eBPF Solutions for Monitoring Robotics Systems

Source:https://chatgpt.com/share/67db852f-9de8-800d-927f-db93046b0d73

Got it! I will research the best open-source eBPF solutions for monitoring a robotics system running on Ubuntu with an ACRN VM, focusing on network monitoring, CPU/memory usage, and IO performance. The solution should be lightweight and capable of integrating with existing monitoring tools like Prometheus and Grafana. I'll get back to you with the most relevant options and their pros/cons.

eBPF-Based Monitoring Solutions for ROS on ACRN (Ubuntu)

Monitoring a robotics system (ROS) in an ACRN virtual machine requires tools that can capture network, CPU/memory, and I/O performance with minimal overhead. eBPF (extended Berkeley Packet Filter) is ideal for this because it runs instrumentation safely in-kernel with low overhead. For example, researchers found an eBPF-based ROS monitor incurred only ~4% of the overhead of generic ROS tools on a system with 80 processesakiannillo.github.io. Below is a comparative analysis of the top open-source eBPF monitoring solutions, their benefits, and implementation considerations – especially regarding Prometheus/Grafana integration and lightweight resource usage.

BCC (BPF Compiler Collection) – Comprehensive eBPF Toolset

What it is: BCC is a mature toolkit providing dozens of ready-made eBPF programs for performance analysis. It covers networking, I/O, CPU, and morewww.redhat.com. Examples include netproc (per-process network throughput), tcptop (top processes by TCP traffic), runqlat (CPU scheduler latency), and biolatency (block I/O latency)www.redhat.com. These tools make it easy to monitor the exact metrics you need without writing eBPF code from scratch.

Advantages:

  • Broad Coverage: BCC includes many pre-built tracing/monitoring scripts for common metrics across CPU, memory, disk, and networkwww.redhat.com. This means you can gather insights on ROS process CPU usage, memory allocations, network packet rates, or disk I/O delays using existing tools.
  • Proven and Documented: It’s one of the oldest and most widely used eBPF toolkitsdevops.com, with community support and documentation (including Brendan Gregg’s examples). Many BCC tools have been battle-tested in real environments, so they are reliable.
  • Extensible: You can also develop custom BCC scripts if needed, embedding C code for specific ROS events or metrics. BCC handles injecting the eBPF program and reading results in user-space, so you work in Python or C++ rather than dealing with raw kernel APIs. Considerations:
  • Overhead and Dependencies: BCC tools compile eBPF programs on-the-fly using LLVM/Clang and require kernel headers on the target systemwww.redhat.com. This heavy reliance on Clang/LLVM makes BCC somewhat bulky and resource-intensive at runtimedevops.com. Running many BCC scripts continuously could consume notable CPU and memory. For a lightweight footprint, you may prefer newer alternatives that avoid runtime compilation.
  • Integration with Prometheus: BCC itself doesn’t natively export Prometheus metrics – it typically prints data to stdout or a file. To integrate with Grafana/Prometheus, you can run BCC tools continuously and feed their output into exporters. One approach is using Performance Co-Pilot (PCP) or a custom wrapper to collect BCC metrics and expose them to Prometheus (discussed below). This adds some complexity compared to tools that have built-in exporters.
  • Kernel Support: Ensure the Ubuntu kernel in the ACRN VM is recent enough for eBPF (Ubuntu 18.04+ with 4.x+ kernel is usually fine). BCC will also need privileges (root access) in the VM to attach eBPF programs.

bpftrace (Dynamic eBPF Tracing Tools)

What it is: bpftrace is a high-level tracing language inspired by DTrace, which lets you write one-liners or small scripts to tap into kernel events and produce metricswww.redhat.com. It’s excellent for custom probes – for example, tracking specific ROS node functions, kernel syscalls, or message-passing events. Tools like ply are similar; ply provides a C-like scripting language for eBPF, designed for embedded systems and able to run without LLVM (it only needs libc and a modern kernel)ebpf.io. These fall under dynamic tracing tools that can be adapted on the fly.

