As large scale computation systems are growing to exascale, Resources and Jobs Management Systems (RJMS) need to evolve to manage this scale modification. However, their study is problematic since they are critical production systems, where experimenting is extremely costly due to downtime and energy costs. Meanwhile, many scheduling algorithms emerging from theoretical studies have not been transferred to production tools for lack of realistic experimental validation. To tackle these problems we propose Batsim, an extendable, language-independent and scalable RJMS simulator. It allows researchers and engineers to test and compare any scheduling algorithm, using a simple event-based communication interface, which allows different levels of realism. In this paper we show that Batsim's behaviour matches the one of the real RJMS OAR. Our evaluation process was made with reproducibility in mind and all the experiment material is freely available.

Energy consumption has become one of the most critical issues in the evolution of High Performance Computing systems (HPC). Controlling the energy consumption of HPC platforms is not only a way to control the cost but also a step forward on the road towards exaflops. Powercapping is a widely studied technique that guarantees that the platform will not exceed a certain power threshold instantaneously but it gives no flexibility to adapt job scheduling to a longer term energy budget control. We propose a job scheduling mechanism that extends the backfilling algorithm to become energy-aware. Simultaneously, we adapt resource management with a node shutdown technique to minimize energy consumption whenever needed. This combination enables an efficient energy consumption budget control on a cluster during a period of time. The technique is experimented, validated and compared with various alternatives through extensive simulations. Experimentation results show high system utilization and limited bounded slowdown along with interesting outcomes in energy efficiency while respecting an energy budget during a particular time period.