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This video is part of the appearance, “cPacket Presents at Networking Field Day 38“. It was recorded as part of Networking Field Day 38 at 8:00-9:30 on July 10, 2025.
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Real-time video environments demand precision and speed. Troubleshooting can’t wait for decoding or downstream analysis. In this session, cPacket explored how packet-level observability enables immediate detection of transport-layer issues like encoder faults, fiber/switch errors, and edge-to-cloud latency disruptions. They demonstrated how their observability solution, with real-time alerts, dynamic dashboards, and ServiceNow integration, empowers proactive monitoring and MTTR (Mean Time To Resolution) reduction across complex, long-path video delivery networks. Erik Rudin, Field CTO, introduced the scenario of live video streaming, emphasizing the critical importance of video quality for businesses. Ron Nevo, CTO, further detailed the intricate environment of live streaming, involving multiple cameras, production vans, cloud processing, transcoding, and distribution, all of which can introduce potential points of failure.
The core of cPacket’s approach is to deploy monitoring points throughout the video delivery path to quickly determine if an issue is network-related. For real-time video, the presence of even minimal packet loss is a clear indicator of a problem. cPacket’s solution continuously analyzes RTP (Real-time Transport Protocol) streams, triggering real-time alerts (e.g., via Slack) when packet loss increases. These alerts provide direct links to detailed analytics, allowing operators to pinpoint the exact location and nature of the fault, whether it’s a physical cable issue, a video machine problem, or a cloud link disruption. Furthermore, the system automatically creates tickets in existing IT service management tools like ServiceNow, ensuring that identified issues are integrated into the customer’s operational workflows for prompt resolution.
This use case exemplifies cPacket’s broader strategy for service assurance, focusing on delivering actionable insights rather than just raw data. By acquiring and contextualizing packet data at line rate, integrating it into existing ecosystems, and leveraging AI for anomaly detection, cPacket aims to proactively identify and prevent service degradations. The emphasis is on improving the triage process and providing measurable outcomes, such as reduced MTTR and improved customer experience. The session underscored that AI serves as an augmentation to existing analytics, enhancing the ability to predict and prevent outages by identifying subtle patterns like under/overutilized links and their correlation to service degradation or security concerns.
Personnel: Erik Rudin, Ron Nevo