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Explore how artificial intelligence transforms network packet analysis in this 27-minute conference presentation that addresses the growing challenges of analyzing network traffic as networks become more complex and faster. Learn practical approaches to automating anomaly detection, accelerating root cause identification, and uncovering hidden patterns in network traffic that traditional methods might miss. Discover how AI integrates into existing troubleshooting workflows, understand its reliability boundaries, and gain insights into when to trust AI findings versus when additional validation is necessary. Examine VIAVI's approach to managing large PCAP files through strategic data capture techniques, including Wireshark's rolling capture methods and identifying optimal capture points for multi-tier applications. Understand how machine learning scores network performance to identify problem domains before narrowing analysis to specific socket connections. See demonstrations of end-user experience (EUE) scoring methods that categorize network inefficiencies as network, client, application, or server-related issues, along with application dependency mapping for visualizing service architecture. Learn best practices for capturing only necessary data, filtering irrelevant information, and exporting manageable PCAP files for detailed analysis in tools like Wireshark. Address the unique challenges of data capture in cloud environments across AWS, Azure, and Google Cloud platforms, with emphasis on reliable capture methodologies that streamline troubleshooting processes for network engineers, security analysts, and performance specialists.
