scx-upstream/scheds/rust

RUST SCHEDULERS

Introduction

This directory contains schedulers with user space rust components.

This document will give some background on each scheduler, including describing the types of workloads or scenarios they're designed to accommodate. For more details on any of these schedulers, please see the header comment in their main.rs or *.bpf.c files.

Schedulers

This section lists, in alphabetical order, all of the current rust user-space schedulers.

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scx_layered

Overview

A highly configurable multi-layer BPF / user space hybrid scheduler.

scx_layered allows the user to classify tasks into multiple layers, and apply different scheduling policies to those layers. For example, a layer could be created of all tasks that are part of the user.slice cgroup slice, and a policy could be specified that ensures that the layer is given at least 80% CPU utilization for some subset of CPUs on the system.

Typical Use Case

scx_layered is designed to be highly customizable, and can be targeted for specific applications. For example, if you had a high-priority service that required priority access to all but 1 physical core to ensure acceptable p99 latencies, you could specify that the service would get priority access to all but 1 core on the system. If that service ends up not utilizing all of those cores, they could be used by other layers until they're needed.

Production Ready?

Yes. If tuned correctly, scx_layered should be performant across various CPU architectures and workloads.

That said, you may run into an issue with infeasible weights, where a task with a very high weight may cause the scheduler to incorrectly leave cores idle because it thinks they're necessary to accommodate the compute for a single task. This can also happen in CFS, and should soon be addressed for scx_layered.

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scx_rusty

Overview

A multi-domain, BPF / user space hybrid scheduler. The BPF portion of the scheduler does a simple round robin in each domain, and the user space portion (written in Rust) calculates the load factor of each domain, and informs BPF of how tasks should be load balanced accordingly.

Typical Use Case

Rusty is designed to be flexible, and accommodate different architectures and workloads. Various load balancing thresholds (e.g. greediness, frequenty, etc), as well as how Rusty should partition the system into scheduling domains, can be tuned to achieve the optimal configuration for any given system or workload.

Production Ready?

Yes. If tuned correctly, rusty should be performant across various CPU architectures and workloads. Rusty by default creates a separate scheduling domain per-LLC, so its default configuration may be performant as well. Note however that scx_rusty does not yet disambiguate between LLCs in different NUMA nodes, so it may perform better on multi-CCX machines where all the LLCs share the same socket, as opposed to multi-socket machines.

Note as well that you may run into an issue with infeasible weights, where a task with a very high weight may cause the scheduler to incorrectly leave cores idle because it thinks they're necessary to accommodate the compute for a single task. This can also happen in CFS, and should soon be addressed for scx_rusty.

scx_rustland

Overview

scx_rustland is made of a BPF component (dispatcher) that implements the low level sched-ext functionalities and a user-space counterpart (scheduler), written in Rust, that implements the actual scheduling policy.

The BPF dispatcher is completely agnostic of the particular scheduling policy implemented in user-space. For this reason developers that are willing to use this scheduler to experiment scheduling policies should be able to simply modify the Rust component, without having to deal with any internal kernel / BPF details.

Typical Use Case

scx_rustland is designed to be "easy to read" template that can be used by any developer to quickly experiment more complex scheduling policies, that can be fully implemented in Rust.

Production Ready?

Not quite. For production scenarios, other schedulers are likely to exhibit better performance, as offloading all scheduling decisions to user-space comes with a certain cost.

However, a scheduler entirely implemented in user-space holds the potential for seamless integration with sophisticated libraries, tracing tools, external services (e.g., AI), etc. Hence, there might be situations where the benefits outweigh the overhead, justifying the use of this scheduler in a production environment.