As a production-grade simulation and forecasting framework
As a production-grade simulation and forecasting framework, ZPE-1 (Zero-Point Evolution Engine) operates as an offline drift modeling environment rather than a direct real-time telemetry agent. Integration with existing monitoring ecosystems like Prometheus is achieved by mapping standard infrastructure telemetry into the deterministic numeric format required for stress evolution modeling.
Architectural Integration Logic
The ZPE-1 engine is intentionally separated from runtime control to preserve safety-critical integrity. In production deployment, the data flow follows a multi-layer hierarchy:
Telemetry Layer
Existing monitoring tools (Prometheus, BMC/Redfish, DCIM, scheduler logs) provide the raw signal substrate.
Detector Kernel (UCC)
A runtime safety kernel ingests these signals at 1–10 Hz to compute the Choke Index (chi_i) and predictive risk metric (rho_i).
Fossil Ledger
State transitions that pass the SE44 gate are serialized and ledgered using 17-digit decimal precision and SHA-256 hashing.
ZPE-1 (Offline Engine)
Consumes auditable ledger states to simulate cascades, tune weights (a_i), and forecast stress signatures.
Signal Mapping and Sensor Requirements
ZPE-1 does not require proprietary physical hardware sensors. It requires instantiation of specific logical sensors or proxies derived from existing telemetry. Integration involves mapping Prometheus-style metrics into four primary core state signals:
Stored Stress (x_i)
Accumulation of disorder or load.
Examples: GPU hotspot temperature (AI), line loading (grid), queue length (logistics).
Throughput (y_i)
Dissipation outflow rate.
Examples: chilled water flow delta-T, completed jobs per second, moves per hour.
Latency (L_i)
Correction response time.
Examples: cross-correlation lag of thermal response, AGC/dispatch lag.
Control Effort (u_i)
Available authority.
Examples: DVFS caps, fan setpoints, redispatch capacity, crane allocation.
For AI clusters, BMC/Redfish and GPU metrics are sufficient to instantiate these signals without custom hardware.
Implementation and Data Normalization
To ensure compatibility between Prometheus-derived metrics and the deterministic drift evolution engine, raw data must be normalized using robust rolling statistics (median and IQR) per node. This allows ZPE-1 to perform cross-domain stress modeling regardless of source units (Celsius, watts, jobs per second, etc.).
Python example for telemetry ingestion:
import numpy as np
class TelemetryAdapter:
def __init__(self, alpha, delta, epsilon=1e-6):
# element-wise sensitivity weights [a1, a2, a3, a4, a5]
self.alpha = np.array(alpha)
self.delta = np.array(delta)
self.epsilon = epsilon
def process_prometheus_metrics(self, x, x_dot, L, sigma, headroom, u_avail, R, delta_headroom):
"""
Ingest typical telemetry and compute entropy production (S_dot)
and dissipation (D) for ZPE-1 simulation ingestion.
"""
s_dot_terms = np.array([
max(0.0, x),
max(0.0, x_dot),
max(0.0, L),
sigma,
max(0.0, -delta_headroom)
])
s_dot = np.sum(self.alpha * s_dot_terms)
d_terms = np.array([headroom, u_avail, R])
d = np.sum(self.delta * d_terms)
chi = s_dot / (d + self.epsilon)
return chi, s_dot, d
Deterministic Ledger Integration
For ZPE-1 to utilize Prometheus data in a ledger-auditable simulation, metrics must conform to strict deterministic standards. This prevents simulation-chain divergence due to floating-point drift across CPU architectures.
Quantization
Metrics should be quantized before serialization (recommended 1e-12).
Precision
All floats are serialized using exactly 17 significant decimal digits to ensure round-trip fidelity.
Ordering
JSON keys are sorted lexicographically for byte-exact SHA-256 hashing.
In summary, ZPE-1 integrates with standard monitoring tools by treating them as a telemetry substrate, provided the data is normalized and quantized to meet deterministic numeric requirements. ZPE-1 then performs offline calibration, synthetic near-miss generation, and cascade amplification modeling.
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