A differentiated proposal of three dimension i/o performance characterization model focusing on storage environments

The I/O bottleneck remains a central issue in high-performance environments. Cloud computing, high-performance computing (HPC) and big data environments share many underneath difficulties to deliver data at a desirable time rate requested by high-performance applications. This increases the possibility of creating bottlenecks throughout the application feeding process by bottom hardware devices located in the storage system layer. In the last years, many researchers have been proposed solutions to improve the I/O architecture considering different approaches. Some of them take advantage of hardware devices while others focus on a sophisticated software approach. However, due to the complexity of dealing with high-performance environments, creating solutions to improve I/O performance in both software and hardware is challenging and gives researchers many opportunities. Classifying these improvements in different dimensions allows researchers to understand how these improvements have been built over the years and how it progresses. In addition, it also allows future efforts to be directed to research topics that have developed at a lower rate, balancing the general development process. This research present a three-dimension characterization model for classifying research works on I/O performance improvements for large scale storage computing facilities. This classification model can also be used as a guideline framework to summarize researches providing an overview of the actual scenario. We also used the proposed model to perform a systematic literature mapping that covered ten years of research on I/O performance improvements in storage environments. This study classified hundreds of distinct researches identifying which were the hardware, software, and storage systems that received more attention over the years, which were the most researches proposals elements and where these elements were evaluated. In order to justify the importance of this model and the development of solutions that targets I/O performance improvements, we evaluated a subset of these improvements using a a real and complete experimentation environment, the Grid5000. Analysis over different scenarios using a synthetic I/O benchmark demonstrates how the throughput and latency parameters behaves when performing different I/O operations using distinct storage technologies and approaches.O gargalo de E/S continua sendo um problema central em ambientes de alto desempenho. Os ambientes de computação em nuvem, computação de alto desempenho (HPC) e big data compartilham muitas dificuldades para fornecer dados em uma taxa de tempo desejável solicitada por aplicações de alto desempenho. Isso aumenta a possibilidade de criar gargalos em todo o processo de alimentação de aplicativos pelos dispositivos de hardware inferiores localizados na camada do sistema de armazenamento. Nos últimos anos, muitos pesquisadores propuseram soluções para melhorar a arquitetura de E/S considerando diferentes abordagens. Alguns deles aproveitam os dispositivos de hardware, enquanto outros se concentram em uma abordagem sofisticada de software. No entanto, devido à complexidade de lidar com ambientes de alto desempenho, criar soluções para melhorar o desempenho de E/S em software e hardware é um desafio e oferece aos pesquisadores muitas oportunidades. A classificação dessas melhorias em diferentes dimensões permite que os pesquisadores entendam como essas melhorias foram construídas ao longo dos anos e como elas progridem. Além disso, também permite que futuros esforços sejam direcionados para tópicos de pesquisa que se desenvolveram em menor proporção, equilibrando o processo geral de desenvolvimento. Esta pesquisa apresenta um modelo de caracterização tridimensional para classificar trabalhos de pesquisa sobre melhorias de desempenho de E/S para instalações de computação de armazenamento em larga escala. Esse modelo de classificação também pode ser usado como uma estrutura de diretrizes para resumir as pesquisas, fornecendo uma visão geral do cenário real. Também usamos o modelo proposto para realizar um mapeamento sistemático da literatura que abrangeu dez anos de pesquisa sobre melhorias no desempenho de E/S em ambientes de armazenamento. Este estudo classificou centenas de pesquisas distintas, identificando quais eram os dispositivos de hardware, software e sistemas de armazenamento que receberam mais atenção ao longo dos anos, quais foram os elementos de proposta mais pesquisados e onde esses elementos foram avaliados. Para justificar a importância desse modelo e o desenvolvimento de soluções que visam melhorias no desempenho de E/S, avaliamos um subconjunto dessas melhorias usando um ambiente de experimentação real e completo, o Grid5000. Análises em cenários diferentes usando um benchmark de E/S sintética demonstra como os parâmetros de vazão e latência se comportam ao executar diferentes operações de E/S usando tecnologias e abordagens distintas de armazenamento

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Optimizations for Energy-Aware, High-Performance and Reliable Distributed Storage Systems

With the decreasing cost and wide-spread use of commodity hard drives, it has become possible to create very large-scale storage systems with less expense. However, as we approach exabyte-scale storage systems, maintaining important features such as energy-efficiency, performance, reliability and usability became increasingly difficult. Despite the decreasing cost of storage systems, the energy consumption of these systems still needs to be addressed in order to retain cost-effectiveness. Any improvements in a storage system can be outweighed by high energy costs. On the other hand, large-scale storage systems can benefit more from the object storage features for improved performance and usability. One area of concern is metadata performance bottleneck of applications reading large directories or creating a large number of files. Similarly, computation on big data where data needs to be transferred between compute and storage clusters adversely affects I/O performance. As the storage systems become more complex and larger, transferring data between remote compute and storage tiers becomes impractical. Furthermore, storage systems implement reliability typically at the file system or client level. This approach might not always be practical in terms of performance. Lastly, object storage features are usually tailored to specific use cases that makes it harder to use them in various contexts. In this thesis, we are presenting several approaches to enhance energy-efficiency, performance, reliability and usability of large-scale storage systems. To begin with, we improve the energy-efficiency of storage systems by moving I/O load to a subset of the storage nodes with energy-aware node allocation methods and turn off the unused nodes, while preserving load balance on demand. To address the metadata performance issue associated with large creates and directory reads, we represent directories with object storage collections and implement lazy creation of objects. Similarly, in-situ computation on large-scale data is enabled by using object storage features to integrate a computational framework with the existing object storage layer to eliminate the need to transfer data between compute and storage silos for better performance. We then present parity-based redundancy using object storage features to achieve reliability with less performance impact. Finally, unified storage brings together the object storage features to meet the needs of distinct use cases; such as cloud storage, big data or high-performance computing to alleviate the unnecessary fragmentation of storage resources. We evaluate each proposed approach thoroughly and validate their effectiveness in terms of improving energy-efficiency, performance, reliability and usability of a large-scale storage system

The Fifth Workshop on HPC Best Practices: File Systems and Archives

The workshop on High Performance Computing (HPC) Best Practices on File Systems and Archives was the fifth in a series sponsored jointly by the Department Of Energy (DOE) Office of Science and DOE National Nuclear Security Administration. The workshop gathered technical and management experts for operations of HPC file systems and archives from around the world. Attendees identified and discussed best practices in use at their facilities, and documented findings for the DOE and HPC community in this report