L7 REVERSE PROXY // 5-LAYER ANFIS // SYMBOLIC GP

STATIC ROUTING
BREAKS UNDER
TAIL LATENCY.

Round Robin and Least Connections route blindly into CPU spikes and I/O stalls. FuzzLoad decouples a lock-free Go Reverse Proxy from a dual-loop AI control plane: a 5-Layer Takagi-Sugeno Neuro-Fuzzy network updating weights every 500ms via online gradient descent, paired with Genetic Programming evolving symbolic heuristics over a 60s traffic replay buffer.

LAUNCH LIVE CLUSTER CONSOLE →BENCHMARK ANY URL →READ MATH SPEC

CONTROL_PLANE // REALTIME SNAPSHOT● LOOP: 500ms

NODE_01[4vCPU · 16GB]

OPTIMAL

P95: 19.8msWEIGHT: 51%

NODE_02[2vCPU · I/O FAULT]

DRAINED

P95: 142.0msWEIGHT: 7%

NODE_03[2vCPU · 16GB]

OPTIMAL

P95: 24.1msWEIGHT: 35%

NODE_04[1vCPU · 4GB]

CAPPED

P95: 38.5msWEIGHT: 7%

ACTIVE EVOLVED GP EXPRESSION [GEN #18 · FIT 0.914]

Φ(x) = (1.00 - CPU²) / (0.25 + LAT × (1.00 + CONN))

ANFIS MSE LOSS: 0.0042INJECT CHAOS FAULT →

< 0.2ms

GO ATOMIC PROXY LOOKUP

Zero neural inference on hot path

-69.5%

P95 TAIL LATENCY DROP

34.2ms vs 112.4ms Round Robin

500ms

ANFIS BACKPROP INTERVAL

Online gradient descent on (c, σ, w)

0.974

JAIN'S FAIRNESS INDEX

Capacity-normalized load distribution

// ARCHITECTURAL SPECIFICATION

THREE SOFT-COMPUTING LOOPS. ONE LOCK-FREE PROXY.

[01] UNCERTAINTY LAYER

TAKAGI-SUGENO FUZZY LOGIC

Static rules like 'if CPU > 80% drop server' cause violent routing oscillation. FuzzLoad maps CPU, RAM, Latency, and Active Connections into smooth Gaussian membership degrees (Low, Medium, High) across 12 firing rules.

μ(x) = exp(-(x - c)² / 2σ²)

[02] FAST LOOP // 500ms

5-LAYER DIFFERENTIABLE ANFIS

Wraps the fuzzy inference system inside a 5-layer neural network. Every 500ms, analytical backpropagation adjusts membership centers (c), widths (σ), and linear rule weights to penalize SLA violations.

w_{t+1} = w_t - η · ∂L_SLA / ∂w

[03] SLOW LOOP // 6s REPLAY

SYMBOLIC GENETIC PROGRAMMING

Evolves a population of 36 mathematical syntax trees via tournament selection, subtree crossover, and mutation against a 60-second Digital Twin replay buffer — discovering non-linear I/O bottlenecks safely offline.

P_i ∝ λ·S_ANFIS + (1-λ)·Φ_GP(x)

// DROP-IN INTEGRATION

DEPLOY IN FRONT OF ANY CLUSTER

# Start FuzzLoad Reverse Proxy + ANFIS/GP Control Plane in front of any servers
pip install -e ./backend

fuzzload start \
  --upstreams "http://10.0.1.11:8080,http://10.0.1.12:8080,http://10.0.1.13:8080" \
  --policy fuzzload \
  --port 8000

Want to benchmark a live endpoint right now?OPEN HTTP PROBER →

// CONTROLLED EXPERIMENT (480 RPS + NODE-2 CHAOS)

VERIFIED LATENCY BENCHMARK

POLICY	P50	P95	ERR%	FAIRNESS
ROUND ROBIN (STATIC)	48.6 ms	112.4 ms	4.15%	0.712
LEAST CONNECTIONS	36.2 ms	78.5 ms	1.85%	0.834
WEIGHTED ROUND ROBIN	33.8 ms	71.2 ms	1.42%	0.861
ANFIS NEURO-FUZZY ONLY	23.4 ms	42.6 ms	0.24%	0.946
FUZZLOAD (GP + ANFIS)	19.8 ms	34.2 ms	0.04%	0.974

Reproduce via `python bench/run_benchmark.py`RUN LIVE IN CONSOLE →

STATIC ROUTINGBREAKS UNDERTAIL LATENCY.