Predicting resource usage for every microservice in an application — without manually tuning a model for each one.
Applied the Multi-Layer Correlation patent to its algorithms, CrystalClear Time Series Analysis Engine is the forecasting core of Federator.ai. It uses cross-correlation between an application’s primary workload and its microservices to generate fast, accurate resource predictions — even across hundreds of metrics at once.
Why Traditional Forecasting Falls Short
Most time series tools were built to model one metric at a time. Microservice environments don’t work that way.
One Model Per Metric
Blind to Dependencies
Too Slow for Real-Time Use
What It Is
CrystalClear Time Series Analysis Engine is ProphetStor’s correlation-based forecasting approach. Instead of modeling every metric from scratch, it uses the relationship between an application’s primary workload and its microservices to generate predictions directly.
High Correlation — Fast Path
Low Correlation — Feature-Based Path
When the relationship is weaker, CrystalClear falls back to analyzing the metric’s own trend, seasonality, and change points to build a tailored forecast.
How CrystalClear Time Series Analysis Engine builds predictions: correlation analysis routes each microservice to either primary-feature or app-feature modeling