Unthrottled: How a Valid Fix Becomes a Regression

This post is the second in a two-part series.

In a previous post, I outlined how we recognized a major throttling issue involving CFS-Cgroup bandwidth control. To uncover the problem, we created a reproducer and used git bisect to identify the causal commit. But that commit appeared completely valid, which added even more complications. In this post, I’ll explain how we uncovered the root of the throttling problem and how we solved it.

Photo by Jake Givens on Unsplash

Scheduling on multiple CPUs with many threads

While accurate, the conceptual model in my prior post fails to fully capture the kernel scheduler’s complexity. If you’re not familiar with the scheduling process, reading the kernel documentation might lead you to believe the kernel tracks the amount of time used. Instead, it tracks the amount of time still available. Here’s how that works.

The kernel scheduler uses a global quota bucket located in cfs_bandwidth->quota. It allocates slices of this quota to each core (cfs_rq->runtime_remaining) on an as-needed basis. This slice amount defaults to 5ms, but you can tune it via the kernel.sched_cfs_bandwidth_slice_us sysctl tunable.

If all threads in a cgroup stop being runnable on a particular CPU, such as blocking on IO, the kernel returns all but 1ms of this slack quota to the global bucket. The kernel leaves 1ms behind, because this decreases global bucket lock contention for many high performance computing applications. At the end of the period, the scheduler expires any remaining core-local time slice and refills the global quota bucket.

That’s at least how it has worked since commit 512ac999 and v4.18 of the kernel.

To clarify, here’s an example of a multi-threaded daemon with two worker threads, each pinned to their own core. The top graph shows the cgroup’s global quota over time. This starts with 20ms of quota, which correlates to .2 CPU. The middle graph shows the quota assigned to per-CPU queues, and the bottom graph shows when the workers were actually running on their CPU.

 Time Action 10ms A request comes in for worker 1.  A slice of quota is transferred from the global quota to the per-CPU queue for CPU 1.   Worker 1 takes exactly 5ms to process and respond to the request. 17ms A request comes in for worker 2.  A slice of quota is transferred from the global quota to the per-CPU queue for CPU 2.

The chance that worker 1 takes precisely 5ms to respond to a request is incredibly unrealistic. What happens if the request requires some other amount of processing time?

 Time Action 30ms A request comes in for worker 1.  Worker 1 needs only 1ms to process the request, leaving 4ms remaining on the per-CPU bucket for CPU 1. Since there is time remaining on the per-CPU run queue, but there are no more runnable threads on CPU 1, a timer is set to return the slack quota back to the global bucket. This timer is set for 7ms after worker 1 stops running. 38ms The slack timer set on CPU 1 triggers and returns all but 1 ms of quota back to the global quota pool.   This leaves 1 ms of quota on CPU 1. 41ms Worker 2 receives a long request.  All the remaining time is transferred from the global bucket to CPU 2’s per-CPU bucket, and worker 2 uses all of it. 49ms Worker 2 on CPU 2 is now throttled without completing the request.   This occurs in spite of the fact that CPU 1 still has 1ms of quota.

While 1ms might not have much impact on a two-core machine, those milliseconds add up on high-core count machines. If we hit this behavior on an 88 core (n) machine, we could potentially strand 87 (n-1) milliseconds per period. That’s 87ms or 870 millicores or .87 CPU that could potentially be unusable. That’s how we hit low-quota usage with excessive throttling. Aha!

Back when 8- and 10-core machines were considered huge, this issue went largely unnoticed. Now that core count is all the rage, this problem has become much more apparent. This is why we noticed an increase in throttling for the same application when run on higher core count machines.

Note: If an application only has 100ms of quota (1 CPU), and the kernel uses 5ms slices, the application can only use 20 cores before running out of quota (100 ms / 5 ms slice = 20 slices). Any threads scheduled on the other 68 cores in an 88-core behemoth are then throttled and must wait for slack time to be returned to the global bucket before running.

Resolving a long-standing bug

How is it, then, that a patch that fixed a clock-skew throttling problem resulted in all this other throttling? In part one of this series, we identified 512ac999 as the causal commit. When I returned to the patch and picked it apart, I noticed this.

