containerlab

Experiment with real-time network telemetry using containerlab to simulate Clos (leaf/spine) fabrics.

• Real-time telemetry from a 5 stage Clos fabric
• Topology aware fabric analytics
• Real-time EVPN fabric visibility
• Containerlab DDoS testbed
• DDoS attacks and BGP Flowspec responses
• BGP Remotely Triggered Blackhole (RTBH)
• SR Linux in Containerlab
• Real-time flow analytics with Containerlab templates
• Leaf and spine network emulation on Mac OS M1/M2 systems
• Containerlab dashboard

Get started (on a system running Docker):

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

5 Stage Clos Topology

Deploy 5 stage Clos topology:

containerlab deploy -t clos5.yml

Generate traffic between h1 and h4:

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

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

Connect to http://localhost:8008/ for analytics, see Quickstart for more information.

3 Stage Clos Topology

Deploy 3 stage Clos topology:

containerlab deploy -t clos3.yml

Generate traffic between h1 and h2:

docker exec -it clab-clos3-h1 iperf3 -c 172.16.2.2

docker exec -it clab-clos3-h1 iperf3 -c 2001:172:16:2::2

Connect to http://localhost:8008/ for analytics, see Quickstart for more information.

EVPN Topology

Deploy EVPN topology:

containerlab deploy -t evpn3.yml

Generate traffic between h1 and h2:

docker exec -it clab-evpn3-h1 iperf3 -c 172.16.10.2

docker exec -it clab-evpn3-h1 iperf3 -c 2001:172:16:10::2

Connect to http://localhost:8008/ for analytics, see Quickstart for more information.

DDoS Topology

Deploy DDOS topology:

containerlab deploy -t ddos.yml

Simulate DDoS attack against victim:

docker exec -it clab-ddos-attacker hping3 --flood --udp -k -a 198.51.100.1 -s 53 192.0.2.129

Connect to http://localhost:8008/ for analytics, see Quickstart for more information.

Nokia SR Linux

Deploy SR Linux topology:

containerlab deploy -t srlinux.yml

Simulate traffic:

docker exec -it clab-srlinux-h1 iperf3 -c 172.16.2.2

Connect to http://localhost:8008/ for analytics, see Quickstart for more information.