MUSIC (2014-2016) with LNCC, UFRJ, UFF, CEFET (Brazil)
MUSIC (MUltiSite Cloud (MUSIC) data management) is an associated team (“équipe associée”) funded by Inria and FAPERJ, between Zenith and 4 teams in the state of Rio de Janeiro (LNCC, COPPE/UFRJ, UFF and CEFET) since january 2014. MUSIC is headed by Esther Pacitti (Zenith) and Fabio Porto (LNCC).
The research project deals with scientific data management in the context of the cloud. The cloud has become a good match for managing big data since it provides unlimited computing, storage and network resources on demand. By centralizing all data in a large-scale data-center, the cloud significantly simplifies the task of system administration. But for scientific data, where different organizations may have their own data-centers, a distributed (multisite) cloud model where each site is visible from outside, may be preferred. The main objective of this research and scientific collaboration is to study the use of different distributed and parallel architectures for managing and analyzing scientific data, including support for heterogeneous data; distributed scientific workflows, and complex big data analysis. In particular, we study the use of a multisite cloud architecture that can be used to host a variety of scientific applications that benefit from computing, storage, and networking resources that span multiple data centers.
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Permanent link to this article: https://team.inria.fr/zenith/music/
1. Multisite cloud architecture In [Liu 2015], we define a multisite cloud architecture that is composed of traditional clouds, e.g., a pay-per-use cloud service such as Amazon EC2 and Microsoft Azure, private data-centers, e.g. a cloud of a scientific organization like Inria, COPPE or LNCC, and client desktop machines that have authorized access to the …
8 December 2016, MUSIC seminar by Esther Pacitti, FGV, Rio de Janeiro 4 November 2016, MUSIC seminar by Marta Mattoso, Inria, Montpellier 25 August 2016, MUSIC Workshop, LNCC, Petropolis, Rio de Janeiro 23 September 2015, MUSIC Meeting, Sophia Antipolis 19 August 2015, MUSIC Workshop, UFF, Niteroi, Rio de Janeiro 20 October 2014, MUSIC Workshop, LNCC, Petropolis, Rio de …
The research challenge is the design of new techniques for scientific data that must be done in a distributed and parallel manner, by leveraging multiple parallel machines in the cloud. In particular, this requires studying the impact of intersite data movement in the performance/cost tradeoffs of our algorithms. Our approach is to capitalize on the principles …
LNCC, Petrópolis, RJ Fabio Porto (senior researcher) Kary Ocaña (researcher) Daniel Gaspar (PhD student) Hermano Lustosa (PhD student) COPPE/UFRJ, Rio de Janeiro, RJ Alvaro Coutinho (professor) Marta Mattoso (professor) Flavio Costa (PhD student) Vitor Silva (PhD student) Renan Souza (PhD student) UFF, Niterói, RJ Daniel Oliveira (professor) Fabricio da Silva (PhD student) CEFET, Rio de Janeiro, …
2016 [Campisano 2016] R. Campisano, F. Porto, E. Pacitti, F. Masseglia, and E. Ogasawara. Spatial Sequential Pattern Mining for Seismic Data. SBBD Conference, to appear, 2016. [Liu 2016a] J. Liu, E. Pacitti, P. Valduriez, D. de Oliveira, M. Mattoso. Multi-Objective Scheduling of Scientific Workflows in Multisite Clouds. Future Generation Computer Systems, Elsevier, 63: 76-95, 2016. [Liu 2016b] J. Liu, E. …
