Predict-IT applied to Air Quality an MObility project (AQMO)
Channel: UCit - You see it
Date: 2020-05-26

The job scheduler is the central point for your HPC infrastructure. It dispatches and monitors the jobs on the available resources, keeping track of the jobs’ allocation and their state. The generated data are stored in the job scheduler’s logs, from which valuable information can be extracted in order to understand the cluster behavior.

For example, when jobs are submitted on the cluster, some of them may not terminate within the specified time limit (walltime) and consequently no results are obtained. This way, to increase cluster profitability and production, it is necessary to enforce that the submitted computations will end up correctly.

That is why we have developed Predict-IT. By analyzing the job-submission historical data in the logs, Predict-IT creates a computational model of your cluster based on machine-learning techniques. Such model is then applied at the moment of job submission to identify which jobs are likely to end-up in failure; moreover, it also provides recommendations for the walltime you need to specify to prevent job failure, and the amount of memory (max RSS) a job will consume per node.

Configured specifically for your cluster, Predict-IT steadily improves over time: it adapts to your HPC environment by learning from your cluster logs and newly arrived jobs, each time becoming more and more accurate in its predictions. The more data you feed it, the higher the prediction accuracy can be.


The AQMO project is co-financed by the European Union through its CEF programme. It addresses the air quality challenge, thanks to the development of a smart city pilot in the area of Rennes Metropole. The project will provide an end-to-end urban platform that extends current practices in air quality measurements. The AQMO platform will provide citizens, local authorities, scientific organizations and private companies with new data and innovative services based on computing simulation.

Predict-IT: https://www.ucit.fr/en/predict-it/
AQMO: http://aqmo.irisa.fr/