Advantages:

  • Flexibility: With bpftrace you can script arbitrary performance probes (e.g. count context switches, trace memory allocation sizes, sniff ROS topic publication system calls). Its AWK/Python-like syntax makes it quick to iterate and tailor to your specific needswww.redhat.com. This is useful in a robotics context where you might monitor custom ROS events or debug performance issues live.
  • No Code Compilation Required (for ply): Tools like ply avoid the heavy LLVM step by generating eBPF bytecode directlyebpf.io. This makes them lightweight and suitable for resource-constrained environments. If minimizing footprint is critical, ply or similar could be used instead of bpftrace, while achieving comparable tracing capabilities.
  • Ad-hoc Diagnostics: You can run bpftrace scripts on demand to drill down into a performance problem, then stop them – very handy for one-off investigations without permanent overhead. Considerations:
  • Not a Long-Term Exporter: bpftrace is geared toward interactive use and troubleshooting rather than always-on monitoring. It doesn’t natively integrate with Prometheus or Grafana. To collect its data continuously, you’d need an external mechanism. For instance, Red Hat’s PCP can run bpftrace scripts persistently and export their variables as metricspkg.go.devwww.redhat.com, or one could use a third-party bpftrace exporter that periodically runs scripts and exposes results. These setups work, but are more involved than using a purpose-built exporter.
  • Runtime Overhead: Like BCC, bpftrace requires LLVM and kernel debug info (BTF or DWARF) to compile scripts on the hostwww.redhat.com. Each running bpftrace script spawns an engine that consumes CPU. In practice, simple bpftrace programs are fairly efficient, but complex scripts with frequent events can impact performance. This is important in a real-time robotics system – you’d want to limit trace scope or sampling rate to avoid jitter.
  • Use of ply: If you opt for ply for lower overhead, note that it’s a newer tool and might have fewer ready-made examples than bpftrace. You’ll still need to integrate its output into your monitoring pipeline (likely by custom coding or using it in conjunction with an agent).

Cloudflare eBPF Exporter (Prometheus Exporter for eBPF)

What it is: The Cloudflare ebpf_exporter is an open-source Prometheus exporter specifically designed to run eBPF programs and expose their metricsblog.cloudflare.com. Essentially, you write or configure eBPF code for the metrics you care about (or use provided examples), and ebpf_exporter will load those into the kernel and serve the results as Prometheus metrics. This tool was created to capture granular Linux performance details (like latency distributions) that aren’t available via standard /proc countersblog.cloudflare.comblog.cloudflare.com.

Advantages:

  • Direct Prometheus Integration: ebpf_exporter was built to “get metrics into Prometheus where they belong”blog.cloudflare.com. It exposes a HTTP metrics endpoint that Prometheus can scrape, eliminating the need for intermediary processing. In practice, you can plug it into your existing Prom/Grafana stack with minimal effort.
  • Lightweight & Focused: You choose exactly which eBPF programs to run (e.g. one for network latency, one for disk I/O latency). Only those metrics are collected, keeping overhead low. The eBPF programs run in-kernel, and the exporter user-space overhead is small (a single Go daemon). For example, Cloudflare provides an eBPF program for block device I/O latency histograms, so you can track SSD/HDD latency distribution with negligible impact on workloadwww.percona.com.
  • Leverages Existing eBPF Code: The exporter’s examples borrow from well-known BCC toolsblog.cloudflare.com. This means you get tried-and-true kernel logic (for TCP connections, run queue delay, etc.) but in a streamlined form outputting Prometheus metrics. In other words, it combines BCC’s kernel-level insight with a production-friendly delivery mechanismwww.percona.com. Considerations:
  • Configuration and Coding: Using ebpf_exporter may require writing or modifying eBPF C code for your specific metrics, then specifying it in a config YAML. This is a lower-level approach than using BCC scripts. While Cloudflare’s repository provides a set of ready-made programs (mimicking tools like biolatency, runqlat, tcplife, etc.www.percona.com), any new metric will demand some eBPF development knowledge. Ensure you have expertise to safely write and test eBPF code, or stick to the provided modules.
  • System Requirements: The exporter relies on BCC or LLVM under the hood to compile the eBPF programs (it was built in 2018, before CO-RE was common). You’ll need a modern Linux kernel (v4.14+ is recommended) and should install BCC/Clang libraries on the Ubuntu guestwww.percona.com. In ACRN, make sure the guest kernel allows eBPF (no lockdown preventing BPF, and the VM has CAP_BPF or root privileges).
  • Maintenance: As the Linux kernel evolves, eBPF programs may need updates (due to changing structure offsets, etc.). Cloudflare’s examples use modern BPF features and are fairly stable, but you should test after kernel upgrades. Using BPF CO-RE (Compile Once, Run Everywhere) can mitigate compatibility issues – however, ebpf_exporter in its vanilla form might not yet fully leverage CO-RE, so kernel-specific builds could be necessary if you diverge from the examples.

