CRAID: Online RAID upgrades using dynamic hot data reorganization

Current algorithms used to upgrade RAID arrays typically require large amounts of data to be migrated, even those that move only the minimum amount of data required to keep a balanced data load. This paper presents CRAID, a self-optimizing RAID array that performs an online block reorganization of frequently used, long-term accessed data in order to reduce this migration even further. To achieve this objective, CRAID tracks frequently used, long-term data blocks and copies them to a dedicated partition spread across all the disks in the array. When new disks are added, CRAID only needs to extend this process to the new devices to redistribute this partition, thus greatly reducing the overhead of the upgrade process. In addition, the reorganized access patterns within this partition improve the array’s performance, amortizing the copy overhead and allowing CRAID to offer a performance competitive with traditional RAIDs. We describe CRAID’s motivation and design and we evaluate it by replaying seven real-world workloads including a file server, a web server and a user share. Our experiments show that CRAID can successfully detect hot data variations and begin using new disks as soon as they are added to the array. Also, the usage of a dedicated partition improves the sequentiality of relevant data access, which amortizes the cost of reorganizations. Finally, we prove that a full-HDD CRAID array with a small distributed partition (<1.28% per disk) can compete in performance with an ideally restriped RAID-5 and a hybrid RAID-5 with a small SSD cache.Peer ReviewedPostprint (published version

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

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

Shingled Magnetic Recording disks for Mass Storage Systems

Disk drives have seen a dramatic increase in storage density over the last five decades, but to continue the growth seems difficult if not impossible because of physical limitations. One way to increase storage density is using a shingled magnetic recording (SMR) disk. Shingled writing is a promising technique that trades off the inability to update in-place for narrower tracks and thus a much higher data density. It is particularly appealing as it can be adopted while utilizing essentially the same physical recording mechanisms currently in use. Because of its manner of writing, an SMR disk would be unable to update a written track without overwriting neighboring tracks, potentially requiring the rewrite of all the tracks to the end of a band where the end of a band is an area left unwritten to allow for a non-overlapped final track. Random reads are still possible on such devices, but the handling of writes becomes particularly critical. In this manuscript, we first look at a variety of potential workloads, drawn from real-world traces, and evaluate their impact on SMR disk models. Later, we evaluate the behavior of SMR disks when used in an array configuration or when faced with heavily interleaved workloads. Specifically, we demonstrate the dramatically different effects that different workloads can have upon the opposing approaches of remapping and restoring blocks, and how write-heavy workloads can (under the right conditions, and contrary to intuition) result in a performance advantage for an SMR disk

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

Flash Cache Hybrid Storage System For Video-On-Demand Server

A video-on-demand (VOD) system allows users to access any video at any time. This system enables users to view videos in the real-time streaming mode for extended durations. One of the important components of the VOD systems is the hybrid storage server. This component consists of HDD, SSD, and RAM, collectively fulfilling the requirement of simultaneous fast data access and large data distribution to numerous users. However, current hybrid storage systems still pose numerous challenges. (1) The integration and roles of the HDD, SSD, and RAM are relatively weak in terms of optimizing fast access prior to streaming to a large number of simultaneous users. (2) The HDD and SSD exhibit poor data layout and streaming controller in supporting the production of a high number of simultaneous streams. This thesis proposes (1) an enhanced hybrid storage system (EHSS) for the VOD servers. The HDD, SSD, and RAM are managed and integrated using an effective mechanism, in which the top 10% most popular videos are utilized to achieve fast data access and service to a large number of simultaneous users. (2) The flash cache hybrid storage system (FCHSS) is also proposed. Unlike the EHSS architecture, the FCHSS architecture has no RAM, which is removed and replaced by a flash-based SSD. The flash-based SSD is used instead of the RAM as a cache for the HDD to achieve fast data access and service to a large number of simultaneous users. (3) The new data layout stores thousands of video segments in the HDD and SSD

A Study of Scalability and Cost-effectiveness of Large-scale Scientific Applications over Heterogeneous Computing Environment

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data. In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures that these software tools need for good performance. 3) Transferring the big scientific dataset among clusters situated at geographically disparate locations. In the first part, I develop scalable algorithms to process huge amounts of scientific big data using the power of recent analytic tools such as, Hadoop, Giraph, NoSQL, etc. At a broader level, these algorithms take the advantage of locality-based computing that can scale with increasing amount of data. The thesis mainly addresses the challenges involved in large-scale genome analysis applications such as, genomic error correction and genome assembly which made their way to the forefront of big data challenges recently. In the second part of the thesis, I perform a systematic benchmark study using the above-mentioned algorithms on different distributed cyberinfrastructures to pinpoint the limitations in a traditional HPC cluster to process big data. Then I propose the solution to those limitations by balancing the I/O bandwidth of the solid state drive (SSD) with the computational speed of high-performance CPUs. A theoretical model has been also proposed to help the HPC system designers who are striving for system balance. In the third part of the thesis, I develop a high throughput architecture for transferring these big scientific datasets among geographically disparate clusters. The architecture leverages the power of Ethereum\u27s Blockchain technology and Swarm\u27s peer-to-peer (P2P) storage technology to transfer the data in secure, tamper-proof fashion. Instead of optimizing the computation in a single cluster, in this part, my major motivation is to foster translational research and data interoperability in collaboration with multiple institutions