The scenario

Job isolation is so hot right now. Everybody has loads of giant servers and loads of jobs, but many of those jobs don’t need an entire server all to themselves. It would be way more efficient with resource usage to be able to stack a bunch of applications on the same server and let them do their thing. Ideally you don’t want any application to affect the other applications on the system, so you want to employ resource limits on the applications to ensure everybody behaves.

Enter cgroups, which allows you to put resource limits on various parts of an applications behavior. You can limit memory usage, cpu usage, io bandwidth, etc. In order to test that this works properly one of our teams was testing the cpu part of this controller. They made two jobs, one that was a stress -n <# cpus> command, and another that was a real live application we use. The amount of cpu time is tracked per cgroup in /sys/fs/cgroup/path/to/group/cpu.stat in cgroupsv2, and they noticed that the stress group got around 50% more cpu time than the real application, despite being given equal weight.

First things first

The first thing I needed to do was to classify our real application, because starting all of the infrastructure for this test took a lot of time and was kind of fragile. Also I needed to be able to show upstream the problem, and I couldn’t very well take our giant application and hand it to them to and tell them how to build and set it all up.

I created this which I used to generate an rt-app configuration that was representative of our workload. Then a simple reproducer script here in order to quickly iterate on my problem.

The problem generally

If you haven’t read my previous post on scheduler basics you are going to want to do that now, we’re going to go into the weeds a bit here.

The problem is generally because of how the processes behave. Whenever a process is runnable it sits on it’s respective runqueue. Because stress is just a cpu consumer it never leaves the runqueue, which means that a lot of the work the scheduler does just doesn’t happen for any of the stress processes. The real application however does real application things, it goes to sleep, wakes up, wakes up other threads, waits on IO, etc. All of these actions interact with the scheduler in various ways, which means there were lots of little ways our application would lose CPU time to the stress application.

The problem turned out to be a combination of 3 things. First was the lazy weight and load propagation up the cfs_rq hierarchy. Second was how we calculated the cfs_rq shares on the cpu at enqueue time. And finally our good friend wake affinity reared it’s ugly head again and made a mess of everything.

Load propagation up the hierarchy

There’s two load measurements we make on a cfs_rq and sched_entity, they are the load_avg and runnable_load_avg. load_avg is the load of the task that gets updated when we are running and sleeping. When we sleep load_avg goes down, when we run load_avg goes up. runnable_load_avg is slightly different in that it only gets updated when we’re running. It can go down if we are asleep for a long period of time, but it only reflects the load of the process when it’s running. Both of these get propagated up the hierarchy, but only the runnable_load_avg matters when it comes to load balancing the cpus (ish, it’s more complicated than this but for all intents and purposes it’s the important one).

Prior to Peter Zijlstra’s patches to fix this problem we never propagated the runnable_load_avg of newly attached cfs_rq’s up the hierarchy, only the load_avg. This meant that the load balancer was usually dealing with stale information, and would make improper load balancing decisions. This affected our workload because our tasks would go on and off the runqueue while the stress group would not, which exacerbated the issue.

Peter’s patches fixed this problem by making the runnable_load_avg be completely re-calculated every time we have a weight change in the hierarchy. Before we would only change the load_avg, and we also only updated the load_avg by a delta of the load change based on the new weights. Now with his patches we keep a runnable_load_sum, which is the time spent runnable, and then calculate the runnable_load_avg by using our new weight in the calculation with runnable_load_sum. Changes are now immediate and more accurate.

A slight aside.

Peter’s patches introduced a sharp regression to my test case. I spent a good amount of time trying to track this down, and eventually realized that his calculation of runnable_weight suffered from the same problem as how we calculated our cfs_rq shares as described below. The fix for this specific problem is in this patch.

