Tracing network packets with eBPF and pwru

pwru (packet, where are you?) is an open source tool from Cilium that used eBPF instrumentation in recent Linux kernels to trace network packets through the kernel.

In this article we will use Multipass to create a virtual machine to experiment with pwru. Multipass is a command line tool for running Ubuntu virtual machines on Mac or Windows. Multipass uses the native virtualization capabilities of the host operating system to simplify the creation of virtual machines.

multipass launch --name=ebpf noble
multipass exec ebpf -- sudo apt update
multipass exec ebpf -- sudo apt -y install git clang llvm make libbpf-dev flex bison golang
multipass exec ebpf -- git clone https://github.com/cilium/pwru.git
multipass exec ebpf --working-directory pwru -- make
multipass exec ebpf -- sudo ./pwru/pwru -h

Run the commands above to create the virtual machine and build pwru from sources.

multipass exec ebpf -- sudo ./pwru/pwru port https

Run pwru to trace https traffic on the virtual machine.

multipass exec ebpf -- curl https://sflow-rt.com

In a second window, run the above command to generate an https request from the virtual machine.

SKB                CPU PROCESS          NETNS      MARK/x        IFACE       PROTO  MTU   LEN   TUPLE FUNC
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0               0         0x0000 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) ip_local_out
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0               0         0x0000 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) __ip_local_out
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0               0         0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) ip_output
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) nf_hook_slow
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) apparmor_ip_postroute
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) ip_finish_output
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) __ip_finish_output
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) ip_finish_output2
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) neigh_resolve_output
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) eth_header
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  60    192.168.66.3:47460->54.190.130.38:443(tcp) skb_push
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) __dev_queue_xmit
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) qdisc_pkt_len_init
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) netdev_core_pick_tx
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) sch_direct_xmit
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) validate_xmit_skb_list
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) validate_xmit_skb
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) netif_skb_features
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) passthru_features_check
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) skb_network_protocol
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) skb_csum_hwoffload_help
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) skb_checksum_help
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) skb_ensure_writable
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) validate_xmit_xfrm
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) dev_hard_start_xmit
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) start_xmit
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) skb_clone_tx_timestamp
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  74    192.168.66.3:47460->54.190.130.38:443(tcp) xmit_skb
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  86    192.168.66.3:47460->54.190.130.38:443(tcp) skb_to_sgvec
0xffff9fc40335a0e8 0   ~r/bin/curl:8966 4026531840 0             ens3:2      0x0800 1500  86    192.168.66.3:47460->54.190.130.38:443(tcp) __skb_to_sgvec

The pwru output provides a detailed trace of each packet through the Linux stack, identifying each application, namespace, interface and kernel module traversed by the packet. Type Ctrl+C to stop the trace.

multipass exec ebpf -- sudo ufw default allow
multipass exec ebpf -- sudo ufw deny out to any port https
multipass exec ebpf -- sudo ufw enable

Now create a firewall rule to block outgoing https traffic and repeat the test

SKB                CPU PROCESS          NETNS      MARK/x        IFACE       PROTO  MTU   LEN   TUPLE FUNC
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0000 1500  60    192.168.67.5:38932->54.190.130.38:443(tcp) ip_local_out
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0000 1500  60    192.168.67.5:38932->54.190.130.38:443(tcp) __ip_local_out
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0800 1500  60    192.168.67.5:38932->54.190.130.38:443(tcp) nf_hook_slow
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0800 1500  60    192.168.67.5:38932->54.190.130.38:443(tcp) kfree_skb_reason(SKB_DROP_REASON_NETFILTER_DROP)
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0800 1500  60    192.168.67.5:38932->54.190.130.38:443(tcp) skb_release_head_state
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0800 0     60    192.168.67.5:38932->54.190.130.38:443(tcp) tcp_wfree
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0800 0     60    192.168.67.5:38932->54.190.130.38:443(tcp) skb_release_data
0xffff970bc5f568e8 0   ~r/bin/curl:7138 4026531840 0               0         0x0800 0     60    192.168.67.5:38932->54.190.130.38:443(tcp) kfree_skbmem

This time, the curl command hangs and the pwru trace shows that packets are being dropped in the Linux firewall.

multipass exec ebpf -- sudo ufw disable

Disable the firewall.

There are additional multipass commands available to manage the virtual machine.

multipass shell ebpf

Connect to the virtual machine and access a command shell.

multipass stop ebpf

Stop the virtual machine.

multipass start ebpf

Start the virtual machine.

multipass delete ebpf
multipass purge

Delete the virtual machine.

