Online videos now make up more than 82% of all consumer internet traffic — 15 times higher than it was in 2017 — (Cisco).  If there is one thing that can overwhelm your network performance, it’s video; unless you plan for it, and ensure your video quality monitoring capabilities allows your customers to play streaming services, anytime and anywhere, with the best viewing experience.

YouTube viewers watch over one billion hours of videos on its platform every single day and are responsible for generating billions and billions of views (YouTube, 2021).

With the rapid evolution of networks to video content, extremely complex network monitoring requirements will become more the norm, requiring substantial improvements in network performance management for video. A successful video quality monitoring solution surveils all the transition points, reports status, and notify on issues that occur to identify video quality issues root causes, to reduce MTTR quickly resolve them before the end customers get affected.


The next generation network performance monitoring tools for video use streaming analytics based on the packets for each session to uncover real-time insights. 

Streaming analytics detects equipment failure, detect anomalies, and unusual traffic patterns.  Combine that with Machine Learning (ML) capabilities enhances accuracy, user experience, efficiency, and capabilities.

YouTube viewers watch over one billion hours of videos every single day.


The evolution of infrastructure and services places greater demand on monitoring and operations to adapt to a more complex ecosystem.

Approaches used in the past fall short of fulfilling the needs of modern networks with increased bandwidth and more use of encryption. These factors complicate the ability to leverage Deep Packet Inspection and other techniques.

New techniques are required to find efficiencies in understanding traffic patterns and to also gain visibility into cloud infrastructure. Customers expect high Quality of Service (QoS) and Quality of Experience (QoE), and continuous network monitoring and real-time analytics as a means of ensuring consistent network excellence for your end users.


Machine Learning (ML) techniques can establish a baseline traffic pattern and continuously compare that to real-time traffic. The allows for the discovery of anomalies that would otherwise be missed. Legacy methodologies without ML can overlook these patterns.

Using correlation between metrics across different points throughout the network — such as packet data, flow data, SNMP utilization metrics, and syslog — helps produces a complete view of the network. A ML-driven approach can reliably detect anomalies in real-time, thereby minimizing the time to resolution and avoiding disruptions that degrade QoE for end users.


Examine your existing network performance system for video to determine whether their evolution path includes AI, ML, and workflows.  Obtain concrete evidence that these will happen in the timeframe you need to evolve and manage your network.

Consider a solution that is universal and can manage multitudes of networks as well as video.

The right software will absorb the data, eliminate duplicates, discard irrelevant bits, correlate the needed data, examine the packet data in real-time, send alerts when the data contains an unusual event, automatically investigate using workflows, and then store ALL the data in a time-series database for longer term event analysis to discover dubious or abnormal behavior and assist in network growth planning.