Performance Co-Pilot (PCP) with eBPF Agents

What it is: PCP is a monitoring toolkit that can aggregate metrics from various sources, including eBPF programs. It provides PMDAs (Performance Metric Domain Agents) for BCC, bpftrace, and libbpf CO-RE, meaning PCP can run eBPF-based collectors and make their metrics available to monitoring systemswww.redhat.com. In practice, you can set up PCP on the ROS VM to continuously run selected BCC/bpftrace tools and log their metrics. Grafana has a plugin (grafana-pcp) to visualize PCP metrics, or you can configure PCP to feed into Prometheus.

Advantages:

  • Unified Monitoring Solution: PCP can gather eBPF metrics alongside standard system metrics. For example, the BCC PMDA can start modules for network bytes per process, CPU run-queue latency, and disk latency, and all these metrics become part of PCP’s data storewww.redhat.comwww.redhat.com. This gives you a one-stop solution – data is collected 24/7, stored for historical analysis (via PCP’s logging), and you can set alerts on it using PCP’s inference engine if not using Prometheus alertingwww.redhat.com.
  • Grafana Integration: Using Grafana with PCP is straightforward. The Red Hat PCP integration allows you to visualize eBPF-derived metrics in Grafana dashboards just like any other data sourcewww.redhat.com. In fact, Red Hat provides example dashboards for metrics like runqlat (CPU latency) and biolatency when using PCP with BCCwww.redhat.com. This means less custom work to chart the data.
  • CO-RE/Libbpf Efficiency: The latest PCP (as of RHEL 9) includes a libbpf PMDA, which can run pre-compiled eBPF programs using BPF CO-REwww.redhat.com. This is a lightweight alternative to BCC – no compiler needed on the target, and the eBPF programs (written in C) are loaded directly, saving resourceswww.redhat.com. If you prioritize minimal overhead, you can use these CO-RE based monitors (many of which are equivalent to popular BCC tools) and avoid the runtime compilation tax.
  • Remote Monitoring and Control: Because PCP was designed for enterprise monitoring, it allows pulling metrics from remote hosts easily. You could run the PCP collector in the VM and either push metrics to a central server or have Grafana/Prometheus scrape it. This fits cases where the robotics VM is one of many devices being observed. Considerations:
  • Added Complexity: Deploying PCP introduces additional moving parts – you’ll be running the pmcd daemon and specific PMDAs inside the VM. This is a heavier setup compared to a single-purpose exporter. For a small-scale deployment (e.g., one robot), PCP might be overkill if you don’t need its full feature set. Make sure the benefits (historical logging, centralized management) justify the complexity for your use case.
  • Resource Usage: PCP itself is designed to be efficient, but enabling many eBPF modules will still consume some CPU/RAM. Each BCC module started under PCP will compile and run an eBPF program (with a helper user-space process). In a constrained environment, stick to the key metrics you need. The libbpf/CO-RE modules mitigate this by removing per-module Python/LLVM overheadwww.redhat.com, so prefer those where available.
  • Data Integration: If you already use Prometheus, you have a choice: either use Grafana’s PCP datasource to read metrics directly, or use a bridge to get PCP metrics into Prometheus. Grafana PCP plugin works well for dashboards, but for alerting in Prometheus you might explore Vector or pmproxy which can expose PCP metrics in Prom format. This setup is documented by Red Hatwww.redhat.com, but plan for some configuration effort to hook into your existing monitoring pipeline.

Cilium Hubble – eBPF-Powered Network Observability

What it is: Hubble is a networking observability platform built on eBPF that comes with the Cilium project (popular in cloud-native/Kubernetes environments). It monitors and logs network flows, service connectivity, and API calls at Layer 7, all through eBPF programs attached in the kernel datapathebpf.io. In a robotics context, if your ROS nodes communicate across networks or you have a microservices architecture, Hubble can give deep insight into network performance and interactions. It provides a service map and can track metrics like request rates, error rates, and latencies between components.

Advantages:

