Best practices for network monitoring

In network monitoring, New Relic utilizes agents to collect network telemetry data, which can be used to monitor network performance, identify bottlenecks, and troubleshoot problems. Learn best practices for deploying the network monitoring agent based on your system architecture and deployment.

Deployment considerations

This guide references a common networking architecture with the following requirements:

SNMP polling and SNMP Traps collection.
Syslog collection from network devices.
Network Flow collection in NetFlow v5, NetFlow v9, IPFIX, and sFlow protocols.
Support for multiple sites separated by a geographically large distance.

Architectural considerations

An agent's task

The tasks of individual agents will determine the design of your network deployment. Here are some basic rules to follow:

Agents that collect SNMP data can also receive SNMP traps by default.
Agents that receive Syslog data should run on their own.
Agents that receive network flow data may need to be isolated based on the type of flow template being collected.

Network Flow and Syslog agents

When using Network Flow and Syslog agents, you do not need a custom configuration file. The default settings from the agent will work fine because all of the agent settings are passed at runtime via flags.

However, if you do not provide a configuration file with entries in the devices section, the results sent to New Relic APIs will use a device_name resolved via DNS from the IP address in the respective packet. You can provide a custom DNS server location at runtime, but for full control with tagging, you should provide your own entries in the devices section with the flow_only setting set to true. This is what administrators generally want to do so that the name sent to New Relic APIs aligns with the name sent in from SNMP polling the same device.

Geography

Due to the downgrade of their priority in modern networks, SNMP and ICMP (ping) protocols can be affected by extended latency in round trip times. To prevent failed polling scenarios due to timeouts, agents should be created close to their target devices.

Compute scale

Individual agents are usually hosted on very small hosts and have minimal requirements as outlined in KTranslate container requirements. However, in heavy polling scenarios, you may need to scale the host's CPU. The primary reason for scaling to a larger CPU footprint for a agent is the amount of load presented to the task. In these situations, it is typically better to run multiple agents to load balance instead of increasing the total size of your host, which has cost implications.

Common architecture example

This diagram reflects a sample networking architecture with the following:

Each geographical location has its own local agents used to collect and forward data to New Relic:
- DC_01 (AMER):
  - Three agents on one host serving the DC_01 location in New York City.
  - Containers process SNMP polling, NetFlow v5 collection, and Syslog collection
  - Consideration: This host conaints a Class B private subnet (/16). To ensure that the job has time to complete, the discovery targets should be split up into manageable subnet sizes.
- OFFICE_01 (APJ):
  - One agent on one host serving the OFFICE_01 location in Sydney, Australia.
  - Container processes SNMP polling and SNMP Trap collection with discovery against a /24 subnet.
- DC_02 (EMEA):
  - Three agents on one host serving the DC_02 location in Dublin, Ireland.
  - Containers process NetFlow v9, IPFIX, and sFlow collection, each targeting a different listening port on the host.
  - Consideration: This host has an even larger Class A private subnet (/8), but there is no need to scale the jobs since there is no need for discovery in this location. Depending on the flows-per-second, there may be a need to scale out into additional agents in the future.

Maintaining your deployment

After initial installation, the network monitoring observability footprint can be maintained using various techniques. These include integrating configuration file changes with tools like Ansible, and building GitOps pipelines around the architecture to support versioning and "guest" options where external teams can submit changes for review.

The most common need for ongoing maintenance is to keep the list of target devices accurate. This can be done using three main discovery methods:

Automatic discovery is the process used by the KTranslate agent to scan a target list of IP addresses and/or ranges, perform a liveness probe, and then run a basic SNMP walk of the MIB-2 System MIB to attempt matching the device to a known SNMP profile.

The agent has embedded agent runtime flags (-snmp_discovery_min and -snmp_discovery_on_start) that allow you to build a schedule of recurring SNMP discovery events. This automates discovery jobs against the targets from the discovery section in the configuration file and then automatically updates the file with new devices and refreshes the service to accept the changes.

Pros

Hands-off discovery for known IP ranges and SNMP community strings.
Automated correlation to the proper SNMP profile for each device.
Safety mechanisms are in place to prevent improper settings that could break your configuration file.

Cons

Requires a pre-existing target list of IP addresses and SNMP community strings/V3 authentication in the discovery section of the configuration file.
Large subnets are at risk of timeouts (we recommend /16 and smaller).
Teams that make use of device-specific user_tags in their configuration files will have extra work to ensure new devices have their tags updated.