Mis-calculation of cfs_rq shares at enqueue time

The code in my previous post is the updated version of calc_cfs_shares, but previously our current load was calculated like this

load = cfs_rq->avg.load_avg

which means that if we suddenly woke up 4 processes on this cfs_rq, the load would be the historical load, rather than a reflection of the current load. This code got changed to the following

load = max(scale_load_down(cfs_rq->load.weight), cfs_rq->avg.load_avg);

load_avg operates in the range of 0 - load.weight. This change meant that at enqueue time we would get credit for our theoretical maximum load, as cfs_rq->load.weight is updated with the sched_entity->load.weight at enqueue time.

The result was that we were now getting an immediate reflection of probable load by newly woken tasks, which made load balancing more accurate.

Wake affinity effective load miscalculation

Wake affinity is the schedulers way of trying to prefer cache locality over wakeup latency. The idea is that all things being equal, we’d rather pay the cost of migrating a task to the cpu where we think it may have cache locality than deal with migrating cache between cpus. This logic isn’t super complicated, we basically have a counter to see if we have a multi-waker to wakee relationship, and if we do make a sort of half-assed load balance decision and migrate the task to our current cpu.

The half-assed load balance decision is part of the problem. Since we make load balancing decisions at the cpu level, and our sched_entity is buried under a cfs_rq hierarchy, we have to calculate the load difference each cpu would see by waking this task up on either cpu. How this happens is we do a recursive walk up the cfs_rq hierarchy doing a modified version of calc_cfs_shares all the way up until we have the delta of the load that would be visible to the cpu.

You’re going to need to read that paragraph a few more times.

How this is different from the normal state of things is that we add our sched_entity->avg.load_avg to the cfs_rq->avg.load_avg, calculate what our theoretical shares would be for the cfs_rq’s sched_entity, subtract the actual value of the cfs_rq’s sched_entity and do this recursively. The problem is in reality, at enqueue time, we only add our sched_entity->load.weight to our cfs_rq->load.weight, then we calculate our shares from there, and set our sched_entity->load.weight to the new weight. Now this isn’t a huge deal, it’s supposed to be an approximation and it’s definitely approximate, but it isn’t a reflection of what happens in reality. And as we see above, the load_avg can bias us against the group that goes to sleep occasionally. With load balancing decisions we want to be using the theoretical maximum load a new task would place on the hierarchy.

Next we have a significantly more subtle problem. We are calculating our new sched_entity->load.weight using current load_avg numbers. Our sched_entity->load.weight that we subtract from our newly calculated weight was computed using historic load_avg numbers, which could have been higher at the time. This means that when we’re trying to calculate how much load we would add to the cpu by moving our task to that cpu, the calculation would actually make it look like we were removing load from the cpu. This meant we were constantly migrating tasks when in reality we should not have been. Since we can easily calculate the “old” weight by using the current numbers without the tasks added weight we do this so our delta is consistent with our current load_avg numbers.

This was a doozy. Even once I understood everything that was going on it still took me a few days (probably more like a week) to get the math and logic right here and realize what was going on.

Wake affinity ping-ponging

The last aspect of the problem was wake affinity ping-ponging our tasks. As I stated above, if everything is equal between two cpu’s, we will prefer to wake up the task on the waker’s cpu. This blanket assumption that these things maybe share cache meant that we were ping-ponging tasks around constantly, which was still causing problems. We would wake affine a process, then the load balancer would come behind us and put the process back on another cpu.

This problem was much easier to understand and more straightforward to fix. I simply kept track of the last time the load balancer moves us off of a cpu, and if it has been in the last second don’t do a affinity wakeup. This got us the rest of the way there, and now we have an even 50-50 split between the two groups.


This problem sucked, I spent a solid 2 months on it. Many thanks to Peter Zijlstra and Tejun Heo for answering my questions and being my sounding board. I had very little scheduler experience before tackling this problem, and it ended up being much more involved than I expected. The whole series has been sent up to fix the problem, I imagine there will be a lot of discussion and the patches that eventually go upstream won’t look anything like these ones, but for now you can look at them here.