Collecting data for analyzing and troubleshooting has always been an
important aspect in monitoring the health of a network.
IOS XR provides several mechanisms such as SNMP, CLI and Syslog to
collect data from a network. These mechanisms have limitations that restrict
automation and scale. One limitation is the use of the pull model, where the
initial request for data from network elements originates from the client. The
pull model does not scale when there is more than one network management
station (NMS) in the network. With this model, the server sends data only when
clients request it. To initiate such requests, continual manual intervention is
required. This continual manual intervention makes the pull model inefficient.
Network state indicators, network statistics, and critical
infrastructure information are exposed to the application layer, where they are
used to enhance operational performance and to reduce troubleshooting time. A
push model uses this capability to continuously stream data out of the network
and notify the client. Telemetry enables the push model, which provides
near-real-time access to monitoring data.
Streaming telemetry provides a mechanism to select data of interest from
IOS XR routers and to transmit it in a structured format to remote management
stations for monitoring. This mechanism enables automatic tuning of the network
based on real-time data, which is crucial for its seamless operation. The finer
granularity and higher frequency of data available through telemetry enables
better performance monitoring and therefore, better troubleshooting. It helps a
more service-efficient bandwidth utilization, link utilization, risk assessment
and control, remote monitoring and scalability. Streaming telemetry, thus,
converts the monitoring process into a Big Data proposition that enables the
rapid extraction and analysis of massive data sets to improve decision-making.