VyOS 1.4 LTS released

Protectli Vault - 4 Port

The VyOS 1.4.0 (Sagitta) LTS release announcement is exciting news! VyOS is an open source router operating system based on Linux that can be installed on commodity PC hardware - for optimal performance at least 1GB RAM and 4GB of storage space is recommended.

The new 1.4 LTS release includes a significantly enhanced implementation of industry standard sFlow telemetry based on the open source Host sFlow agent.

set system sflow interface eth0
set system sflow interface eth1
set system sflow interface eth2
set system sflow interface eth3
set system sflow polling 30
set system sflow sampling-rate 1000
set system sflow drop-monitor-limit 50
set system sflow server 192.0.2.100

Enter the commands above to enable sFlow monitoring on interfaces eth0, eth1, eth2, and eth3. Interface counters will be exported every 30 seconds, packets will be sampled with probability 1/1000, and up to 50 packet headers (and drop reasons) per second will collected from packets dropped by the router. The sFlow telemetry stream will be sent to an sFlow collector at 192.0.2.100.

Running Docker on the sFlow collector makes it easy to run a variety of sFlow analytics tools.

docker run --rm -p 6343:6343/udp sflow/sflowtool

Run the sflow/sflowtool image to decode and print the contents of the sFlow telemetry stream and verify receipt of data.

docker run --rm -p 6343:6343/udp sflow/tcpdump tcp port 80

Run the sflow/tcpdump image to decode and filter sampled packet headers. For more complex packet analysis tasks, try the sflow/tshark image.

Run the sflow/sflowtrend image to trend interface counters and top flows.

Deploy real-time network dashboards using Docker compose describes how to configure Prometheus and Grafana to capture time series data and create custom dashboards.

Dropped packet reason codes in VyOS describes how the new Linux kernel in VyOS 1.4 provides detailed visibility into every dropped packet (including the reason it was dropped). This cabability is used by the new sFlow agent implement the sFlow Dropped Packet Notification Structures extension to provide network-wide visibility into dropped packets.

Download VyOS today to try out the new features. Pre-built LTS images are available with paid support, but anyone can build an image from sources or download the latest rolling release.

Raspberry Pi 5 network emulation with Containerlab

The GitHub sflow-rt/containerlab project contains example network topologies for the Containerlab network emulation tool that demonstrate real-time streaming telemetry in realistic data center topologies and network configurations. The examples use the same FRRouting (FRR) engine that is part of SONiC, NVIDIA Cumulus Linux, and DENT network operating systems. Containerlab can be used to experiment before deploying solutions into production. Examples include: tracing ECMP flows in leaf and spine topologies, EVPN visibility, and automated DDoS mitigation using BGP Flowspec and RTBH controls.

Raspberry Pi 5 real-time network analytics describes how to install Docker on a Raspberry Pi 5.

docker run hello-world

Run the hello-world container to verify that Docker in properly installed and running before proceeding.

git clone https://github.com/sflow-rt/containerlab.git

Download the sflow-rt/containerlab project from GitHub.

cd containerlab
./run-clab

Start Containerlab.

containerlab deploy -t clos5.yml

Start the 5 stage leaf and spine topology shown at the top of this page. The initial launch may take a couple of minutes as the container images are downloaded for the first time. Once the images are downloaded, the topology deploys in around 10 seconds.

./topo.py clab-clos5

Push the topology to the sFlow-RT analytics software.

An instance of the sFlow-RT real-time analytics engine receives industry standard sFlow telemetry from all the switches in the network. All of the switches in the topology are configured to send sFlow to the sFlow-RT instance. In this case, Containerlab is running the pre-built sflow/clab-sflow-rt image which packages sFlow-RT with useful applications for exploring the data.

Connect to the web interface on port 8008. The sFlow-RT dashboard verifies that telemetry is being received from 10 agents (the 10 switches in the Clos fabric). See the sFlow-RT Quickstart guide for more information.

The Containerlab Dashboard (click on sFlow-RT Apps tab and containerlab-dashboard button) shows real-time dashboard displaying up to the second traffic.

docker exec -it clab-clos5-h1 iperf3 -c 172.16.4.2

Each of the hosts in the network has an iperf3 server, so running the above command will test bandwidth between h1 and h4.

docker exec -it clab-clos5-h1 iperf3 -c 2001:172:16:4::2

Generate a large IPv6 flow between h1 and h4. The traffic flows should immediately appear in the Top Flows chart. You can check the accuracy by comparing the values reported by iperf3 with those shown in the chart.

