with ThousandEyes or Catchpoint to assess SaaS and internet path performance.
- Observability platforms: Connect with Datadog, Splunk, or Elastic for telemetry ingestion and cross-domain correlation.
- ITSM systems: Synchronize with ServiceNow or Jira Service Management to automate incident updates and change documentation.
Integrated ecosystems ensure AI-driven decisions are measurable, governed, and aligned with enterprise operations.
Dependencies and prerequisites
The following foundations are essential for successful deployment.
- Comprehensive link telemetry: Continuous performance metrics across all WAN transports and edge devices.
- Defined application performance tiers: Clear classification of latency-sensitive and mission-critical applications.
- Programmable SD-WAN infrastructure: API-enabled controllers capable of real-time path and policy changes.
- Operational governance framework: Agreed-upon thresholds for automated rerouting and escalation procedures.
These prerequisites ensure automation enhances performance without introducing instability or compliance risk.
Examples of Implementation
Several industries use AI-driven SD-WAN performance automation to maintain service quality across distributed operations.
- Retail enterprises: Dynamically reroute point-of-sale and inventory traffic across broadband and LTE links during peak shopping periods.
- Healthcare networks: Optimize telehealth and electronic health record traffic across redundant WAN links.
- Manufacturing organizations: Use AI to maintain reliable connectivity between production facilities and centralized ERP systems, dynamically balancing traffic across MPLS and internet-based transports.
These implementations demonstrate how intelligent automation sustains consistent performance in high-availability environments.
Vendors
Several startups are advancing intelligent WAN and performance automation capabilities.
- Aryaka: Deliver managed SD-WAN and SASE solutions with integrated performance optimization across a global private backbone. (Aryaka)
- Graphiant: Provide Network-as-a-Service with intelligent traffic steering across a global edge fabric to optimize WAN performance. (Graphiant)
- Alkira: Offer cloud-native networking infrastructure with centralized policy control and programmable traffic optimization across hybrid environments. (Alkira)