AI Metrics with InfluxDB Cloud

The InfluxDB AI Metrics dashboard shown above tracks performance metrics for AI/ML RoCEv2 network traffic, for example, large scale CUDA compute tasks using NVIDIA Collective Communication Library (NCCL) operations for inter-GPU communications: AllReduce, Broadcast, Reduce, AllGather, and ReduceScatter.

The metrics include:

• Total Traffic Total traffic entering fabric
• Operations Total RoCEv2 operations broken out by type
• Core Link Traffic Histogram of load on fabric links
• Edge Link Traffic Histogram of load on access ports
• RDMA Operations Total RDMA operations
• RDMA Bytes Average RDMA operation size
• Credits Average number of credits in RoCEv2 acknowledgements
• Period Detected period of compute / exchange activity on fabric (in this case just over 0.5 seconds)
• Congestion Total ECN / CNP congestion messages
• Errors Total ingress / egress errors
• Discards Total ingress / egress discards
• Drop Reasons Packet drop reasons

This article shows how to integrate with InfluxDB Cloud instead of running the services locally.

Note: InfluxDB Cloud has a free service tier that can be used to test this example.

Save the following compose.yml file on a system running Docker.

configs:
  config.telegraf:
    content: |
      [agent]
        interval = '15s'
        round_interval = true
        omit_hostname = true
      [[outputs.influxdb_v2]]
        urls = ['https://<INFLUXDB_CLOUD_INSTANCE>.cloud2.influxdata.com']
        token = '<INFLUXDB_CLOUD_TOKEN>'
        organization = '<INFLUXDB_CLOUD_USER>'
        bucket = 'sflow'
      [[inputs.prometheus]]
        urls = ['http://sflow-rt:8008/app/ai-metrics/scripts/metrics.js/prometheus/txt']
        metric_version = 1

networks:

  monitoring:
    driver: bridge

services:

  sflow-rt:
    image: sflow/ai-metrics
    container_name: sflow-rt
    restart: unless-stopped
    ports:
      - '6343:6343/udp'
      - '8008:8008'
    networks:
      - monitoring

  telegraf:
    image: telegraf:alpine
    container_name: telegraf
    restart: unless-stopped
    configs:
      - source: config.telegraf
        target: /etc/telegraf/telegraf.conf
    depends_on:
      - sflow-rt
    networks:
      - monitoring

Use the Load Data menu to create an sflow bucket, create an API TOKEN to upload data, and find TELEGRAF INFLUXDB OUTPUT PLUGIN settings. Navigate to the Dashboards menu and create a new dashboard by importing ai_metrics.json

Edit the highlighted outputs.influxdb_v2 telegraf settings (INFLUXDB_CLOUD_INSTANCE, INFLUXDB_CLOUD_TOKEN and INFLUXDB_CLOUD_USER) to match those provided by InfluxDB Cloud.

docker compose up -d

Run the command above to start streaming metrics to InfluxDB Cloud.

Enable sFlow on all switches in the cluster (leaf and spine) using the recommeded settings. Enable sFlow dropped packet notifications to populate the drop reasons metric, see Dropped packet notifications with Arista Networks, NVIDIA Cumulus Linux 5.11 for AI / ML and Dropped packet notifications with Cisco 8000 Series Routers for examples.

Note: Tuning Performance describes how to optimize settings for very large clusters.

Industry standard sFlow telemetry is uniquely suited monitoring AI workloads. The sFlow agents leverage instrumentation built into switch ASICs to stream randomly sampled packet headers and metadata in real-time. Sampling provides a scaleable method of monitoring the large numbers of 400G/800G links found in AI fabrics. Export of packet headers allows the sFlow collector to decode the InfiniBand Base Transport headers to extract operations and RDMA metrics. The Dropped Packet extension uses Mirror-on-Drop (MoD) / What Just Happened (WJH) capabilities in the ASIC to include packet header, location, and reason for EVERY dropped packet in the fabric.

Talk to your switch vendor about their plans to support the Transit delay and queueing extension. This extension provides visibility into queue depth and switch transit delay using instrumentation built into the ASIC.

A network topology is required to generate the analytics, see Topology for a description of the JSON file and instructions for generating topologies from Graphvis DOT format, NVIDIA NetQ, Arista eAPI, and NetBox.

Use the Topology Status dashboard to verify that the topology is consistent with the sFlow telemetry and fully monitored. The Locate tab can be used to locate network addresses to access switch ports.

Note: If any gauges indicate an error, click on the gauge to get specific details.

Congratulations! The configuration is now complete and you should see charts above in AI Metric application Traffic tab. In addition, the AI Metrics dashboard at the top of this page should start to populate with data.

Getting Started provides an introductions to sFlow-RT, describes how to browse metrics and traffic flows using tools included in the Docker image, and links to information on creating applications using sFlow-RT APIs.