Bogdan Nicolae

BlobSeer: Next-generation data management for large scale infrastructures

As data volumes increase at a high speed in more and more application fields of science, engineering, information services, etc., the challenges posed by data-intensive computing gain increasing importance. The emergence of highly scalable infrastructures, e.g. for cloud computing and for petascale computing and beyond, introduces additional issues for which scalable data management becomes an immediate need. This paper makes several contributions. First, it proposes a set of principles for designing highly scalable distributed storage systems that are optimized for heavy data access concurrency. In particular, we highlight the potentially large benefits of using versioning in this context. Second, based on these principles, we propose a set of versioning algorithms, both for data and metadata, that enable a high throughput under concurrency. Finally, we implement and evaluate these algorithms in the BlobSeer prototype, that we integrate as a storage backend in the Hadoop MapReduce framework. We perform extensive microbenchmarks as well as experiments with real MapReduce applications: they demonstrate that applying the principles defended in our approach brings substantial benefits to data intensive applications.

BlobSeer: Bringing high throughput under heavy concurrency to Hadoop Map-Reduce applications

Hadoop is a software framework supporting the Map-Reduce programming model. It relies on the Hadoop Distributed File System (HDFS) as its primary storage system. The efficiency of HDFS is crucial for the performance of Map-Reduce applications. We substitute the original HDFS layer of Hadoop with a new, concurrency-optimized data storage layer based on the BlobSeer data management service. Thereby, the efficiency of Hadoop is significantly improved for data-intensive Map-Reduce applications, which naturally exhibit a high degree of data access concurrency. Moreover, BlobSeers features (built-in versioning, its support for concurrent append operations) open the possibility for Hadoop to further extend its functionalities. We report on extensive experiments conducted on the Grid5000 testbed. The results illustrate the benefits of our approach over the original HDFS-based implementation of Hadoop.

High throughput data-compression for cloud storage

As data volumes processed by large-scale distributed data-intensive applications grow at high-speed, an increasing I/O pressure is put on the underlying storage service, which is responsible for data management. One particularly difficult challenge, that the storage service has to deal with, is to sustain a high I/O throughput in spite of heavy access concurrency to massive data. In order to do so, massively parallel data transfers need to be performed, which invariably lead to a high bandwidth utilization. With the emergence of cloud computing, data intensive applications become attractive for a wide public that does not have the resources to maintain expensive large scale distributed infrastructures to run such applications. In this context, minimizing the storage space and bandwidth utilization is highly relevant, as these resources are paid for according to the consumption. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on I/O throughput when under heavy access concurrency. Our solution builds on BlobSeer, a highly parallel distributed data management service specifically designed to enable reading, writing and appending huge data sequences that are fragmented and distributed at a large scale. We demonstrate the benefits of our approach by performing extensive experimentations on the Grid5000 testbed.

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 distributed data. This paper addresses the problem of efficiently storing and accessing very large binary data objects (blobs). It proposes an 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.

Enabling High Data Throughput in Desktop Grids through Decentralized Data and Metadata Management: The BlobSeer Approach

Whereas traditional Desktop Grids rely on centralized servers for data management, some recent progress has been made to enable distributed, large input data, using to peer-to-peer (P2P) protocols and Content Distribution Networks (CDN). We make a step further and propose a generic, yet efficient data storage which enables the use of Desktop Grids for applications with high output data requirements, where the access grain and the access patterns may be random. Our solution builds on a blob management service enabling a large number of concurrent clients to efficiently read/write and append huge data that are fragmented and distributed at a large scale. Scalability under heavy concurrency is achieved thanks to an original metadata scheme using a distributed segment tree built on top of a Distributed Hash Table (DHT). The proposed approach has been implemented and its benefits have successfully been demonstrated within our BlobSeer prototype on the Grid5000 testbed.

Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services

The cloud computing model aims to make large-scale data-intensive computing affordable even for users with limited financial resources, that cannot invest into expensive infrastructures necesssary to run them. In this context, MapReduce is emerging as a highly scalable programming paradigm that enables high-throughput data-intensive processing as a cloud service. Its performance is highly dependent on the underlying storage service, responsible to efficiently support massively parallel data accesses by guaranteeing a high throughput under heavy access concurrency. In this context, quality of service plays a crucial role: the storage service needs to sustain a stable throughput for each individual accesss, in addition to achieving a high aggregated throughput under concurrency. In this paper we propose a technique to address this problem using component monitoring, application-side feedback and behavior pattern analysis to automatically infer useful knowledge about the causes of poor quality of service and provide an easy way to reason in about potential improvements. We apply our proposal to Blob Seer, a representative data storage service specifically designed to achieve high aggregated throughputs and show through extensive experimentation substantial improvements in the stability of individual data read accesses under MapReduce workloads.

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 on the Grid5000 testbed 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 requirements 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.

BlobSeer: Efficient data management for data-intensive applications distributed at large-scale

As the rate, scale and variety of data increases in complexity, the need for flexible applications that can crunch huge amounts of heterogeneous data fast and cost-effective is of utmost importance. Such applications are data-intensive: in a typical scenario, they continuously acquire massive datasets (e.g. by crawling the Web or analyzing access logs) while performing computations over these changing datasets (e.g. building up-to-date search indexes). In order to achieve scalability and performance, data acquisitions and computations need to be distributed at large scale in infrastructures comprising hundreds and thousands of machines. As these applications focus on data rather then on computation, a heavy burden is put on the storage service employed to handle data management, because it must efficiently deal with massively parallel data accesses. In order to achieve this, a series of issues need to be address properly: scalable aggregation of storage space from the participating nodes with minimal overhead, the ability to store huge data objects, efficient fine-grain access to data subsets, high throughput even under heavy access concurrency, versioning, as well as fault tolerance and a high quality of service for access throughput. This paper introduces BlobSeer, an efficient distributed data management service that addresses the issues presented above. In BlobSeer, long sequences of bytes representing unstructured data are called blobs (Binary Large OBject).

Towards a Grid File System Based on a Large-Scale BLOB Management Service

This paper addresses the problem of building a grid file system for applications that need to manipulate huge data, distributed and concurrently accessed at a very large scale. In this paper we explore how this goal could be reached through a cooperation between the Gfarm grid file system and BlobSeer, a distributed object management system specifically designed for huge data management under heavy concurrency. The resulting BLOB-based grid file system exhibits scalable file access performance in scenarios where huge files are subject to massive, concurrent, fine-grain accesses. This is demonstrated through preliminary experiments of our prototype, conducted on the Grid’5000 testbed.