Exascale storage systems: an analytical study of expenses

The computational power and storage capability of supercomputers are growing at a different pace, with storage lagging behind; the widening gap necessitates new approaches to keep the investment and running costs for storage systems at bay. In this paper, we aim to unify previous models and compare different approaches for solving these problems. By extrapolating the characteristics of the German Climate Computing Center's previous supercomputers to the future, cost factors are identified and quantified in order to foster adequate research and development. Using models to estimate the execution costs of two prototypical use cases, we are discussing the potential of three concepts: re-computation, data deduplication and data compression

Deduplication potential of HPC applications' checkpoints

© 2016 IEEE. HPC systems contain an increasing number of components, decreasing the mean time between failures. Checkpoint mechanisms help to overcome such failures for long-running applications. A viable solution to remove the resulting pressure from the I/O backends is to deduplicate the checkpoints. However, there is little knowledge about the potential to save I/Os for HPC applications by using deduplication within the checkpointing process. In this paper, we perform a broad study about the deduplication behavior of HPC application checkpointing and its impact on system design

A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable

GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

Analyzing data properties using statistical sampling techniques – illustrated on scientific file formats and compression features

Understanding the characteristics of data stored in data centers helps computer scientists in identifying the most suitable storage infrastructure to deal with these workloads. For example, knowing the relevance of file formats allows optimizing the relevant formats but also helps in a procurement to define benchmarks that cover these formats. Existing studies that investigate performance improvements and techniques for data reduction such as deduplication and compression operate on a small set of data. Some of those studies claim the selected data is representative and scale their result to the scale of the data center. One hurdle of running novel schemes on the complete data is the vast amount of data stored and, thus, the resources required to analyze the complete data set. Even if this would be feasible, the costs for running many of those experiments must be justified. This paper investigates stochastic sampling methods to compute and analyze quantities of interest on file numbers but also on the occupied storage space. It will be demonstrated that on our production system, scanning 1 % of files and data volume is sufficient to deduct conclusions. This speeds up the analysis process and reduces costs of such studies significantly. The contributions of this paper are: (1) the systematic investigation of the inherent analysis error when operating only on a subset of data, (2) the demonstration of methods that help future studies to mitigate this error, (3) the illustration of the approach on a study for scientific file types and compression for a data center

Resource-Efficient Replication and Migration of Virtual Machines.

Continuous replication and live migration of Virtual Machines (VMs) are two vital tools in a virtualized environment, but they are resource-expensive. Continuously replicating a VM's checkpointed state to a backup host maintains high-availability (HA) of the VM despite host failures, but checkpoint replication can generate significant network traffic. Each replicated VM also incurs a 100% memory overhead, since the backup unproductively reserves the same amount of memory to hold the redundant VM state. Live migration, though being widely used for load-balancing, power-saving, etc., can also generate excessive network traffic, by transferring VM state iteratively. In addition, it can incur a long completion time and degrade application performance. This thesis explores ways to replicate VMs for HA using resources efficiently, and to migrate VMs fast, with minimal execution disruption and using resources efficiently. First, we investigate the tradeoffs in using different compression methods to reduce the network traffic of checkpoint replication in a HA system. We evaluate gzip, delta and similarity compressions based on metrics that are specifically important in a HA system, and then suggest guidelines for their selection. Next, we propose HydraVM, a storage-based HA approach that eliminates the unproductive memory reservation made in backup hosts. HydraVM maintains a recent image of a protected VM in a shared storage by taking and consolidating incremental VM checkpoints. When a failure occurs, HydraVM quickly resumes the execution of a failed VM by loading a small amount of essential VM state from the storage. As the VM executes, the VM state not yet loaded is supplied on-demand. Finally, we propose application-assisted live migration, which skips transfer of VM memory that need not be migrated to execute running applications at the destination. We develop a generic framework for the proposed approach, and then use the framework to build JAVMM, a system that migrates VMs running Java applications skipping transfer of garbage in Java memory. Our evaluation results show that compared to Xen live migration, which is agnostic of running applications, JAVMM can reduce the completion time, network traffic and application downtime caused by Java VM migration, all by up to over 90%.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111575/1/karenhou_1.pd

