Managing Workflows on top of a Cloud Computing Orchestrator for using heterogeneous environments on e-Science

[EN] Scientific workflows (SWFs) are widely used to model processes in e-Science. SWFs are executed by means of workflow management systems (WMSs), which orchestrate the workload on top of computing infrastructures. The advent of cloud computing infrastructures has opened the door of using on-demand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilisation and elasticity. In this article, we present an ad hoc solution for managing workflows exploiting the capabilities of cloud orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (clusters) to minimise costs and response time. The work does not propose yet another WMS but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The article shows several configuration experiments from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The article computes running time and cost suggesting best practices.This paper wants to acknowledge the support of the EUBrazilCC project, funded by the European Commission (STREP 614048) and the Brazilian MCT/CNPq N. 13/2012, for the use of its infrastructure. The authors would like also to thank the Spanish 'Ministerio de Economia y Competitividad' for the project 'Clusters Virtuales Elasticos y Migrables sobre Infraestructuras Cloud Hibridas' with reference TIN2013-44390-R.Carrión Collado, AA.; Caballer Fernández, M.; Blanquer Espert, I.; Kotowski, N.; Jardim, R.; Dávila, AMR. (2017). Managing Workflows on top of a Cloud Computing Orchestrator for using heterogeneous environments on e-Science. International Journal of Web and Grid Services. 13(4):375-402. doi:10.1504/IJWGS.2017.10003225S37540213

StreamFlow: cross-breeding cloud with HPC

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The proliferation of sensor technologies and advancements in data collection methods have enabled the accumulation of very large amounts of data. Increasingly, these datasets are considered for scientific research. However, the design of the system architecture to achieve high performance in terms of parallelization, query processing time, aggregation of heterogeneous data types (e.g., time series, images, structured data, among others), and difficulty in reproducing scientific research remain a major challenge. This is specifically true for health sciences research, where the systems must be i) easy to use with the flexibility to manipulate data at the most granular level, ii) agnostic of programming language kernel, iii) scalable, and iv) compliant with the HIPAA privacy law. In this paper, we review the existing literature for such big data systems for scientific research in health sciences and identify the gaps of the current system landscape. We propose a novel architecture for software-hardware-data ecosystem using open source technologies such as Apache Hadoop, Kubernetes and JupyterHub in a distributed environment. We also evaluate the system using a large clinical data set of 69M patients.Comment: This paper is accepted in ACM-BCB 202

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