BlobSeer: How to Enable Efficient Versioning for Large Object Storage under Heavy Access Concurrency (0905.1113v1)

Abstract: 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 Grid'5000 testbed, using up to 175 nodes.

• The paper introduces an efficient versioning mechanism that enables inexpensive branching and rollback for large binary objects.
• It employs a distributed segment tree over a DHT to achieve parallel metadata management and reduce redundancy.
• Experiments demonstrate BlobSeer’s scalability with high throughput and atomic operations under heavy concurrent access.

The paper "BlobSeer: How to Enable Efficient Versioning for Large Object Storage under Heavy Access Concurrency" presents a system designed to provide scalable and concurrent data management for large binary large objects (blobs) in distributed peer-to-peer (P2P) environments. This system, called BlobSeer, addresses the challenges posed by large-scale data storage and management, especially when dealing with unstructured data under conditions of heavy access concurrency.

Overview of BlobSeer's Approach

BlobSeer introduces an efficient versioning mechanism for managing large binary objects in environments with high concurrency. The key contributions of BlobSeer include:

1. Versioning Scheme: The system allows for the efficient versioning of blobs, facilitating the rollback of data changes and enabling inexpensive branching. This feature allows different computations to proceed independently on different versions of the blob without compromising performance.
2. Distributed Segment Tree on a DHT: To handle metadata efficiently, BlobSeer employs a novel metadata scheme based on a distributed segment tree constructed atop a Distributed Hash Table (DHT). This approach allows the system to distribute not only the data but also its metadata, enabling parallel, direct access I/O paths and effective use of storage space.
3. High Concurrency and Scalability: BlobSeer's design achieves scalability under heavy concurrent access by ensuring atomicity through a decentralized approach to metadata management and data access.

Key Features and Algorithms

The system is characterized by the following main features and algorithms:

• Data Striping: Blobs are divided into fixed-size pages, which are distributed among various storage nodes. This facilitates parallel data access and storage efficiency.
• Efficient Metadata Management: The metadata is managed using a segment-tree structure that allows sharing of metadata across different versions, reducing redundancy and improving access efficiency.
• Parallel Data Access: Both read and write operations are performed in parallel, minimizing synchronization overhead and maximizing throughput.
• Atomicity and Isolation: The system guarantees that all operations are atomic, appearing instantaneous despite concurrent modifications by multiple clients.

System Architecture

BlobSeer's architecture is divided into several roles:

• Clients: These perform operations such as creating blobs and reading, writing, or appending data.
• Data Providers: These store the pages generated by the system.
• Provider Manager: Manages the allocation and distribution of storage space across data providers to ensure an even distribution and reduce conflicts.
• Metadata Provider: Stores the metadata regarding blob versions and facilitates access to the corresponding data.
• Version Manager: Coordinates version management by assigning version numbers to updates and ensuring their atomic and consistent publication.

Experimental Results

Experiments conducted on the Grid’5000 testbed demonstrated that BlobSeer maintains high throughput under varying degrees of concurrency and blob sizes. These experiments illustrated:

• Scalability: The system sustained performance even as the number of concurrent clients increased, showing a minor drop in bandwidth as concurrency and blob size grew.
• Efficient Handling of Large Data Sets: BlobSeer efficiently managed large and dynamic data sets, showcasing its capability to handle extensive and concurrent update patterns.

Conclusion

BlobSeer represents a significant step forward in managing massive and mutable data objects in distributed systems. Its combination of efficient versioning, scalable metadata management, and high-performance concurrency control makes it a robust solution for modern cloud and P2P environments, where data grows continuously and requires high availability and immediate access. The paper highlights the potential improvements in further extending the system to support dynamic environments with volatility and failures.

Overall, BlobSeer effectively addresses the challenges of storing and accessing large-scale, unstructured data in a distributed context, maintaining system performance and data consistency even under high concurrency.