5G SA networks will host a wide range of applications and devices reliant on mission critical connectivity and edge computing. Keeping a clear view of the UX of each user and device–and maintaining 5G RAN and core network performance–is the new challenge for operations and engineering teams. However, only 2% think their current service assurance systems are enough. (GSMA Mobile World Live survey, 2021).
5G standalone demands new troubleshooting tools across multiple data sources for timely, actionable insight.
Sources of change
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More devices covered by SLAs
Billions of new devices, many with high performance expectations and strict service level requirements, are coming online, while network complexity makes existing troubleshooting approaches a thing of the past.
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A tsunami of data
At scale, data intensive monitoring quickly becomes cost-prohibitive. Adaptive data collection significantly cuts the processing, storage and network resources typically associated with a “keep everything just in case” approach.
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Dynamic network and service topologies
Network automation and orchestration mean that the network picture is constantly changing. Virtualized and cloud native functions that are deployed and torn down in real time make it difficult to troubleshoot issues affecting the user experience.
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Control plane and user plane separation
Service providers are putting CP and UP functions where they are most cost-effective. However, this split hampers real-time visibility, while there is a need to correlate user experience KQIs and trace every session and event.
-
More devices covered by SLAs
Billions of new devices, many with high performance expectations and strict service level requirements, are coming online, while network complexity makes existing troubleshooting approaches a thing of the past.
-
A tsunami of data
At scale, data intensive monitoring quickly becomes cost-prohibitive. Adaptive data collection significantly cuts the processing, storage and network resources typically associated with a “keep everything just in case” approach.
-
Dynamic network and service topologies
Network automation and orchestration mean that the network picture is constantly changing. Virtualized and cloud native functions that are deployed and torn down in real time make it difficult to troubleshoot issues affecting the user experience.
-
Control plane and user plane separation
Service providers are putting CP and UP functions where they are most cost-effective. However, this split hampers real-time visibility, while there is a need to correlate user experience KQIs and trace every session and event.
There is a new cloud-native approach for troubleshooting 5G standalone that offers full end-to-end visibility, that leverages adaptive data collection to reduce the need for compute, storage and network resources while promoting real-time insight, and that correlates network performance and the user experience across user and control planes with rich metrics and xDRs.
EXFO’s solutions for troubleshooting 5G standalone enable service providers to deliver on the promise of 5G.
Impacts
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Event correlation and data enrichment
Real-time correlation of events across control and user planes, network traces and raw packets, faults and telemetry. Generation of context-rich XDRs that speed analysis. Intuitive ladder diagrams and protocol decoding tools highlight critical events along the service path.
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Data ingestion across multiple sources
Ingest network function events, infrastructure observability, performance data from third party probes, and RAN session records for a complete picture of network performance and customer experience to accelerate MTTR. Stream metrics and KPIs across open interfaces to share actionable insight with existing systems.
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Adaptive probing and data capture
Orchestrator-driven cloud-native probes can be instantiated where and when they are needed. Adaptive probing and data capture reduce compute and storage resources by up to 90% while providing the xDRs, metrics and other data required to take action.
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Automated anomaly detection and root cause analysis
AI and machine learning enable service providers to automatically detect and measure customer-impacting events in real time, including knowing who is impacted, where and for how long.
-
Event correlation and data enrichment
Real-time correlation of events across control and user planes, network traces and raw packets, faults and telemetry. Generation of context-rich XDRs that speed analysis. Intuitive ladder diagrams and protocol decoding tools highlight critical events along the service path.
-
Data ingestion across multiple sources
Ingest network function events, infrastructure observability, performance data from third party probes, and RAN session records for a complete picture of network performance and customer experience to accelerate MTTR. Stream metrics and KPIs across open interfaces to share actionable insight with existing systems.
-
Adaptive probing and data capture
Orchestrator-driven cloud-native probes can be instantiated where and when they are needed. Adaptive probing and data capture reduce compute and storage resources by up to 90% while providing the xDRs, metrics and other data required to take action.
-
Automated anomaly detection and root cause analysis
AI and machine learning enable service providers to automatically detect and measure customer-impacting events in real time, including knowing who is impacted, where and for how long.
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