Luca Roverelli

Lessons learned implementing a science gateway for hydro-meteorological research

A full hydrometeorological (HM) simulation, from rainfall to impact on urban areas, is a multidisciplinary job, which relies on the execution of a workflow composed of complex and heterogeneous model engines. Moreover, the accuracy of the simulation is strongly dependent on an extensive set of configuration parameters, which have to be selected in a consistent way among the models. Within the Distributed Research Infrastructure for Hydro‐Meteorology project, a Web‐based science gateway was developed with the aim to support HM researchers in designing, executing, and managing HM experiments. The core of this science gateway is the portal, which takes care of generating all the configuration files and handles the execution of simulation steps on a heterogeneous computing infrastructure composed of high‐performance computing, Grid resources, and Cloud resources. This paper presents technological insights about the implementation of the portal, with an analysis of the adopted technologies and infrastructures. Our experience highlights the need of coherent policies in the management of data, computational resources, and software components that represent the ecosystem to develop science gateways. Copyright

Setting Up an Hydro-Meteo Experiment in Minutes: The DRIHM e-Infrastructure for HM Research

Predicting weather and climate and its impacts on the environment, including hazards such as floods and landslides, is a big challenge that can be efficiently supported by a distributed and heterogeneous infrastructure, exploiting several kinds of computational resources: HPC, Grids and Clouds. This can help researchers in speeding up experiments, improve resolution and accuracy, simulate with different numerical models and model chains. Such numerical models are complex with heavy computational requirements, huge numbers of parameters to tune, and not fully standardized interfaces. Hence, each research entity is usually focusing on a limited set of tools and hard-wired solutions to enable their interaction. The DRIHM approach is based on strong standardization, well defined interfaces, and an easy to use web interface for model configuration and experiment definition. A researcher can easily compare outputs from different hydrologic models forced by the same meteorological model, or compare different meteorological models to validate or improve her research. This paper presents the benefit of a web-based interface for hydro-meteorology research through a detailed analysis of the portal (based on liferay-gUse) developed by the DRIHM project.

From Lesson Learned to the Refactoring of the DRIHM Science Gateway for Hydro-meteorological Research

A full hydro-meteorological (HM) simulation, from rainfall to impact on urban areas, is a multidisciplinary activity which consists in the execution of a workflow composed by complex and heterogeneous model engines. Moreover an extensive set of configuration parameters have to be selected consistently among the models, otherwise the simulation can fail or produce unreliable results. The DRIHM portal is a Web-based science gateway aiming to support HM researchers in designing, executing and managing HM simulations. The first version of the portal was developed during the DRIHM project using the gUSE science gateway toolkit. The lesson we learned is guiding a refactoring process that, together with a review of the most relevant technologies for the development of a science gateway, represent the focus of this paper. Beside the technological aspects, the need of a strong interplay between ICT and other domain-specific communities clearly emerged, together with coherent policies in the management of data, computational resources and software components that represent the ecosystem of a science gateways.

Low-power portable devices for metagenomics analysis: Fog computing makes bioinformatics ready for the Internet of Things

Abstract Portable sequencing machines, such as the Oxford Nanopore MinION, are making the genome sequencing ubiquitous. This can be particularly interesting for identifying specific bacteria in air-filters or waters and for monitoring the microbioma composition in cultivated soils or in different animal samples, using a simple and portable approach. However, a main problem of these portable sequencing devices is that they stream huge amounts of data, which management can be actually challenging. Low-power System-on-Chip architectures represent a feasible way for designing a solution, based on the Fog computing paradigm, for processing locally the raw data, considering both the base calling step and the genome alignment part, and for sending only meaningful results over Internet. Cloud services can be then used to collect and integrate results in a Internet of Things framework, in order to trigger notifications or alarms and, in perspective, for more sophisticated applications based on statistical or machine learning approaches.

A science gateway for Exploring the X-ray Transient and variable sky using EGI Federated Cloud

Abstract Modern soft X-ray observatories can yield unique insights into time domain astrophysics, and a huge amount of information is stored – and largely unexploited – in data archives. Like a treasure-hunt, the EXTraS project harvested the hitherto unexplored temporal domain information buried in the serendipitous data collected by the European Photon Imaging Camera instrument onboard the ESA XMM-Newton, in 16 years of observations. All results have been released to the scientific community, together with new software analysis tools. This paper presents the architecture of the EXTraS science gateway, that has the goal to provide the software through a web based portal using the EGI Federated Cloud infrastructure. The main focus is on the light software architecture of the portal and on the technological insights for an effective use of the EGI ecosystem.

Clustering Protein Structures with Hadoop

Machine learning is a widely used technique in structural biology, since the analysis of large conformational ensembles originated from single protein structures (e.g. derived from NMR experiments or molecular dynamics simulations) can be approached by partitioning the original dataset into sensible subsets, revealing important structural and dynamics behaviours. Clustering is a good unsupervised approach for dealing with these ensembles of structures, in order to identify stable conformations and driving characteristics shared by the different structures. A common problem of the applications that implement protein clustering is the scalability of the performance, in particular concerning the data load into memory. In this work we show how it is possible to improve the parallel performance of the GROMOS clustering algorithm by using Hadoop. The preliminary results show the validity of this approach, providing a hint for future development in this field.