To monitor QoS and assure quality of experience (QoE) for a mobile subscriber, you need complete visibility into all of a subscriber’s data sessions. QoS and QoE monitoring use probes that generate relevant key performance indicators (KPIs) based on subscriber traffic. Probes, however, are not designed to correlate session traffic. With their resources dedicated to other functions, probes are limited in the number of subscribers, throughput, and packets per second (pps) they can support.

With GTP/SIP session correlation, you can recreate a subscriber's data session by tapping the various control and data plane interfaces and directing all traffic belonging to a given user to the same monitoring probe. Offloading correlation to Vision X, Vision ONE, GTP Session Controller (GSC) or CloudLens can free up probe resources by up to 50% — allowing you to scale out your monitoring infrastructure.

To scale monitoring infrastructure, you can use load balancing to ensure network probes get fairly distributed traffic or sessions. This distribution of traffic ensures multiple probes can split the load of monitoring QoS without exceeding a given probe's capacity.

Load balancing, coupled with GTP / SIP correlation, can apply to more than just throughput. You can load balance on the number of supported subscribers per probe or by packets per second (pps), parameters which are often exceeded long before throughput is.

Subscriber-aware filtering enables two crucial use cases for operators. First, you can use it as a debugging technique. You can filter data from users who are using new features for selective analysis. Second, you can use it with subscriber sampling to ensure always-on monitoring of high value subscribers.

You can get the same globally relevant statistics without losing visibility for key customers. This allows flexibility in ensuring visibility of user experience by different subscriber segments. It reduces costs associated with infrastructure while allowing an operator to provide customer's with different service level agreements.

You can identify subscribers by IMSI (International Mobile Subscriber Identity) or IMEI (International Mobile Equipment Identity).

Reduce costs by sending only a certain percentage of the subscriber traffic to the monitoring infrastructure. Help improve throughput and limit load on tools, while still getting the insight needed to manage QoS and monitoring quality of experience (QoE).

If you select representative sample of data is selected, the variation in Probe KPIs (for example, the percentage of calls dropped over a given period) will not be statistically significant.

You can use this feature with subscriber-aware filtering to provide allow list capabilities for high value customers that are never sampled out.

Reduce monitoring costs by selectively sending traffic to probes based on traffic type. For example, you could send only voice over IP/voice over LTE (VoLTE) traffic or 4G/5G traffic to probes, but not 2G or 3G. This makes the monitoring infrastructure more efficient, allowing faster resolution of issues based on traffic types.

Parameters for filtering include:

• 5G parameters SUPI, NSSAI, QFI, PEI, DNN
• radio access technology (RAT) – 2G, 3G, 4G 5G
  
• bearer QoS class identifier (QCI), which can distinguish between regular data, voice and video conferencing
• access point name (APN), which can further be used for differentiating between the data APN and VoLTE IMS for example)