HIODS: hybrid inline and offline deduplication system

Dissertação de mestrado integrado em Engenharia InformáticaDeduplication is a technique that allows finding and removing duplicate data at storage systems. With the current exponential growth of digital information, this mechanism is becoming more and more desirable for reducing the infrastructural costs of persisting such data. Therefore, deduplication is now being widely applied to several storage appliances serving applications with different requirements (e.g., archival, backup, primary storage). However, deduplication requires additional processing logic for each storage request in order to detect and eliminate duplicate content. Traditionally, this processing is done in the I/O critical path (inline), thus introducing a performance penalty on the throughput and latency of requests being served by the storage appliance. An alternative solution is to do this process as a background task, thus outside of the I/O critical path (offline), at the cost of requiring additional storage space as duplicate content is not found and eliminated immediately. However, the choice of what type of strategy to use is typically done manually and does not take into consideration changes in the applications' workloads. This dissertation proposes HIODS, a hybrid deduplication solution capable of automati cally changing between inline and offline deduplication according to the requirements (e.g., desired storage I/O throughput goal) of applications and their dynamic workloads. The goal is to choose the best strategy that fulfills the targeted I/O performance objectives while optimizing deduplication space savings. Finally, a prototype of HIODS is implemented and evaluated extensively with different storage workloads. Results show that HIODS is able to change its deduplication mode dy namically, according to the storage workload being served, while balancing I/O performance and space savings requirements efficiently.A deduplicação é uma técnica que permite encontrar e remover dados duplicados guardados nos sistemas de armazenamento. Com o crescimento exponencial da informação digital que vivemos atualmente, este mecanismo está a tornar-se cada vez mais popular para reduzir os custos das infraestruturas onde esses dados se encontram alojados. De facto, a deduplicação é, hoje em dia, usada numa grande variedade de serviços de armazenamento que servem diferentes aplicações com requisitos particulares (ex.: arquivo, backup, armazenamento primário). No entanto, a deduplicação adiciona uma camada de processamento extra a cada pedido de armazenamento, de modo a conseguir detetar e eliminar o conteúdo redundante. Tradicionalmente, este processo é realizado durante o caminho crítico do I/O (inline), causando perdas de desempenho e aumentos na latência dos pedidos processados. Uma alternativa é alterar o processamento para segundo plano, aliviando assim os custos no caminho crítico do I/O (offline). Esta solução requer espaço de armazenamento adicional, visto que os duplicados não são encontrados nem eliminados imediatamente. No entanto, a estratégia a seguir é escolhida de forma manual, não tendo em consideração qualquer possível mudança na carga de trabalho das aplicações. Esta dissertação propõe assim o HIODS, um sistema de deduplicação híbrido capaz de alterar entre o modo inline e offline de forma automática considerando os requisitos (ex.: débito do sistema de armazenamento desejado) das aplicações e das suas cargas de trabalho dinâmicas. Por fim, um protótipo do HIODS é implementado e avaliado exaustivamente. Os resultados mostram que o HIODS é capaz de alterar o modo de deduplicação de forma dinâmica e de acordo com a carga de trabalho, considerando os requisitos de desempenho e a eliminação eficiente dos dados duplicados

An Analysis of Storage Virtualization

Investigating technologies and writing expansive documentation on their capabilities is like hitting a moving target. Technology is evolving, growing, and expanding what it can do each and every day. This makes it very difficult when trying to snap a line and investigate competing technologies. Storage virtualization is one of those moving targets. Large corporations develop software and hardware solutions that try to one up the competition by releasing firmware and patch updates to include their latest developments. Some of their latest innovations include differing RAID levels, virtualized storage, data compression, data deduplication, file deduplication, thin provisioning, new file system types, tiered storage, solid state disk, and software updates to coincide these technologies with their applicable hardware. Even data center environmental considerations like reusable energies, data center environmental characteristics, and geographic locations are being used by companies both small and large to reduce operating costs and limit environmental impacts. Companies are even moving to an entire cloud based setup to limit their environmental impact as it could be cost prohibited to maintain your own corporate infrastructure. The trifecta of integrating smart storage architectures to include storage virtualization technologies, reducing footprint to promote energy savings, and migrating to cloud based services will ensure a long-term sustainable storage subsystem