Example

This is the native configuration pattern found if you follow the guided installation through the New Relic UI:

devices: {}
trap:
  listen: '0.0.0.0:1620'
discovery:
  cidrs:
      - 192.168.0.0/24
  ignore_list: []
  debug: false
  ports:
      - 161
  default_communities:
      - public
  default_v3: null
  add_devices: true
  add_mibs: true
  threads: 4
  replace_devices: true
  check_all_ips: true
  use_snmp_v1: false
global:
  poll_time_sec: 300
  mib_profile_dir: /etc/ktranslate/profiles
  mibs_enabled:
      - IF-MIB
  timeout_ms: 3000
  retries: 0

Your associated Docker run command would look like this, replacing $UNIQUE_NAME, $YOUR_NR_LICENSE_KEY and $YOUR_NR_ACCOUNT_ID:

bash

$docker run -d --name ktranslate-$UNIQUE_NAME --restart unless-stopped --pull=always -p 162:1620/udp \
>  -v `pwd`/snmp-base.yaml:/snmp-base.yaml \
>  -e NEW_RELIC_API_KEY=$YOUR_NR_LICENSE_KEY \
>kentik/ktranslate:v2 \
>  -snmp /snmp-base.yaml \
>  -nr_account_id=$YOUR_NR_ACCOUNT_ID \
>  -metrics=jchf \
>  -tee_logs=true \
>  -service_name=$UNIQUE_NAME \
>  -snmp_discovery_on_start=true \
>  -snmp_discovery_min=180 \
>  nr1.snmp

Manual discovery uses the same mechanism as automated discovery, but it gives you more control. With manual discovery, you can run a bespoke agent ad hoc, which means that you can run it whenever you want and you can review and manipulate the results as needed. This is the preferred method for environments where tagging is prevalent or where there is a good amount of control from a centralized team adding new devices to the network. This reduces the need for full subnet scanning, which can be time-consuming and disruptive.

Pros

Full control over the targets and results, including tag decoration.
Helps to prevent possible devices that are not in scope for your monitoring footprint.
Automated correlation to the proper SNMP profile for each device.
Safety mechanisms are in place to prevent improper settings that could break your configuration file.

Cons

An administrator must run the agent on demand, and from the same host that your production agent runs on to ensure network/SNMP connectivity is tested properly.
Moving the results from the discovery into the production configuration file is a manual process that requires a restart of the production agent in order to load the new settings.

Example

This discovery method follows the original deployment option for KTranslate agents. At a high level, the discovery process is:

Pull the latest version of the agent image to your local machine.
Copy the sample snmp-base.yaml configuration file from the image to your local machine.
Edit the configuration file to update the discovery section with the settings you need for cidrs and default_communities.
Launch a short-lived agent executing an ad hoc discovery job.
Edit any changes needed to the resulting devices in your configuration file.
Copy the new devices from your discovery configuration file into the production agent configuration file.
Restart your production agent to load the new settings.

To use this method, follow steps on Manual container setup.

The last option is to skip the entire discovery process and manually add devices directly into the production configuration file. In practice, it is fairly rare to see this pattern in use as the standard discovery options automatically match devices to their profiles, and ensure that your configuration file is formatted correctly.

Pros

Full control over the configuration of devices and their tag decorations.

Cons

Medium risk of misconfiguration in the settings. This method requires that you know the System Object Identifier (SysOID) of the device as well as understand the profile the device would target so you can identify which MIBs you want enabled (all of this is automated in discovery).
Still requires a restart of the production agent to load the new settings.

Example

Here's an example of the device settings needed to successfully monitor an APC UPS:

devices:
  ups_snmpv2c__10.10.0.201:
    device_name: ups_snmpv2c
    device_ip: 10.10.0.201
    snmp_comm: public
    oid: .1.3.6.1.4.1.318.1.3.27
    mib_profile: apc_ups.yml
    provider: kentik-ups
    user_tags:
      owning_team: dc_ops
...
global:
...
  mibs_enabled:
    - IF-MIB
    - PowerNet-MIB_UPS
    - UPS-MIB

Important

global.mibs_enabled must be updated in order to start polling a MIB. When adding devices, you need to ensure this setting is updated with a list of distinct MIB names found throughout the associated SNMP profiles.

Required settings are outlined in detail in our documentation for devices and global blocks.

Deployment considerations .css-21sua1{background:none;border:none;width:0;padding:0;}

Architectural considerations

An agent's task

Network Flow and Syslog agents

Geography

Compute scale

Common architecture example

Maintaining your deployment

Pros

Cons

Example

Pros

Cons

Example

Pros

Cons

Example

Important

Deployment considerations