What Is AI Network Optimization?

AI network optimization uses AI and ML to perform traffic management and routing across the corporate WAN. AI tools operate at the control layer, analyzing telemetry, identifying patterns, and recommending configuration changes designed to improve the performance and reliability of the corporate WAN.

As corporate networks grow more distributed with the adoption of cloud infrastructure and hybrid work, network inefficiencies have a greater impact on the business. Additionally, the dynamic nature of IT environments – with ephemeral cloud resources and traveling workers – means that configuration changes must be made regularly to optimize these environments.

AI can analyze data, detect patterns, and make updates in real-time, keeping pace with the rapid evolution of the corporate WAN. If AI is deployed as part of a SASE platform, integrated with the network infrastructure rather than being deployed as a point solution, this can be accomplished without performance impacts.

How Do You Define AI Network Optimization?

AI network optimization uses AI and ML to enhance network performance, user experience, and reliability by analyzing network telemetry and suggesting improvements. This builds on traditional optimization techniques, such as TCP acceleration, compression, and static QoS rules, by adopting feedback loops to test, validate, and improve potential optimizations.

AI network optimization applies to the entirety of the corporate WAN, managing routing, QoS, bandwidth allocation, and potentially security policy placement when relevant to path selection.

Core Elements of AI Network Optimization

AI network optimization implements intelligent analysis and optimization for network management. Key elements of the infrastructure include:

• Data Collection: AI tools require access to high-quality telemetry to identify potential areas for optimization. This includes insight into all elements of the corporate WAN, including on-prem and cloud-based infrastructure.
• Feature Extraction: Feature extraction distills collected data into a set of useful features for analysis. This eliminates noisy data while focusing on useful information.
• Model Training and Inference: With access to high-quality data, AI and ML can perform analysis and look for trends. Learning is performed continuously, allowing the model to adapt to changes in network infrastructure and usage.
• Enforcement in the Network: AI network optimization tools can make recommendations, but integration with other network tools is essential for enforcement. AI is best applied as part of a converged SASE deployment, where recommendations can be applied consistently across the entire corporate WAN.

How Does AI Network Optimization Work in Modern Networks?

In modern networks, AI network optimization typically lives in the cloud control pane or centralized management plane of a SASE or SD-WAN architecture. This system is responsible for collecting telemetry, identifying patterns or anomalies, and recommending or automatically applying changes to routing and QoS policies.

Real-Time Path Selection and Policy Adjustment

Path selection is vital to network performance, especially for SD-WAN and SASE deployments reliant on the public Internet. AI systems can analyze available paths based on current latency, jitter, packet loss, and throughput, offering up-to-date information and rankings.

This near-real-time visibility allows AI systems to dynamically adjust route selection, QoS queues, and traffic steering in response to performance data and traffic analytics. For example, the system could intelligently route video and voice traffic to low-latency and low-jitter paths while using less expensive links to carry bulk traffic.

How Does AI WAN Optimization Fit Into This Picture?

AI WAN optimization focuses on optimization of the corporate WAN, specifically choosing WAN paths and applying WAN-specific optimizations. With AI, organizations can move beyond static policies and fixed algorithms to a more dynamic and intelligent approach.

What Problems Does AI Network Optimization Solve?

AI network optimization solves the problem of unpredictable latency, packet loss, and jitter within corporate networks. Modern Internet traffic commonly crosses multiple ISPs and cloud providers, introducing complexity that leads to poor SaaS performance and degrades the user experience. With AI, problems can be identified and addressed in near-real time, reducing friction in the user experience and decreasing troubleshooting fatigue amongst operations teams.

Performance Issues That AI Network Optimization Addresses

Traffic routed over the public Internet can suffer various problems due to the unreliability of these network links. Common symptoms include:

• Latency
• Jitter
• Packet loss
• Intermittent brownouts
• Congestion at shared Internet exchanges
• Inconsistent SaaS performance.

AI tools are skilled at pattern identification, allowing them to identify common issues, such as time-of-day congestion or specific links that suffer recurring brownouts. Based on this analysis and knowledge of available links, the AI can suggest alternative routes or automatically apply these changes itself.

Operational Challenges AI Helps Reduce

Operations teams face significant challenges when attempting to diagnose and address problems related to network performance. The growing distribution of corporate environments means that corporate network traffic may traverse multiple ISPs and cloud platforms. As a result, it is difficult and time-consuming to pin down the root cause of an issue.

AI helps to reduce overhead and mean time to detection by performing automatic root cause analysis for incidents, offering insights into probable root causes. By automating the investigation and triage process, these tools reduce load on human operators and allow smaller teams to rapidly make decisions and implement changes to enhance the performance of complex, distributed networks.

Which Data and Telemetry Feed AI Network Optimization?

AI systems are only as good as the data that they have access to. In the case of AI network optimization tools, they need high-quality telemetry that provides visibility into the entire corporate network and insight into key goals, such as network performance, security, and the user experience.

AI network optimization tools are commonly deployed as part of SASE and SD-WAN platforms, which already collect the types of network flow, user, application, and security data that AI tools require. Bringing this information into a single, centralized data lake makes it easier for AI tools to access and analyze this information.

