Performance Prediction for Large Systems via Text-to-Text Regression

This paper introduces **text-to-text regression (RLM)** as a novel approach for **predicting system performance metrics**, particularly in complex industrial environments like **Google's Borg compute cluster**. Unlike traditional methods that struggle with non-tabular data, RLMs **directly process raw text inputs** from system logs and configuration files to deliver highly accurate floating-point predictions. The research highlights the **importance of maximizing feature observability** and **large-scale pretraining** for superior performance and **efficient adaptation to new tasks** with minimal additional data. Ultimately, this work positions RLMs as **versatile and scalable tools** for creating **universal simulators of real-world outcomes**.