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

This paper introduces text-to-text regression as a novel approach to predicting the performance of large-scale industrial systems, like Google's Borg compute cluster. Unlike traditional tabular methods that struggle with complex, non-tabular data such as configuration files and system logs, this method utilizes encoder-decoder Regression Language Models (RLMs). The research demonstrates that these RLMs can achieve high accuracy (up to 0.99 rank correlation), adapt efficiently to new tasks with minimal new data, and accurately capture the densities of complex outcome distributions. The findings highlight the importance of observing comprehensive features, extensive pretraining for transfer learning, and the model's inherent uncertainty quantification, paving the way for more universal system simulators.