Network and Transport Layer Metrics

Network and transport layer metrics focus on enhancing the performance and reliability of an organization’s network infrastructure. Key metrics include:

• Latency
• Jitter
• Packet loss
• Throughput
• Link utilization
• Error rates
• Route changes.

AI models can use these metrics to classify the health of various network links and predict future performance. For example, a particular network link may see a surge in usage at particular times because of user behavior or a scheduled task or job. An AI tool that identifies this pattern can provide recommendations that route traffic to other links during these times to reduce congestion and latency.

Global SASE backbones are ideally suited to this type of analysis due to their ability to collect telemetry and compare performance across multiple edges and ISPs. This wider field of view enriches the training data available to AI models and improves path selection across the backbone.

Application and User Experience Metrics

Digital experience monitoring (DEM) attempts to improve the user experience by tracking metrics such as:

• User device telemetry
• Application response times
• Transaction success and error rates
• Synthetic transaction results

With visibility into the network infrastructure and application-layer metrics, AI network optimization tools can correlate degradation in DEM metrics with network-level conditions. This allows them to identify potential changes and optimizations at the network level that would have a positive impact on the user experience as well.

For example, user reports of slow load times for a particular SaaS app might prompt an investigation into that app to identify potential improvements. However, a wider investigation might reveal that traffic to that and other SaaS apps flows over a link that is commonly overloaded. Routing updates that reduce strain on this link would positively impact the user experience across all SaaS apps, rather than addressing the symptoms within a single app.

How Does AI Congestion Prediction Improve Performance?

AI congestion prediction uses predictive models to forecast when and where congestion is likely to occur based on historical data and real-time telemetry. With AI, organizations can go beyond simple threshold-based alerts to identify patterns that point to potential congestion. This is increasingly important for enterprise networks as employees grow more reliant on voice and video tools and other latency-sensitive SaaS apps.

Predictive Models for Congestion and Brownouts

AI and ML can leverage time-series models and anomaly detection to identify likely congestion windows on specific network paths or PoPs. For example, an AI tool with sufficient access to historical data might note trends, such as the propensity for employees to check their email upon return from a scheduled lunch break or the fact that developers and IT personnel schedule certain tasks to execute at specific times. With knowledge of these trends, the AI can recommend network routes that avoid the network congestion that these activities could cause on particular network links.

While these sources of congestion are internal to a business, AI tools hosted on a multi-tenant SASE backbone have much wider visibility. With access to data on aggregate behavior across many customers and geographies, they can identify patterns and perform optimization on a more global level.

Routing and QoS Actions Driven by Prediction

AI offers the ability to predict potential brownouts and congestion, allowing the organization to take action to mitigate their effects. For example, an AI system may route critical traffic to alternative routes in advance of a brownout or adjust QoS rules to ensure that latency-sensitive, real-time applications are prioritized during times of peak traffic.

The ability of these systems to adapt to changes depends on the level of human involvement in the process. In the case of frequent, predictable issues, AI may forecast the incident in time for humans to approve changes. However, AI identifying signs of an unpredictable, pending outage may require automatic intervention to minimize the impact. Organizations should implement policy controls that dictate which decisions can be automated and which recommendations require human review and approval.

FAQs about AI Network Optimization

Does AI network optimization replace traditional WAN optimization tools?

No, AI network optimization augments traditional WAN optimization tools, such as caching, compression, and TCP tuning. AI adds a layer of intelligence and adaptability, allowing configurations and policies to be modified in response to changes in the network infrastructure and usage. Many modern SD-WAN and SASE solutions include those traditional WAN optimization tools that make sense in context, while also expanding capabilities by integrating AI.

How mature are AI network optimization capabilities today?

AI-assisted network optimization capabilities are common in SD-WAN, SASE, and digital experience solutions, but their effectiveness can vary. When considering solutions, ask about how AI features are used in production, including documented use cases, such as dynamic path selection, noise reduction in alerts, and predictive insights.

How does AI network optimization interact with security policies?

AI network optimization tools have the ability to reroute network traffic, but this doesn’t come at the expense of security. Tools must ensure that traffic still undergoes inspection or controls, but can make recommendations or choices between multiple secure, policy-compliant routes, and can prioritize certain types of traffic within a route. Some platforms may also use AI to analyze security policies and routing rules to identify potential room for improvement, such as addressing redundancies or misconfigurations that have a negative impact on network performance.

What skills do teams need to get value from AI network optimization?

To effectively operate an AI network optimization strategy, teams need skills in data management, AI, and optimization, as well as their fundamentals in networking, routing, and security. Operators should understand what data to feed to the AI system, how to interpret its results, and how to decide which use cases can be fully automated and which require a human in the loop.

Do enterprises need full automation, or can AI network optimization stay human in the loop?

AI network optimization can be deployed in various modes, ranging from full automation to a more advisory role, where humans remain in the loop. Often, this advisory option is a good starting point for businesses deploying the technology, allowing them to test visibility and the quality of the AI’s recommendations before allowing it to autonomously make low-risk changes to network routing. Over time, enterprises commonly move to a hybrid mode, where routine and repetitive changes are managed by AI, while humans remain in control of high-impact decisions. Regardless, all recommendations and changes should be logged so that they can be reviewed and reversed if needed.