Simulation

Adaptive Simulation Platform

Stress-test system behavior under configurable load, latency, and failure conditions before production.

A real-time simulation environment for modeling distributed topologies, applying control policies, and observing emergent system behavior under pressure.

D3.jsSimulation EngineTypeScriptReact
Overview

Problem Space

Production environments are too costly and fragile for exploratory testing. Teams cannot safely model burst traffic, partial outages, or the cascading effects of policy changes without risking real services.

Solution

System Design

A real-time simulation environment with configurable topology, pluggable control policies, and live metric visualization. Teams stress-test system behavior under varying load, latency, and failure conditions.

Architecture

System Components

Simulation Engine
discrete-time tick loop with seeded RNG
Topology Model
services, queues, workers, dependencies
Policy Module
autoscale, load shedding, retry strategies
Metrics Collector
RPS, queue depth, latency, error sampling
Scenario API
preset configurations for common patterns
Comparison Engine
A/B policy overlay with differential metrics
Interactive Demo

Live Prototype

Loading prototype...

Interactive prototype — all data generated client-side with deterministic seeds.

Benchmark

Reference Performance

Reference benchmark: burst_traffic scenario, autoscale policy, seeded RNG. 12 agents at baseline scaling to 512 at peak. Measured tick duration and policy convergence over 60s window.

Simulated Agents: minimum 12.0, maximum 512.0, average 198.9
Tick Duration (ms): minimum 4.0, maximum 38.0, average 14.7
Policy Convergence (s): minimum 8.0, maximum 94.0, average 36.6

Deterministic seed · 60s window · Simulated workload · Local environment

Technology

Implementation Details

D3.jsSimulation EngineTypeScriptReact