Research centers performing biomedical research collide with the problem of the treatment of large amounts of data. Several scientific fields in biomedical adopt technologies that can analyze samples in a more accurate way thanks to the high granularity which the current equipment provide the results. The cloud computing technology allows to create scalable and flexible infrastructures and data management services. In recent years the number of solutions that can be included within the phenomenon of cloud computing has increased. There are many cases of distributed solutions with high storage and processing capacity and the possibility of serving a large number of users. In this paper authors describe a biological networks modeling tool, implemented with the aid of MapReduce algorithms that works on a cluster in a cloud computing infrastructure.

Evaluating Scalability of a Cloud Based Platform for Biological Networks Analysis / Bertone, Fabrizio; Caragnano, Giuseppe; Ruiu, Pietro; Terzo, Olivier; Vasciaveo, Alessandro; Benso, Alfredo. - STAMPA. - (2015), pp. 464-468. (Intervento presentato al convegno 4th International Workshop on Hybrid Cloud Computing Infrastructure for E-science Application (HCCIEA) tenutosi a Brazil nel July 2015) [10.1109/CISIS.2015.68].

Evaluating Scalability of a Cloud Based Platform for Biological Networks Analysis

BERTONE, FABRIZIO;RUIU, PIETRO;TERZO, OLIVIER;VASCIAVEO, ALESSANDRO;BENSO, Alfredo
2015

Abstract

Research centers performing biomedical research collide with the problem of the treatment of large amounts of data. Several scientific fields in biomedical adopt technologies that can analyze samples in a more accurate way thanks to the high granularity which the current equipment provide the results. The cloud computing technology allows to create scalable and flexible infrastructures and data management services. In recent years the number of solutions that can be included within the phenomenon of cloud computing has increased. There are many cases of distributed solutions with high storage and processing capacity and the possibility of serving a large number of users. In this paper authors describe a biological networks modeling tool, implemented with the aid of MapReduce algorithms that works on a cluster in a cloud computing infrastructure.
2015
978-1-4799-8869-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2620706
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