  • Rich Network Telemetry: Hubble can capture per-connection and per-service metrics that go beyond basic bandwidth. It can record HTTP/gRPC request rates, response codes, and durations, as well as TCP-level stats (retransmits, drops) – essentially the “golden signals” for network health. This is all done in-kernel without instrumenting applicationswww.reddit.com, which is valuable if you want to monitor ROS traffic (e.g., ROS 2 uses DDS/RTPS; Hubble could potentially trace those UDP flows if integrated with Cilium).
  • Built-in UI and Dashboards: Hubble has its own UI for viewing service maps and metrics, and it integrates with Grafana easily. In Kubernetes deployments, Hubble’s metrics are exposed to Prometheus automaticallydocs.cilium.io, and Grafana dashboards are available for common Hubble metricsgrafana.com. This means if you deploy Hubble, you can quickly visualize network performance in Grafana without writing new dashboards from scratch.
  • Security Observability: Aside from performance metrics, Hubble (with Cilium) can enforce or audit network security policies using eBPF. It can detect unauthorized flows or odd communication patterns. For a robotics system that might have safety or security requirements, this offers an additional layer of insight. Considerations:
  • Kubernetes-Centric: Hubble is designed for Kubernetes clusters – it runs as a daemon set and assumes a Cilium-managed network. If your ROS stack is just running on a single Ubuntu VM without Kubernetes, adopting Hubble is non-trivial. You’d essentially have to install Cilium on the VM (which replaces the kernel networking for that VM) and run Hubble. This could be heavy and complex purely for monitoring purposes. It’s likely overkill unless you already use containerization or have multiple distributed ROS instances to observe.
  • Resource Footprint: While eBPF is efficient, Hubble/Cilium are comprehensive networking solutions and will occupy system resources. The Cilium agent and Hubble relay/UI consume CPU and memory. In a resource-constrained robot controller, this might conflict with ROS itself. Ensure that the VM has enough headroom if you go this route, or consider limiting Hubble to just the monitoring features you need (disabling unused components).
  • Specific Use-Case: If your primary concern is network performance (packet loss, latency between nodes, throughput), Hubble is a top-tier solution. But if network monitoring is only a small part of your needs and CPU/memory/IO are the bigger concerns, a general eBPF approach (like BCC or ebpf_exporter focusing on those metrics) might be simpler. You could also mix and match – for instance, use eBPF exporter for system metrics and only enable a slimmed-down eBPF probe for critical network stats (rather than full Hubble).

Comparative Summary and Implementation Tips

Choosing the “best” solution depends on your priorities: breadth of insight vs. minimal overhead vs. ease of integration. In summary:

  • BCC offers the widest range of monitoring capabilities (covering all aspects of system performance) and is a good starting point for exploring issueswww.redhat.com. However, it’s relatively heavy due to just-in-time compilation and Python runtimedevops.com. It may be best used in combination with PCP or by extracting just the needed eBPF programs into a more efficient framework.
  • bpftrace/ply enable quick, custom instrumentation of the ROS system. They shine for one-off investigations or niche metrics that no pre-made tool covers. For continuous monitoring, they require extra work to export metrics. If you need custom metrics and still want low overhead, consider writing a small libbpf CO-RE program once you’ve prototyped it in bpftrace – this way you get the best of both (ease of development and efficient runtime).
  • Cloudflare’s eBPF Exporter is a strong choice for a lightweight, production-ready monitoring agent. It directly targets Prometheus/Grafana setups and avoids the complexity of a full monitoring suite. Use this if you have a short list of kernel-level metrics to track (like network latency, CPU scheduler latency, GC pauses, etc.) and you want to minimize interference with the robot’s operationwww.percona.com.
  • PCP with eBPF is ideal if you desire a holistic monitoring solution with long-term data retention, multiple metric sources, and perhaps existing PCP usage. It integrates well with Grafanawww.redhat.comand can scale to handle many metrics, but it does introduce additional services on your VM. In an industrial robotics scenario with many devices, PCP could help centralize monitoring across all ROS VMs. If going this route, take advantage of the new CO-RE based agents for efficiencywww.redhat.com.
  • Hubble (Cilium) is specialized for networking observability and would be the go-to if network performance between components is complex or mission-critical. It’s less suitable if you just need basic interface stats (those you can get with simpler tools). Only consider Hubble if you either run Kubernetes or you truly need deep network insights; otherwise, standard eBPF network tools (like BCC’s tcplife or a custom ebpf_exporter program counting ROS messages) might suffice at a fraction of the complexity. Implementation considerations: All these solutions require a modern Linux kernel with eBPF enabled. Ensure your Ubuntu kernel in the ACRN VM meets the requirements (Ubuntu 20.04+ is ideal, as it has a 5.x kernel with built-in BPF Type Format for CO-RE). You will need root privileges in the VM to load eBPF programs. It’s wise to test any eBPF tool in a staging environment – verify that the overhead is indeed minimal and that there are no conflicts with real-time threads or safety features of your robot. In practice, eBPF’s impact is very low because of its design (running in kernel with jitter measured in microseconds), and companies like Facebook routinely run dozens of eBPF programs in production on each serverwww.percona.com. Still, monitor your system’s baseline to ensure the added monitoring doesn’t introduce noticeable latency.