-       if (cfs_rq->runtime_expires != cfs_b->runtime_expires) {
+       if (cfs_rq->expires_seq == cfs_b->expires_seq) {
/* extend local deadline, drift is bounded above by 2 ticks */
cfs_rq->runtime_expires += TICK_NSEC;
} else {
cfs_rq->runtime_remaining = 0;
}

The pre-patch code expired runtime if and only if the per-CPU expire time matched the global expire time (cfs_rq->runtime_expires != cfs_b->runtime_expires). By instrumenting the kernel, I proved that this condition was almost never the case on my nodes. Therefore, those 1 milliseconds never expired. The patch changed this logic from being clock time based to a period sequence count, resolving a long-standing bug in the kernel.

The original intention of that code was to expire any remaining CPU-local time at the end of the period. Commit 512ac999 actually fixed this so the quota properly expired. This results in quota being strictly limited for each period.

When CFS-Cgroup bandwidth control was initially created, time-sharing on supercomputers was one of the key features. This strict enforcement worked well for those CPU-bound applications since they used all their quota in each period anyway, and none of it ever expired. For Java web applications with tons of tiny worker threads, this meant tons of quota expiring each period, 1ms at a time.

The solution

Once we knew what was going on, we needed to fix the issue. We approached the problem in several different ways.

First, we tried implementing “rollover minutes” that banked expiring quota and made it usable in the next period. This created a thundering herd problem on the global bucket lock at the period boundary. Then, we tried to make quota expiration configurable separate from the period. This led to other issues where bursty applications could consume way more quota in some periods. We also tried returning all the slack quota when threads became unable to run, but this led to a ton of lock contention and some performance issues. Ben Segall, the author of the CFS scheduler, suggested tracking the core-local slack and reclaiming it only when needed. This solution had performance issues of its own on high-core count machines.

As it turns out, the solution was actually staring us right in the face the whole time. No one had noticed any issues with CFS CPU bandwidth constraints since 2014. Then, the expiration bug was fixed in commit 512ac999, and lots of people started reporting the throttling problem.

So, why not remove the expiration logic altogether? That’s the solution we ended up pushing back into the mainline kernel. Now, instead of being strictly limited to a quota amount of time per period, we still strictly enforce average CPU usage over a longer time window. Additionally, the amount that an application can burst is limited to 1ms for each CPU queue. You can read the whole conversation and see the five subsequent patch revisions on the Linux kernel mailing list archives.

These changes are now a part of the 5.4+ mainline kernels. They have been backported onto many available kernels:

• Linux-stable: 4.14.154+, 4.19.84+, 5.3.9+
• Ubuntu: 4.15.0-67+, 5.3.0-24+
• Redhat Enterprise Linux:
• RHEL 7: 3.10.0-1062.8.1.el7+
• RHEL 8: 4.18.0-147.2.1.el8_1+
• CoreOS: v4.19.84+

The results

In the best-case scenario, this fix enables a .87 increase in usable CPU for each instance of our affected applications, or a corresponding decrease in required CPU quota. These benefits will unlock increased application density and decreased application response times across our clusters.

How to mitigate the issue

Here’s what you can do to prevent CFS-Cgroup bandwidth control from creating a throttling issue on your systems:

• If you are using Kubernetes, use whole CPU quotas, as this decreases the number of schedulable CPUs available to the cgroup
• Increase quota where necessary

Ongoing scheduler developments

Konstantin Khlebnikov of Yandex proposed patches to the Linux kernel mailing list to create a “burst bank.” These changes are feasible now that we have removed the expiration logic, as described above. These bursting patches could enable even tighter packing of applications with small quota limits. If you find this idea interesting, join us on the Linux kernel mailing list and show your support.

To read more about kernel scheduler bugs in Kubernetes, see these interesting GitHub issues:

Unthrottled: Fixing CPU Limits in the Cloud

This post is the first in a two-part series.

