Working Docker containers is not only for individuals with gentle OCD that like every little thing to be in its correct place. Containerization has grow to be a useful technique for skilled software program improvement groups trying to keep away from a visit down into the depths of dependency hell. By maintaining every little thing an software wants in a single bundle, remoted from the remainder of the system, constant software efficiency and better ranges of safety are a lot simpler to realize. However every little thing, together with Docker, comes at a value, proper?
Standard knowledge says that the first price is a small efficiency hit. In spite of everything, any extra software program layers should have some computational price, so this makes intuitive sense. For many functions, particularly within the enterprise world, we’ve an overabundance of {hardware} assets lately. As such, a tiny efficiency hit is usually a suitable trade-off for the myriad advantages of containerization. However on this planet of real-time functions and robotics any latency is an excessive amount of, so Docker is essentially averted.
Nevertheless, typical knowledge does typically fail us. Shouldn’t we ask whether or not or not our intuitions are literally true earlier than making an essential determination? The crew over at robocore believes that to be the case, so that they took a deep dive into Docker to get some onerous information and decide if it actually does sluggish issues down. The outcomes may shock you and make you rethink your improvement technique.
The crew centered on robotics workloads with strict real-time necessities like management loops, high-rate sensor streams, and notion pipelines. Utilizing a Jetson Orin Nano, they ran benchmarks evaluating Dockerized ROS 2 setups to native execution. Assessments measured latency, throughput, and jitter underneath each idle and closely loaded CPU situations.
What they discovered is that at idle, the variations between native and containerized execution had been negligible. Extra curiously, underneath heavy load, Docker usually matched, and even outperformed, native when it comes to worst-case latency. This will appear counterintuitive, but it surely was found that the rationale for the surprising enhance got here from Linux’s Fully Truthful Scheduler (CFS). CFS can typically allocate CPU time extra evenly to a container’s course of group than to equal processes working immediately on the host, smoothing out efficiency spikes.
Throughput checks additionally confirmed little efficiency penalty underneath Docker. Actually, containerized setups typically held goal message charges extra constantly underneath CPU strain. Jitter benchmarks, that are essential for understanding the steadiness of management loops, confirmed that median efficiency was very near native efficiency. Cautious configuration, comparable to growing shared reminiscence, utilizing host IPC, and explicitly pinning CPU cores, might additional enhance container efficiency.
The principle takeaway of this work is that Docker doesn’t kill real-time efficiency, a minimum of not when configured correctly. So, the following time somebody dismisses Docker for robotics as too sluggish, you may simply ask if they’ve really measured it. The info means that with the best setup you’ll be able to have each the comfort of containers and the efficiency your robots require.
A comparability of latency over time (📷: robocore)
Throughput check outcomes (📷: robocore)
