Crate scx_mlfq 

scx_mlfq, a Multilevel Feedback Queue scheduler for sched_ext.

Per-CPU, virtual-time-ordered user DSQs (Q1/Q2/Q3 per CPU) over an EEVDF virtual-time substrate. Tasks are classified into queues by a regression tree that predicts the next CPU burst from per-task features (see mlfq_tree.rs), with the EMA interactivity gauge as a tree feature and the fallback before the first model. The wakeup path is promotion-only, through the tree, the short-sleep and I/O boost and the band hysteresis. Demotion flows through the run-out gate. See README.md for the design overview.

Re-exports

pub use bpf_intf::*;

Modules

alloc 🔒

bpf_intf

bpf_skel 🔒

config 🔒

Userspace configuration, validated scheduling constants written into BPF rodata.

mlfq_tree 🔒

CART regression tree for next-CPU-burst prediction.

stats 🔒

Stats server, #[derive(Stats)] metrics and monitor loop.

topology 🔒

Hybrid-capacity and cache-domain topology discovery.

types

webui 🔒

Loopback web UI: live scheduler metrics as a small HTTP server.

Structs

BpfLinks

BpfMaps

BpfProgs

BpfSkel

BpfSkelBuilder

ModelMeta 🔒

Metadata of the committed MLFQ tree model, reported to the stats server and the exit log. Defaults describe the untrained state.

OpenBpfMaps

OpenBpfProgs

OpenBpfSkel

Opts 🔒

Scheduler 🔒

The Scheduler facade owns the loaded skeleton, the struct_ops link, the stats server and the MLFQ tree daemon state; drives the run loop until shutdown or UEI exit.

StructOps

TrainResult 🔒

Result of one training run, handed back from the worker thread. The metrics are computed on the held-out slice so the publish gate and the reported numbers describe out-of-sample error.

Constants

MLFQ_IDLE_RESUME_LATENCY_US 🔒

MLFQ_MAX_CPUS 🔒

MLFQ_TREE_MAX_DEPTH 🔒

MLFQ_TREE_MAX_NODES 🔒

MLFQ_TREE_MIN_LEAF 🔒

MLFQ_TREE_MIN_PIDS 🔒

MLFQ_TREE_MIN_SAMPLES 🔒

MLFQ_TREE_PER_PID_CAP 🔒

MLFQ_TREE_RETRAIN_INTERVAL 🔒

MLFQ_TREE_WINDOW_MAX 🔒

NSEC_PER_USEC 🔒

SCHEDULER_NAME 🔒

Functions

full_version 🔒

main 🔒

spawn_train_worker 🔒

Spawn the training worker and return its job and result channels.

split_holdout 🔒

Split the window into the fit slice (first 90%) and the held-out evaluation slice (last 10%). The daemon only trains once the window holds MLFQ_TREE_MIN_SAMPLES samples, so the holdout is never empty in production; a window too small for a meaningful 90/10 split (< 20 samples) is an error, which the caller treats as a skipped training round (log + keep the previous model) instead of silently training and evaluating on the same slice.

train_model 🔒

Fit a tree and compute the holdout metrics, on the training worker.

train_model_with_scratch 🔒

Fit a tree and compute holdout metrics reusing the scratch arena. The scratch buffers are cleared in place, so the second call with the same window size does not allocate.

tree_admit_pid 🔒

Admit one sample of pid into the window under the per-pid cap. Returns true when the sample is admitted and the pid’s count is incremented, false when the pid already holds its MLFQ_TREE_PER_PID_CAP share and the caller must drop the sample. Entries are pruned on eviction, not here: a pid at the cap keeps its entry until the window ages its samples out.

tree_distinct_pids 🔒

Number of distinct pids in a slice, the concentration input for the per-pid cap.

tree_evict_pid 🔒

Remove one sample of pid from the per-pid accounting when the window evicts its oldest sample, pruning the entry when the count reaches zero so the map cannot grow with retired pids.