Enabling Lock-Free Concurrent Fine-Grain Access to Massive Distributed Data: Application to Supernovae Detection

Bogdan Nicolae 1 Gabriel Antoniu 1 Luc Bougé 1
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 : We consider the problem of efficiently managing massive data in a large-scale distributed environment. We consider data strings of size in the order of Terabytes, shared and accessed by concurrent clients. On each individual access, a segment of a string, of the order of Megabytes, is read or modified. Our goal is to provide the clients with efficient fine-grain access the data string as concurrently as possible, without locking the string itself. This issue is crucial in the context of applications in the field of astronomy, databases, data mining and multimedia. We illustrate these requiremens with the case of an application for searching supernovae. Our solution relies on distributed, RAM-based data storage, while leveraging a DHT-based, parallel metadata management scheme. The proposed architecture and algorithms have been validated through a software prototype and evaluated in a cluster environment.
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/inria-00329698
Contributor : Bogdan Nicolae <>
Submitted on : Monday, October 13, 2008 - 11:32:13 AM
Last modification on : Friday, November 16, 2018 - 1:30:41 AM
Long-term archiving on : Tuesday, June 28, 2011 - 5:07:28 PM

Files

paper.pdf
Files produced by the author(s)

Identifiers

Citation

Bogdan Nicolae, Gabriel Antoniu, Luc Bougé. Enabling Lock-Free Concurrent Fine-Grain Access to Massive Distributed Data: Application to Supernovae Detection. CLUSTER '08: Proceedings of the 2008 IEEE International Conference on Cluster Computing, Sep 2008, Tsukuba, Japan. pp.310-315, ⟨10.1109/CLUSTR.2008.4663787⟩. ⟨inria-00329698⟩

Share

Metrics

Record views

491

Files downloads

268