Optimizing OpenStack Nova for Scientific Workloads

• Main Authors: Moreira Belmiro, Trigazis Spyridon, Tsioutsias Theodoros
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: EPJ Web of Conferences
• Online Access: https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_07031.pdf

The CERN OpenStack cloud provides over 300,000 CPU cores to run data processing analyses for the Large Hadron Collider (LHC) experiments. To deliver these services, with high performance and reliable service levels, while at the same time ensuring a continuous high resource utilization has been one of the major challenges for the CERN cloud engineering team. Several optimizations like NUMA-aware scheduling and huge pages, have been deployed to improve scientific workloads performance, but the CERN Cloud team continues to explore new possibilities like preemptible instances and containers on bare-metal. In this paper we will dive into the concept and implementation challenges of preemptible instances and containers on bare-metal for scientific workloads. We will also explore how they can improve scientific workloads throughput and infrastructure resource utilization. We will present the ongoing collaboration with the Square Kilometer Array (SKA) community to develop the necessary upstream enhancement to further improve OpenStack Nova to support large-scale scientific workloads.