Additionally, no free tools are obtainable that supply a simple solution for mining Netconf data for network monitoring purpose. An easy, open solution for gathering, normalizing and envisioning all Junos KPIs would be a major step ahead for these clients. This is how the open-source universal collector (OpenNTI) was born.

Open-Source Collector

OpenNTI is bundled as a set of Docker containers, which are able to be run on any operational system supporting Docker.

Each container is constructed in a ?plug-and-play? manner. Everything has been preconfigured so that the user only has to initiate the container and stream data to it; collected information can then be shown in preconfigured user dashboards. Trivial information input is needed only if you want to collect information utilizing Netconf over SSH.

OpenNTI supports numerous streaming formats:

ÿÿ JTI: GPB/UDP (for the MX Series routing platforms)

ÿÿ AnalyticsD: JSON/UDP (for the QFX Series switching platforms)

Differentiated UDP ports are utilized to obtain streamed information from differentiated device types; each UDP port is related to a Fluentd input plugin. Normalized information is created once the information has been processed by the FluentD input plugin. A preconfigured FluentD output plugin writes structured information as a time-series into the InfluxDB database. This super flexible architecture permits users to simply interchange an output plugin if needed. For instance, a differentiated database can be replaced, as a substitute of InfluxDB.

KPI values, which are not obtainable through the JTI interface, can be demanded from time to time by utilizing Netconf over SSH.

The Grafana UI is preconfigured with dashboards for both the Data Streaming Collector and Data Collection Agent.

The same Docker container admits syslog happenings, which are saved to the same InfluxDB. This permits significant events (such as modifications in the configuration or error syslogs) to be overlaid on all graphs in a shared dashboard.

OpenNTI supplies a simple method to gather, normalize and envision every KPI obtainable in Junos. While not an endorsed Juniper product, Juniper is making this tool obtainable to the Juniper Networks user community as an open source project.

To help grow its utilization, Juniper has also created a sequence of short ?how to? videos which can assist with an overview of OpenNTI.

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The Simple Network Management Protocol (SNMP)?established and simple to utilize?still is the principal monitoring protocol for networking equipment. Nevertheless, as technology advances, SNMP is presenting some restrictions in tackling today?s most persistent problems. For example:

ÿÿ Pull vs. push: SNMP utilizes a ?pull? model where servers begin data collection on a set schedule?for example every 2 minutes. Nevertheless, a lot can occur in 2 minutes, and a plethora of data?like traffic drops caused by bottlenecks?can be squandered if polling isn?t performed more regularly. A ?push? model which reports distinguished happenings or data, initiated by pre-set starting points, activities or happenings, is desirable.

ÿÿ Not all data is obtainable in SNMP: While fifteen years ago SNMP supplied the most perceptibility into and data about network equipment, that is no more the situation. Modern network operating systems incorporate feature rich APIs which allow users to organize their equipment to access all kinds of data in a programmatic manner. As an example, on Juniper equipment which is running the Junos OS, Netconf and Yang data models can be utilized to gather information at smaller intermissions, focused by happenings, and by using streaming technology ? which is not possible with old-style SNMP.

The capability to collect statistical information at scale is mainly due to Junos Telemetry Interface (JTI), a super scalable distributed collection engine developed to assist network operators stream statistics and event states to data collectors, network controllers, or comparable equipment for real-time or post investigation.

JTI utilizes a ?push? client which subscribes itself to key performance indicators or KPIs (like interface data) which are streamed straight from a device. The data format can be binary (e.g. Google Protobuf) or JSON based. In certain platforms, statistics streaming is performed completely in hardware to ensure the optimum general system performance.

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