This year, my teammates and I solved a CPU throttling issue that affects nearly every container orchestrator with hard limits, including Kubernetes, Docker, and Mesos. In doing so, we lowered worst-case response latency in one of Indeed’s applications from over two seconds to 30 milliseconds. In this two-part series, I’ll explain our journey to find the root cause and how we ultimately arrived at the solution.

Photo by twinsfisch on Unsplash

The issue began last year, shortly after the v4.18 release of the Linux kernel. We saw an increase in tail response times for our web applications, but when we looked at CPU usage, everything seemed fine. Upon further investigation, it was clear that the incidence of high response times directly correlated to periods of high CPU throttling. Something was off. Normal CPU usage and high throttling shouldn’t have been possible. We eventually found the culprit, but first we had to understand the mechanisms at work.

Background: How container CPU constraints work

Almost all container orchestrators rely on the kernel control group (cgroup) mechanisms to manage resource constraints. When hard CPU limits are set in a container orchestrator, the kernel uses Completely Fair Scheduler (CFS) Cgroup bandwidth control to enforce those limits. The CFS-Cgroup bandwidth control mechanism manages CPU allocation using two settings: quota and period. When an application has used its allotted CPU quota for a given period, it gets throttled until the next period.

All CPU metrics for a cgroup are located in /sys/fs/cgroup/cpu,cpuacct/<container>. Quota and period settings are in cpu.cfs_quota_us and cpu.cfs_period_us.

You can also view throttling metrics in cpu.stat. Inside cpu.stat you’ll find:

• nr_periods – number of periods that any thread in the cgroup was runnable
• nr_throttled – number of runnable periods in which the application used its entire quota and was throttled
• throttled_time – sum total amount of time individual threads within the cgroup were throttled

During our investigation into the response time regression, one engineer noticed that applications with slow response times saw excessive amounts of periods throttled (nr_throttled). We divided nr_throttled by nr_periods to find a crucial metric for identifying excessively throttled applications. We call this metric “throttled percentage.” We didn’t like using throttled_time for this purpose because it can vary widely between applications depending on the extent of thread usage.

A conceptual model of CPU constraints

To see how CPU constraints work, consider an example. A single-threaded application is running on a CPU with cgroup constraints. This application needs 200 milliseconds of processing time to complete a request. Unconstrained, its response graph would look something like this.

Now, say we assign a CPU limit of .4 CPU to the application. This means the application gets 40ms of run time for every 100ms period—even if the CPU has no other work to do. The 200ms request now takes 440ms to complete.

If we gather metrics at time 1000ms, statistics for our example are:

 Metric Value Reasoning nr_periods 5 From 440ms to 1000ms the application had nothing to do and as such was not runnable. nr_throttled 4 The application is not throttled in the fifth period because it is no longer runnable. throttled_time 240ms For every 100ms period, the application can only run for 40ms and is throttled for 60ms. It has been throttled for 4 periods, so 4 multiplied by 60 equals 240ms. throttled percentage 80% 4 nr_throttled divided by 5 nr_periods.

But that’s at the high-level, not real life. There are a couple of problems with this conceptual model. First, we live in a world of multi-core, multi-threaded applications. Second, if all this were completely true, our problematic application shouldn’t have hit throttling before exhausting its CPU quota.

Reproducing the problem

We knew a succinct reproducing test case would help convince the kernel community that a problem actually existed and needed to be fixed. We tried a number of stress tests and Bash scripts, but struggled to reliably reproduce the behavior.

Our breakthrough came after we considered that many web applications use asynchronous worker threads. In that threading model, each worker is given a small task to accomplish. For example, these workers might handle IO or some other small amount of work. To reproduce this type of workload, we created a small reproducer in C called Fibtest. Instead of using unpredictable IO, we used a combination of the Fibonacci sequence and sleeps to mimic the behavior of these worker threads. We split these between fast threads and slow worker threads. The fast threads run through as many iterations of the Fibonacci sequence as possible. The slow threads complete 100 iterations and then sleep for 10ms.

