BlobSeer: How to Enable Efficient Versioning for Large Object Storage under Heavy Access Concurrency

To accommodate the needs of large-scale distributed P2P systems, scalable data management strategies are required, allowing applications to efficiently cope with continuously growing, highly dis tributed data. This paper addresses the problem of efficiently stor ing and accessing very large binary data objects (blobs). It proposesan efficient versioning scheme allowing a large number of clients to concurrently read, write and append data to huge blobs that are fragmented and distributed at a very large scale. Scalability under heavy concurrency is achieved thanks to an original metadata scheme, based on a distributed segment tree built on top of a Distributed Hash Table (DHT). Our approach has been implemented and experimented within our BlobSeer prototype on the Grid5000 testbed, using up to 175 nodes.

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

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.

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

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.