Predicting ALICE Grid throughput using recurrent neural networks

The Worldwide LHC Computing Grid (WLCG) is the infrastructure enabling the storage and processing of the large amount of data generated by the LHC experiments, and in particular the ALICE experiment. With the foreseen increase in the computing requirements of the future High Luminosity LHC experiments, a data placement strategy which increases the efficiency of the WLCG computing infrastructure becomes extremely relevant for the scientific success of the LHC scientific programme. Currently, the data placement at the ALICE Grid computing sites is determined by heuristic algorithms. Optimisation of the data storage could yield substantial benefits in terms of efficiency and time-to-result. This has however proven to be arduous due to the complexity of the problem. In this work we propose a modelisation of the behaviour of the system via principal component analysis, time series analysis and deep learning, starting from the detailed data collected by the MonALISA monitoring system. We show that it is possible to analyse and model the throughput of the ALICE Grid to a level that has not been possible before, comparing the performance of different deep learning architectures based on recurrent neural networks. Analyzing about six weeks of activity, the Grid I/O throughput trend is successfully predicted with a mean relative error of 4%, while the prediction of the throughput itself performs at 5%.