To the scheduler, these slow threads act much like asynchronous worker threads, in that they do a small amount of work and then block. Remember, our goal was not to produce the most Fibonacci iterations. Instead, we wanted a test case that could reliably reproduce a high amount of throttling with simultaneous low CPU usage. By pinning these fast and slow threads each to their own CPU, we finally had a test case that could reproduce the CPU throttling behavior.

The first throttling fix / regression

Our next step was to use Fibtest as the condition for running a git bisect on the kernel. Using this technique, we were able to quickly discover the commit that introduced the excessive throttling: 512ac999d275 “sched/fair: Fix bandwidth timer clock drift condition”. This change was introduced in the 4.18 kernel. Testing a kernel after removing this commit fixed our issue of low CPU usage with high throttling. However, as we analyzed the commit and the related sources, the fix looked perfectly valid. And more confusingly, this commit was also introduced to fix inadvertent throttling.

The issue this commit fixed was exemplified by throttling that appeared to have no correlation with actual CPU usage. This was due to clock-skew between the cores that resulted in the kernel prematurely expiring the quota for a period.

Fortunately, this problem was much rarer, as most of our nodes were running kernels that already had the fix. One unlucky application ran into this problem, though. That application was mostly idle and allocated 4.1 CPUs. The resulting CPU usage and throttle percentage graphs looked like this.

CPU usage graph with 4 CPUs allocated and usage not exceeding .5 CPU

Graph of throttled percentage showing excessive throttling

Commit 512ac999d275 fixed the issue and was backported onto many of the Linux-stable trees. The commit was applied to most major distribution kernels, including RHEL, CentOS, and Ubuntu. As a result, some users have probably seen throttling improvements. However, many others are likely seeing the problem that initiated this investigation.

At this point in our journey, we found a major issue, created a reproducer, and identified the causal commit. This commit appeared completely correct but had some negative side-effects. In part two of this series, I’ll further explain the root cause, update the conceptual model to explain how CFS-Cgroup CPU constraints actually work, and describe the solution we eventually pushed into the kernel.

The FOSS Contributor Fund: Forming a Community of Adopters

In January 2019, Indeed launched a new program that democratizes the way we provide financial support to open source projects that we use. We call it The FOSS Contributor Fund. The fund enables Indeed employees who make open source contributions to nominate and vote for projects. Each month, the winning project receives funding. This program encourages support of projects we use and more engagement with the open source community.

Now, we want to help other companies start similar funds. Our goal is to collaborate for the benefit of the open source community. Regardless of a company’s size or resources, we want to discover what we can accomplish when we work together. Indeed is forming a community of FOSS Fund Adopters—companies that will run their own FOSS Contributor Fund initiatives in 2020. We invite you to join us and other FOSS Funders in sharing knowledge and experiences. We’re looking for adopters who are willing to run the same experiment we ran, or something similar. We will work with the community of Funders to set up regular events, exploring different models of open source support and funding.

We’ve seen great results

In our blog post at the six month mark, we described how the program helped encourage Indeed employees to make open source contributions. Since program launch, we’ve seen thousands of such contributions. Indeedians have reported and fixed bugs. They’ve reviewed pull requests and developed features. They’ve improved documentation and designs.

Even better, Indeed developers now have an avenue to advocate for projects in need of our support. And the program has inspired some employees to make their first open source contributions.

The FOSS Contributor Fund is one of the ways Indeed’s Open Source Program Office honors our commitment to helping sustain the projects we depend on. We gave our open source contributors a voice in the process, and we’ve seen some great benefits from doing so: increased contribution activity, better visibility into our dependencies, and a short list of projects where we can send new contributors.

Watching the program evolve and grow is exciting. We’ve learned a lot this year and look forward to more growth in 2020. Now, we’d like you to join us.

Use Indeed’s blueprint to start your FOSS Fund

To find out how we administer the FOSS Fund at Indeed, read our blueprint (released under a Creative Commons license). We’ve also released an open source tool called Starfish that we use to determine voter eligibility. In the coming months, FOSS Funders will publish additional documentation and tools to support these programs. We want to make it easy for anyone to run their own FOSS Fund.