Fine-Grained Long-Range Prediction of Resource Usage in Computer Clusters

In order to facilitate the development of intelligent resource managers of computer clusters, we investigate the utility of the state-of-the-art neural networks for the purpose of finegrained long-range prediction of the resource usage in one such cluster. We consider a large data set of real-life traces and describe in detail our workflow, starting from making the data accessible for learning and finishing by predicting the resource usage of individual tasks multiple steps ahead. The experimental results indicate that such fine-grained traces as the ones considered possess a certain structure, and that this structure can be extracted by advanced machine-learning techniques and subsequently utilized for making informed predictions.