Reducing tail latencies with automatic cooperative task yielding

Tokio is a runtime for asynchronous Rust applications. It allows writing code using async & await syntax. For example:

let mut listener = TcpListener::bind(&addr). await ?; loop { let ( mut socket, _) = listener.accept(). await ?; tokio::spawn( async move { }); }

The Rust compiler transforms this code into a state machine. The Tokio runtime executes these state machines, multiplexing many tasks on a handful of threads. Tokio's scheduler requires that the generated task's state machine yields control back to the scheduler in order to multiplex tasks. Each .await call is an opportunity to yield back to the scheduler. In the above example, listener.accept().await will return a socket if one is pending. If there are no pending sockets, control is yielded back to the scheduler.

This system works well in most cases. However, when a system comes under load, it is possible for an asynchronous resource to always be ready. For example, consider an echo server:

tokio::spawn( async move { let mut buf = [ 0 ; 1024 ]; loop { let n = socket.read(& mut buf). await ?; if n == 0 { break ; } socket.write(buf[..n]). await ?; } });

If data is received faster than it can be processed, it is possible that more data will have already been received by the time the processing of a data chunk completes. In this case, .await will never yield control back to the scheduler, other tasks will not be scheduled, resulting in starvation and large latency variance.

Currently, the answer to this problem is that the user of Tokio is responsible for adding yield points in both the application and libraries. In practice, very few actually do this and end up being vulnerable to this sort of problem.

A common solution to this problem is preemption. With normal OS threads, the kernel will interrupt execution every so often in order to ensure fair scheduling of all threads. Runtimes that have full control over execution (Go, Erlang, etc.) will also use preemption to ensure fair scheduling of tasks. This is accomplished by injecting yield points — code which checks if the task has been executing for long enough and yields back to the scheduler if so — at compile-time. Unfortunately, Tokio is not able to use this technique as Rust's async generators do not provide any mechanism for executors (like Tokio) to inject such yield points.

Per-task operation budget

Even though Tokio is not able to preempt, there is still an opportunity to nudge a task to yield back to the scheduler. As of 0.2.14, each Tokio task has an operation budget. This budget is reset when the scheduler switches to the task. Each Tokio resource (socket, timer, channel, ...) is aware of this budget. As long as the task has budget remaining, the resource operates as it did previously. Each asynchronous operation (actions that users must .await on) decrements the task's budget. Once the task is out of budget, all Tokio resources will perpetually return "not ready" until the task yields back to the scheduler. At that point, the budget is reset, and future .await s on Tokio resources will again function normally.

Let's go back to the echo server example from above. When the task is scheduled, it is assigned a budget of 128 operations pr "tick". The number 128 was picked mostly because it felt good and seemed to work well with the cases we were testing against (Noria and HTTP). When socket.read(..) and socket.write(..) are called, the budget is decremented. If the budget is zero, the task yields back to the scheduler. If either read or write cannot proceed due to the underlying socket not being ready (no pending data or a full send buffer), then the task also yields back to the scheduler.

The idea originated from a conversation I had with Ryan Dahl. He is using Tokio as the underlying runtime for Deno. When doing some HTTP experimentation with Hyper a while back, he was seeing some high tail latencies in some benchmarks. The problem was due to a loop not yielding back to the scheduler under load. Hyper ended up fixing the problem by hand in this one case, but Ryan mentioned that, when he worked on node.js, they handled the problem by adding per resource limits. So, if a TCP socket was always ready, it would force a yield every so often. I mentioned this conversation to Jon Gjenset, and he came up with the idea of placing the limit on the task itself instead of on each resource.

The end result is that Tokio should be able to provide more consistent runtime behavior under load. While the exact heuristics will most likely be tweaked over time, initial measurements show that, in some cases, tail latencies are reduced by almost 3x.

"master" is before the automatic yielding and "preempt" is after. Click for a bigger version, see also the original PR comment for more details.

A note on blocking

Although automatic cooperative task yielding improves performance in many cases, it cannot preempt tasks. Users of Tokio must still take care to avoid both CPU intensive work and blocking APIs. The spawn_blocking function can be used to "asyncify" these sorts of tasks by running them on a thread pool where blocking is allowed.

Tokio does not, and will not attempt to detect blocking tasks and automatically compensate by adding threads to the scheduler. This question has come up a number of times in the past, so allow me to elaborate.

For context, the idea is for the scheduler to include a monitoring thread. This thread would poll scheduler threads every so often and check that workers are making progress. If a worker is not making progress, it is assumed that the worker is executing a blocking task, and a new thread should be spawned to compensate.

This idea is not new. The first occurence of this strategy that I am aware of is in the .NET thread pool, and was introduced more than ten years ago. Unfortunately, the strategy has a number of problems and because of this, it has not been featured in other thread pools / schedulers (Go, Java, Erlang, etc.).

The first problem is that it is very hard to define "progress". A naive definition of progress is whether or not a task has been scheduled for over some unit of time. For example, if a worker has been stuck scheduling the same task for more than 100ms, then that worker is flagged as blocked and a new thread is spawned. In this definition, how does one detect scenarios where spawning a new thread reduces throughput? This can happen when the scheduler is generally under load and adding threads would make the situation much worse. To combat this, the .NET thread pool uses hill climbing. This article provides a good overview of how it works.

The second problem is that any automatic detection strategy will be vulnerable to bursty or otherwise uneven workloads. This specific problem has been the bane of the .NET thread pool and is known as the "stuttering" problem. The hill climbing strategy requires some period of time (hundreds of milliseconds) to adapt to load changes. This time period is needed, in part, to be able to determine that adding threads is improving the situation and not making it worse.

The stuttering problem can be managed with the .NET thread pool, in part, because the pool is designed to schedule coarse tasks, i.e. tasks that execute in the order of hundreds of milliseconds to multiple seconds. However, in Rust, asynchronous task schedulers are designed to schedule tasks that should run in the order of microseconds to tens of milliseconds at most. In this case, any stutttering problem from a heuristic-based scheduler will result in far greater latency variations.

The most common follow-up question I've received after this is "doesn't the Go scheduler automatically detect blocked tasks?". The short answer is: no. Doing so would result in the same stuttering problems as mentioned above. Also, Go has no need to have generalized blocked task detection because Go is able to preempt. What the Go scheduler does do is annotate potentially blocking system calls. This is roughly equivalent to the Tokio's block_in_place .

In short, as of now, the automatic cooperative task yielding strategy that has just been introduced is the best we have found for reducing tail latencies. Because this strategy only requires Tokio's types to opt-in, the end user does not need to change anything to gain this benefit. Simply upgrading the Tokio version will include this new functionality. Also, if Tokio's types are used from outside of the Tokio runtime, they will behave as they did before.

There is more work that should happen on this topic. It is still how unclear how task budgets should work with "sub-schedulers" (e.g. FuturesUnordered ). The task budget APIs should eventually be exposed publicly so that third party libs can integrate with them. It also would be nice to figure out a way to generalize this concept so more than just Tokio users can take advantage of it.

We hope you find your tail latencies improve after this release. Either way, we will be interested to hear how this change impacted real-world deployments. Feel free to comment on this issue.

—Carl Lerche