software-defined WAN (SD-WAN) continues to be a game changer in any organization’s digital transformation journey. SD-WAN’s ability to intelligently direct application traffic, enforce service level agreements (SLAs), and provide granular visibility has made it a de facto solution for delivering branch transformation, cloud adoption, and hybrid workforce support. While legacy SD-WAN manages to automate some day 0 operations with zero-touch provisioning and centralized configuration, it still requires significant human intervention to identify, troubleshoot, and resolve issues, leading to operational complexity and cost.
Evolution of AIOps for SD-WAN
Somehow, legacy SD-WAN has managed to hide underlying network issues by addressing performance issues with its rudimentary techniques like failover and redundancy. As a result, infrastructure teams continue to react to a critical outage instead of proactively identifying anomalies and resolving them. To overcome these challenges, organizations are increasing their use of artificial intelligence for operations (AIOps) capabilities to reduce operational complexity and costs. Gardener acknowledges this trend, stating, “By 2025, 40% of enterprises with SD-WAN deployments will use artificial intelligence (AI) capabilities to automate Day 2 operations, compared to less than 5% in 2021.”
AIOps effectively curates big operational data collected through SD-WAN analytics to correlate events, provide insights across the WAN, and automatically fix issues that simplify operations. However, some organizations are taking a cautious approach to implementing AIOps due to challenges in measuring its values and realizing its benefits. AIOps adoption is expanding, but infrastructure leaders are looking for a strategic approach that ensures tangible business results.
Infrastructure leaders need to understand that the correct approach to implementing AIOps for SD-WAN will deliver the following benefits:
- Improve observability by automatically monitoring the network and proactively identifying critical events, so IT teams notice problems immediately and act quickly. AI and machine learning (ML) are used to correlate thousands of events and data to provide meaningful insights into network and application performance issues. Innovative deep neural network models and Seq2Seq ML models help with forecasting and detection of anomalies, which are otherwise difficult for humans to identify.
- Provide interpretable assessments with pattern discovery that identifies root cause analysis of network and application performance issues along with recommendations to proactively resolve them.
- Offer proactive remediation taking advantage of the data of topological changes, the configuration of commercial policies and access controls to automatically remedy with the most relevant solutions of the multiple possibilities that may exist.
- Empowering conversational AI with SD-WAN chatbot based on natural language understanding (NLU) for our clients. This virtual assistant helps customers quickly provide information on network and application issues, significantly reducing and resolving user-generated IT tickets, in an easy-to-use interface.
Focus on business results
SD-WAN solutions they have expanded visibility into performance, user experience, network connectivity, and business continuity, leading to an explosion of data. There is no point in constantly relying on human interventions for insights by analyzing thousands of data points or events that have led to the rapid adoption of AIOps. At the same time, there are concerns about the accuracy of the results of the AI systems, since most of these derivations are hidden. Infrastructure leaders must implement an incremental approach that allows them to leverage AIOps and interpret insights to provide root cause analysis that leads to a shutdown of these systems. They should consider the following steps to reliably implement AIOps for SD-WAN, including:
- Discover critical network/topology changes by tracking the products, applications, and users of the branch infrastructure to create a topological representation of the network and identify critical changes that negatively affect performance and alert users. Use ML-based baselines and identify anomalies.
- Leverage deep insights with predictive analytics by processing large volumes of data and events to proactively identify incidents and predict problems that enable IT staff to proactively resolve issues.
- Use guided recommendations for faster resolution continually learning and refining knowledge to provide step-by-step guidance to administrators to resolve issues and provide confirmation that the issue has been resolved.
- Allow automatic correction adjusting application SLAs and business policies with proven incident and response assessments to automatically trigger problem resolution.
Understand the building blocks of AIOps for SD-WAN
an effective AIOps for SD-WAN harnesses meaningful data to provide explainable and interpretable results. Understanding how and what data AIOps ingests and the techniques in place to analyze that data to produce actionable results is essential. Here are some of the building blocks that ensure the right approach to implementing AIOps for SD-WAN:
- data lake – Collected in real time and historical, AIOps must be able to analyze large volumes of data collected through multiple sources, including applications, networks and users. This data is then cleaned, correlated, optimized and tracked, necessary for deeper and more meaningful insights.
- Deep neural network and machine learning – Implement pattern recognition and prediction with DNN, supervised and unsupervised machine learning models to enable baseline, anomaly detection, root cause analysis, and network topology discovery to provide analytics predictive, guided and automated repair.
- centralized dashboard IT staff should easily consume AIOps-enabled insights and analytics from a centralized console that provides SD-WAN analytics. Without such an integrated offering, AIOps becomes another siled automation tool irrelevant to SD-WAN analytics-based troubleshooting and resolution.
The future is AIOps
Organizations are under pressure to improve performance and deliver an exceptional user experience for applications. A predictive SD-WAN solution that leverages AIOps can help organizations automatically troubleshoot, improve user experience, and improve business outcomes.
As the leader in offering AIOps for SD-WAN, we at Palo Alto Networks understand our customers’ requirements and are innovating on our customers’ behalf to simplify tedious network operations. join us for SASE 2022 convergence by Palo Alto Networks, the industry’s leading SASE conference. In this exclusive two-day virtual summit, you’ll hear from the brightest minds as they define the future of SD-WAN, Zero Trust Network Access, and SASE.
Sutapa Bansal is director of product management at Palo Alto Networks. She is customer obsessed and has experience in leading AI/ML and cloud products.