Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme

Bogdan Nicolae 1 Gabriel Antoniu 1, * Luc Bougé 1
* Corresponding author
1 PARIS - Programming distributed parallel systems for large scale numerical simulation
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, ENS Cachan - École normale supérieure - Cachan, Inria Rennes – Bretagne Atlantique
Abstract : This paper addresses the problem of efficiently storing and accessing massive data blocks in a large-scale distributed environment, while providing efficient fine-grain access to data subsets. This issue is crucial in the context of applications in the field of databases, data mining and multimedia. We propose a data sharing service based on distributed, RAM-based storage of data, while leveraging a DHT-based, natively parallel metadata management scheme. As opposed to the most commonly used grid storage infrastructures that provide mechanisms for explicit data localization and transfer, we provide a transparent access model, where data are accessed through global identifiers. Our proposal has been validated through a prototype implementation whose preliminary evaluation provides promising results.
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Bogdan Nicolae, Gabriel Antoniu, Luc Bougé. Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme. VECPAR '08: Proceedings of the 8th International Conference on High Performance Computing for Computational Science, Jun 2008, Toulouse, France. pp.532-543, ⟨10.1007/978-3-540-92859-1_47⟩. ⟨inria-00323248⟩

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