Alea -Complex Job Scheduling Simulator

International audienceUsing large computer systems such as HPC clusters up to their full potential can be hard. Many problems and inefficiencies relate to the interactions of user workloads and system-level policies. These policies enable various setup choices of the resource management system (RMS) as well as the applied scheduling policy. While expert's assessment and well known best practices do their job when tuning the performance , there is usually plenty of room for further improvements, e.g., by considering more efficient system setups or even radically new scheduling policies. For such potentially damaging modifications it is very suitable to use some form of a simulator first, which allows for repeated evaluations of various setups in a fully controlled manner. This paper presents the latest improvements and advanced simulation capabilities of the Alea job scheduling simulator that has been actively developed for over 10 years now. We present both recently added advanced simulation capabilities as well as a set of real-life based case studies where Alea has been used to evaluate major modifications of real HPC and HTC systems

Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems

International audiencePower-aware scheduling is a promising solution to the resource usage monitoring of High-Performance Computing facility electrical power consumption. This kind of solution needs a reliable estimation of job power consumption to feed the Resources and Jobs Management System at submission time. Available data for inference is restricted in practice because unavailable or even untrustworthy. We propose in this work an instance-based model using only the submission logs and user provided job data. GID and the number of tasks per node appears to be good features for prediction of a job’s average power consumption. Moreover, we extant this model to production context with online computation to make a practical global power prediction from job submission data using instances re-weighting. The performance of the online model are excellent on COBALT’s data. With any doubt this model will be a good candidate for the achievement of consistent power-aware scheduling for other computing centers with similar informative inputs