The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools or techniques to support the design of the underlying infrastructure configuration backing such systems. In particular, the focus in this paper is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying Quality of Service constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method.
Ciavotta, M., Gianniti, E., Ardagna, D. (2016). D-SPACE4Cloud: A design tool for big data applications. In 16th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2016; Granada; Spain; 14 December 2016 through 16 December 2016 (pp.614-629). Springer Verlag [10.1007/978-3-319-49583-5_48].
D-SPACE4Cloud: A design tool for big data applications
Ciavotta, M;
2016
Abstract
The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools or techniques to support the design of the underlying infrastructure configuration backing such systems. In particular, the focus in this paper is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying Quality of Service constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method.
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