Previously _onSample() computed and stored a volatile float _sma_avg
on every sample (64 times/minute on core 0). The getter simply returned
that pre-computed value.
This commit inverts the responsibility:
- _sma_sum is now volatile uint16_t — the raw integer sum is the
cross-core shared state, written atomically by core 0 (single S16I
instruction on Xtensa LX7) and read by core 1.
- The float conversion (uint16_t → float multiply) is moved into
getCore0Util(), which is called rarely (once/minute for history,
on-demand for web requests). The multiply now happens on the core
that actually needs the result.
- _sma_avg is removed entirely, saving 4 bytes and one volatile float
store per sample from the hot path.
- The SMA update is rewritten as a single combined expression:
const uint16_t last = _sma_buf[_sma_idx];
_sma_buf[_sma_idx] = s8;
_sma_sum = (_sma_sum - last) + s8;
This produces a single write to _sma_sum instead of two (decrement
then increment), which is cleaner when _sma_sum is volatile.
The cross-core contract is unchanged: core 0 writes _sma_sum once per
sample; core 1 reads it in getCore0Util(). A 16-bit aligned store on
Xtensa LX7 is a single instruction, so no spinlock is needed.
The previous implementation maintained three exponential moving averages
(1/5/15-minute) of CPU busy-fraction, sampled every 5 seconds. This had
two problems:
1. Wrong semantics: the metric was labelled "load_avg" (a Unix concept
measuring run-queue depth), but the tracker actually measures CPU
busy-time as a fraction of FreeRTOS ticks — i.e. utilization, not
load. The name was misleading.
2. Poor responsiveness: the 1-minute EMA (DECAY = exp(-5/60) ≈ 0.920)
has a ~60-second time constant. On a live web panel, this made the
metric appear frozen, especially during short bursts of activity
(e.g. an HTTP request from the panel itself).
This commit replaces the EMA with a 64-sample simple moving average
(SMA) over a compact uint8_t circular buffer. The sample interval is
60 s / 64 = 937,500 µs, so the window covers exactly 60 seconds:
Algorithm:
- Every 937,500 µs (= 60 s / 64), the FreeRTOS tick delta (busy vs
idle ticks on core 0) is computed and stored as a uint8_t
(0–255 = 0–100% utilization).
- The circular buffer holds 64 samples, spanning exactly 60 seconds
(64 × 937,500 µs = 60,000,000 µs). The timer interval is derived
directly from the window size: 60 000 000 / SMA_WINDOW µs.
- The index wraps with a bitwise AND (& 63) instead of modulo, since
64 is a power of 2.
- The running sum is a uint16_t (max 64 × 255 = 16 320, fits easily).
- The result _sma_avg is a volatile float written atomically by the
esp_timer task (core 0) and read from the main loop (core 1).
No spinlock is needed: on Xtensa LX7, a 32-bit aligned float store
is a single instruction.
The 60-second window smooths out short bursts (e.g. WiFi/HTTP spikes)
while reacting to sustained load changes within ~10–15 seconds.
Naming:
- The public API is now getCore0Util() returning a float in [0.0, 1.0].
The name explicitly identifies which core is measured (core 0, which
runs WiFi, MQTT, HTTP, and LwIP — see task_pinning.c).
- JSON keys: "load_avg" (array) → "core0_util" (scalar float, percent)
- HistorySample field: load_avg1_pct → core0_util_pct
- StatsHistory series key: "cpu_load" → "core0_util"
- Web panel label: "Load Avg" → "Core0 Util"
The history snapshot (once per minute) reads the same _sma_avg value,
which at that point represents the rolling average of the last 60
seconds — exactly one history interval.
Track CPU utilisation on core 0 using a FreeRTOS tick hook that
increments per-tick idle/busy counters, sampled every 5s by an
esp_timer callback. Exponential moving averages with Linux-style
time constants (1 / 5 / 15 min) are maintained in software:
DECAY1 = exp(-5/60) ≈ 0.9200 (1-minute window)
DECAY5 = exp(-5/300) ≈ 0.9835 (5-minute window)
DECAY15 = exp(-5/900) ≈ 0.9945 (15-minute window)
Core 1 is excluded intentionally: it runs the Arduino loopTask at
100% load for LoRa packet processing, so its figure is always 1.0
and carries no diagnostic value. All other tasks are pinned to
core 0 by task_pinning.c, so core-0 load reflects the true system
utilisation.
New files:
arch/esp32/CPUUsageTracker.h – class declaration
arch/esp32/CPUUsageTracker.cpp – tick hook + esp_timer sampling
Integration:
MyMesh::begin() calls _cpu_tracker.begin() on ESP32 builds.
formatStatsReply() emits "load_avg":[<1m>,<5m>,<15m>] in the
compact stats JSON payload.
formatWebStatsSummaryJson() adds the same field to the web-panel
stats endpoint under core.load_avg.
The web-panel HUD gains a "Load Avg" metric tile showing all
three values side-by-side; the core-metrics grid is widened from
4 to 5 columns. The tile is rendered conditionally so older
firmware (or non-ESP32 builds) that omit the field degrade
gracefully.
CPUUsageTracker.cpp is added to the esp32_base build_src_filter
in platformio.ini.
Valentin V. Bartenev