47 research outputs found
A Survey on the Integration of NAND Flash Storage in the Design of File Systems and the Host Storage Software Stack
With the ever-increasing amount of data generate in the world, estimated to reach over 200 Zettabytes by 2025, pressure on efficient data storage systems is intensifying. The shift from HDD to flash-based SSD provides one of the most fundamental shifts in storage technology, increasing performance capabilities significantly. However, flash storage comes with different characteristics than prior HDD storage technology. Therefore, storage software was unsuitable for leveraging the capabilities of flash storage. As a result, a plethora of storage applications have been design to better integrate with flash storage and align with flash characteristics. In this literature study we evaluate the effect the introduction of flash storage has had on the design of file systems, which providing one of the most essential mechanisms for managing persistent storage. We analyze the mechanisms for effectively managing flash storage, managing overheads of introduced design requirements, and leverage the capabilities of flash storage. Numerous methods have been adopted in file systems, however prominently revolve around similar design decisions, adhering to the flash hardware constrains, and limiting software intervention. Future design of storage software remains prominent with the constant growth in flash-based storage devices and interfaces, providing an increasing possibility to enhance flash integration in the host storage software stack
A Survey on the Integration of NAND Flash Storage in the Design of File Systems and the Host Storage Software Stack
With the ever-increasing amount of data generate in the world, estimated to
reach over 200 Zettabytes by 2025, pressure on efficient data storage systems
is intensifying. The shift from HDD to flash-based SSD provides one of the most
fundamental shifts in storage technology, increasing performance capabilities
significantly. However, flash storage comes with different characteristics than
prior HDD storage technology. Therefore, storage software was unsuitable for
leveraging the capabilities of flash storage. As a result, a plethora of
storage applications have been design to better integrate with flash storage
and align with flash characteristics.
In this literature study we evaluate the effect the introduction of flash
storage has had on the design of file systems, which providing one of the most
essential mechanisms for managing persistent storage. We analyze the mechanisms
for effectively managing flash storage, managing overheads of introduced design
requirements, and leverage the capabilities of flash storage. Numerous methods
have been adopted in file systems, however prominently revolve around similar
design decisions, adhering to the flash hardware constrains, and limiting
software intervention. Future design of storage software remains prominent with
the constant growth in flash-based storage devices and interfaces, providing an
increasing possibility to enhance flash integration in the host storage
software stack
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
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SoC-Based In-Storage Processing: Bringing Flexibility and Efficiency to Near-Data Processing
Data are among the most valuable assets in the modern world, and they have caused a revolutionary stage in human life. Nowadays, companies make knowledge-based decisions by analyzing a huge volume of data, super-scale data centers are used to process customers’ data to suggest products to them, government services rely on the data people provide to them, and there are many similar cases wherein data are used as an important asset. Data are originally stored in storage systems. To process data, application servers need to fetch the data from storage units, which imposes the cost of moving the data to the system. This cost has a direct relationship to the distance of the processing engines from the data, and this is the key motivation for the emergence of distributed processing platforms such as Hadoop, which bring the process closer to the data.In-storage processing (ISP) pushes the “bring the process to data” paradigm to its ultimate boundaries by utilizing processing engines inside the storage units to process data. The architecture of modern solid-state drives (SSDs) provides a suitable environment for implementing such technology. Thus, this dissertation focuses on SSD architectures that are able to run user applications in-place, which are called computational storage devices (CSDs). In this dissertation, we propose CSD architectures and investigate the benefits of deploying CSDs for running different applications. This research uses a practical approach that includes building fully functional prototypes of the proposed CSD architectures, developing storage systems equipped with the CSDs, and running different benchmarks to investigate the benefits of deploying the CSDs in the systems. This research proposes two different CSD architectures, namely CompStor and Catalina.These are the first CSDs to be equipped with a dedicated ISP engine for running user applications in-place that includes a quad-core ARM Cortex-A53 processor together with FPGA- and application-specific integrated circuit (ASIC) based accelerators. The proposed architectures run a full-fledged operating system inside, which provides a flexible environment for running a wide range of user applications in-place. The system-on-chip (SOC) based architecture of Catalina CSD, together with a software stack developed for seamless deployment of the CSD, makes it a platform for the implementation of different ISP concepts and ideas.To the best of our knowledge, Catalina is the only ISP platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and message passing interface (MPI) based applications in-place without any modifications to the underlying distributed processing framework. We performed extensive experimental tests using several datasets on both CompStor and Catalina CSDs. The experimental results show up to 2.2x and 4.3x improvements in performance and energy consumption, respectively, for running Hadoop MapReduce benchmarks using Catalina CSDs and up to 5.4x and 8.9x improvements for running 1-, 2-, and 3-dimensional DFT algorithms due to the Neon SIMD engines inside Catalina. Additionally, using FPGA-based accelerators, Catalina CSDs can improve the performance and energy consumption of a highly demanding image similarity search application up to 11x and 7x, respectively