Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

International audienceBrain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a two weeks deployment on hundreds of virtual machines

Supporting NGS pipelines in the cloud

[EN] Cloud4Science is a research activity funded by Microsoft that develops a unique online platform providing cloud services, datasets, tools, documentations, tutorial and best practices to meet the needs of researchers across the globe in terms of storing and managing datasets. Cloud4Science initially focuses on dedicated services for the bioinformatics community. Its ultimate goal is to support a wide range of scientific communities as the natural first choice for scientific data curation, analysis andThe authors want to thank Microsoft and the cloud4Science project for funding this research activity.Blanquer Espert, I.; Brasche, G.; Cala, J.; Gagliardi, F.; Gannon, D.; Hiden, H.; Soncu, H.... (2013). Supporting NGS pipelines in the cloud. EMBnet Journal. 19(Supplement A):14-16. doi:10.14806/ej.19.A.625S141619Supplement

TomusBlobs: Scalable Data-intensive Processing on Azure Clouds

International audienceThe emergence of cloud computing has brought the opportunity to use large-scale compute infrastructures for a broader and broader spectrum of applications and users. As the cloud paradigm gets attractive for the "elasticity'' in resource usage and associated costs (the users only pay for resources actually used), cloud applications still suffer from the high latencies and low performance of cloud storage services. As Big Data analysis on clouds becomes more and more relevant in many application areas, enabling high-throughput massive data processing on cloud data becomes a critical issue, as it impacts the overall application performance. In this paper we address this challenge at the level of cloud storage. We introduce a concurrency-optimized data storage system (called TomusBlobs) which federates the virtual disks associated to the Virtual Machines running the application code on the cloud. We demonstrate the performance benefits of our solution for efficient data-intensive processing by building an optimized prototype MapReduce framework for Microsoft's Azure cloud platform based on TomusBlobs. Finally, we specifically address the limitations of state-of-the-art MapReduce frameworks for reduce-intensive workloads, by proposing MapIterativeReduce as an extension of the MapReduce model. We validate the above contributions through large-scale experiments with synthetic benchmarks and with real-world applications on the Azure commercial cloud, using resources distributed across multiple data centers: they demonstrate that our solutions bring substantial benefits to data intensive applications compared to approaches relying on state-of-the-art cloud object storage

Adaptive File Management for Scientific Workflows on the Azure Cloud

International audienceScientific workflows typically communicate data between tasks using files. Currently, on public clouds, this is achieved by using the cloud storage services, which are unable to exploit the workflow semantics and are subject to low throughput and high latencies. To overcome these limitations, we propose an alternative leveraging data locality through direct file transfers between the compute nodes. We rely on the observation that workflows generate a set of common data access patterns that our solution exploits in conjunction with context information to self-adapt, choose the most adequate transfer protocol and expose the data layout within the virtual machines to the workflow engines. This file management system was integrated within the Microsoft Generic Worker workflow engine and was validated using synthetic benchmarks and a real-life application on the Azure cloud. The results show it can bring significant performance gains: up to 5x file transfer speedup compared to solutions based on standard cloud storage and over 25% application timespan reduction compared to Hadoop on Azure