Relatório de resultado – Piloto experimentação Cloud Computing

Abstract

In this report, we present a comparison of model performance indicators for several operational coastal forecast systems and structural engineering predictions and evaluations executed in local workstations, in HPC cluster nodes and in a pilot cloud, aiming at contributing to the best choice for the National Infrastructure for Scientific Computing. Results show that the scalability and flexibility of cloud computing resources makes them an attractive alternative for the implementation of multiple forecast systems using serial, non-MPI models, as well as for sensor data acquisition and processing applications. For MPI-based models, tests using cloud virtual machines with resources equal to or lower than the smaller physical bases performed well relative to the other resources. However, as the cloud resources under testing did not reach the optimal number of processors for the present use cases, the HPC cluster remained the best option, as it fits better the requirements for the typical dimensions of computational grids for multi-scale (port to ocean) analysis. Federated cloud resources allowed a better performance for small pool sizes, allowing the combination of the processing power of several hosts. However, the performance does scale very badly if the choice relies in any combination that uses many processes (by using many hosts or many processes within each host), even if we use resources with some hardware assistance. Further testing is still necessary to explore this possibility in detail, taking into account the need to assure an adequate quality of service (QoS), especially to meet forecasting deadlines and real-time streaming bandwidth. We conclude that an evolution from the current cluster setup to a cloud-based architecture will satisfy most of our simulation requirements while offering a more flexible and affordable computing environment.