Click on the Topology tab to see a real-time weathermap of traffic flowing over the topology. See how repeated iperf3 tests take different ECMP (equal-cost multi-path) routes across the network.

docker exec -it clab-clos5-leaf1 vtysh

Linux with open source routing software (FRRouting) is an accessible alternative to vendor routing stacks (no registration / license required, no restriction on copying means you can share images on Docker Hub, no need for virtual machines). FRRouting is popular in production network operating systems (e.g. Cumulus Linux, SONiC, DENT, etc.) and the VTY shell provides an industry standard CLI for configuration, so labs built around FRR allow realistic network configurations to be explored.

docker exec -it clab-clos5-leaf1 vtysh -c "show running-config"

Use vtysh to show the running configuration on leaf1.

containerlab destroy -t clos5.yml

When you are finished, run the above command to stop the containers and free the resources associated with the emulation. Try out other topologies from the project to explore topics such as DDoS mitigation, BGP Flowspec, and EVPN.

Note: If you are building your own topologies, the Raspberry Pi 5 8G can comfortably handle topologies with up to 50 FRR/Alpine Linux nodes.

Getting Started provides an introduction to sFlow-RT analytics and APIs. Containerlab provides a useful environment for developing and testing monitoring applications for sFlow-RT before moving them into production.

Moving monitoring solutions from Containerlab to production is straightforward since sFlow is widely implemented in datacenter equipment from vendors including: A10, Arista, Aruba, Cisco, Edge-Core, Extreme, Huawei, Juniper, NEC, Netgear, Nokia, NVIDIA, Quanta, and ZTE. In addition, the open source Host sFlow agent makes it easy to extend visibility beyond the physical network into the compute infrastructure.

Raspberry Pi 5 real-time network analytics describes how to deploy an sFlow-RT, Prometheus, and Grafana monitoring stack to monitor live network traffic.

Raspberry Pi 5 real-time network analytics

CanaKit Raspberry Pi 5 Starter Kit - Aluminum

This article describes how build an inexpensive Raspberry Pi 5 based server for real-time flow analytics using industry standard sFlow streaming telemetry. Support for sFlow is widely implemented in datacenter equipment from vendors including: A10, Arista, Aruba, Cisco, Edge-Core, Extreme, Huawei, Juniper, NEC, Netgear, Nokia, NVIDIA, Quanta, and ZTE.

In this example, we will use an 8G Raspberry Pi 5 running Raspberry Pi OS Lite (64-bit).  The easiest way to format a memory card and install the operating system is to use the Raspberry Pi Imager (shown above).

Click on EDIT SETTINGS button to customize the installation.

Set a hostname, username, and password.

Click on the SERVICES tab and select Enable SSH.  Click SAVE to save the settings and then YES to apply the settings and create a bootable micro SD card. These initial settings allow the Rasberry Pi to be accessed over the network without having to attach a screen, keyboard, and mouse.

ssh pp@192.168.4.170

Use ssh to log into Raspberry Pi (having installled the micro SD card).

sudo apt-get update && sudo apt-get -y upgrade

Update packages and OS to latest version.

curl -sSL https://get.docker.com | sh

Install Docker.

sudo usermod -aG docker $USER

Give permission to run Docker without sudo command. Exit ssh session and log in again to pick up the new settings.

docker run hello-world

Run the hello-world container to verify that docker in properly installed and running.

git clone https://github.com/sflow-rt/prometheus-grafana.git
cd prometheus-grafana
./start.sh

Start sFlow-RT, Prometheus, and Grafana using Docker compose.

Configure sFlow Agents embedded in switches, routers and servers to stream sFlow telemetry to the Raspberry Pi. The sFlow-RT Getting Started guide shows how to verify that sFlow is being received and includes tools flow and counter based analytics.

For example, the Flow Browser application lets you list attributes of network traffic that you are interested in and trend top flows with the attributes in real-time (up to the second). Defining Flows describes the flow analytics capability of sFlow-RT that can be explored.

Deploy real-time network dashboards using Docker compose describes how to configure Prometheus and Grafana to capture time series data and create custom dashboards.

The Raspberry Pi 5 is surprisingly capable, this pocket-sized server can easily monitor thousands of high speed (100G+) links, providing up to the second visibility into network flows. In this example, sFlow telemetry from 100 switches, each with 48 active 100G ports, was easily handled by the Raspberry Pi 5. Performance of the Prometheus database is likely to be the limiting factor given the relatively slow disk performance of the micro SD card, but could be improved adding an M.2 PCIe disk.