1.2How AI-Driven Profiling Works

AI-driven load profiling replaces intuition with data. The process follows four stages:

1. Collect -- Export production access logs, APM traces, or CDN analytics into a structured format
2. Cluster -- Use ML clustering (k-means, DBSCAN) to identify distinct user behavior patterns
3. Model -- Build a traffic model that captures arrival rates, session duration, endpoint mix, and temporal patterns
4. Generate -- Feed the model into your load testing tool as a realistic virtual user scenario

Architecture Overview

  Production Logs / APM Data
           |
           v
  +------------------+
  | Feature          |
  | Engineering      |  requests/session, unique endpoints,
  |                  |  avg response time, session duration
  +--------+---------+
           |
           v
  +--------+---------+
  | ML Clustering    |  k-means, DBSCAN, hierarchical
  | (scikit-learn)   |
  +--------+---------+
           |
           v
  +--------+---------+
  | User Personas    |  power_user, casual_browser,
  |                  |  api_consumer, bot_crawler
  +--------+---------+
           |
           v
  +--------+---------+
  | Load Test        |  k6 scenarios, Locust user classes,
  | Scenario Gen     |  with realistic think times
  +------------------+