Performance Measurements in a High Throughput Computing Environment

The IT infrastructures of companies and research centres are implementing new technologies to satisfy the increasing need of computing resources for big data analysis. In this context, resource profiling plays a crucial role in identifying areas where the improvement of the utilisation efficiency is needed. In order to deal with the profiling and optimisation of computing resources, two complementary approaches can be adopted: the measurement-based approach and the model-based approach. The measurement-based approach gathers and analyses performance metrics executing benchmark applications on computing resources. Instead, the model-based approach implies the design and implementation of a model as an abstraction of the real system, selecting only those aspects relevant to the study. This Thesis originates from a project carried out by the author within the CERN IT department. CERN is an international scientific laboratory that conducts fundamental researches in the domain of elementary particle physics. The project concerns the profiling of a set of 240 servers used to sustain the High Energy Physics workloads in the CERN batch system. The virtualised resources have been tested through a benchmarking process in order to extract performance metrics that have been analysed in detail. Finally, the creation of a model that simulates the real system behaviour, starting from the aforementioned measures, has been flanked to the analytical approach.