Elevating commodity storage with the SALSA host translation layer

To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices employ a translation layer that conceals the idiosyncrasies of their mediums and enables random access. Device translation layers are, however, inherently constrained: resources on the drive are scarce, they cannot be adapted to application requirements, and lack visibility across multiple devices. As a result, performance and durability of many storage devices is severely degraded. In this paper, we present SALSA: a translation layer that executes on the host and allows unmodified applications to better utilize commodity storage. SALSA supports a wide range of single- and multi-device optimizations and, because is implemented in software, can adapt to specific workloads. We describe SALSA's design, and demonstrate its significant benefits using microbenchmarks and case studies based on three applications: MySQL, the Swift object store, and a video server.Comment: Presented at 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

DC: Small: Energy-aware Coordinated Caching in Cluster-based Storage Systems

The main goal of this project is to improve the performance and energy efficiency of I/O (Input/Output) operations of large-scale cluster computing platforms. The major activities include: 1) characterize the memory access workloads; 2) investigate the new and emerging new storage and memory devices, such as SSD and PCM, on I/O performance. (3) study energy-efficient buffer and cache replacement algorithms, (4) leveraging SSD as a new caching device to improve the energy efficiency and performance of I/O performanc

DC:Small: Energy-aware Coordinated Caching in Cluster-based Storage Systems

As the computing capacity increases rapidly in large-scale cluster computing platforms, power management becomes an increasingly important concern. This project focuses on the research of reducing disk and memory power consumption through energy-aware cooperative caching in cluster-based storage systems. The project leverages I/O characteristics of scientific applications and dynamic power management features of disk drives and memory chips to reduce I/O energy consumption. This project involves three components: (1) investigate program context based pattern detection to predict I/O activities in the operating systems, (2) investigate disk energy aware cooperative cache management schemes, and (3) prototype the management schemes and incorporate into cluster-based file systems. This project has broader impact through its contributions to the energy-aware computing, graduate education, and undergraduate education via an existing NSF-REU site award

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

CSR: Small: Collaborative Research: SANE: Semantic-Aware Namespace in Exascale File Systems

Explosive growth in volume and complexity of data exacerbates the key challenge facing the management of massive data in a way that fundamentally improves the ease and efficacy of their usage. Exascale storage systems in general rely on hierarchically structured namespace that leads to severe performance bottlenecks and makes it hard to support real-time queries on multi-dimensional attributes. Thus, existing storage systems, characterized by the hierarchical directory tree structure, are not scalable in light of the explosive growth in both the volume and the complexity of data. As a result, directory-tree based hierarchical namespace has become restrictive, difficult to use, and limited in scalability for today\u27s large-scale file systems. This project investigates a novel semantic-aware namespace scheme to provide dynamic and adaptive namespace management and support typical file-based operations in Exascale file systems. The project leverages semantic correlations among files and exploits the evolution of metadata attributes to support customized namespace management, with the end goal of efficiently facilitating file identification and end users data lookup. This project provides significant performance improvements for existing file systems in Exascale file systems. Since Exascale file systems constitute one of the backbones of the high-performance computing infrastructure, the semantic-aware techniques also benefits a great number of scientific and engineering data-intensive applications. This project strengthens the ongoing development of high performance computing infrastructures at both UNL and UMaine. The project enhances undergraduate and graduate education at both participating institutions and outreach to K-12 in UMaine via an ongoing NSF-funded ITEST program

ON REDUCING THE DECODING COMPLEXITY OF SHINGLED MAGNETIC RECORDING SYSTEM

Shingled Magnetic Recording (SMR) has been recognised as one of the alternative technologies to achieve an areal density beyond the limit of the perpendicular recording technique, 1 Tb/in2, which has an advantage of extending the use of the conventional method media and read/write head. This work presents SMR system subject to both Inter Symbol Interference (ISI) and Inter Track Interference (ITI) and investigates different equalisation/detection techniques in order to reduce the complexity of this system. To investigate the ITI in shingled systems, one-track one-head system model has been extended into two-track one-head system model to have two interfering tracks. Consequently, six novel decoding techniques have been applied to the new system in order to find the Maximum Likelihood (ML) sequence. The decoding complexity of the six techniques has been investigated and then measured. The results show that the complexity is reduced by more than three times with 0.5 dB loss in performance. To measure this complexity practically, perpendicular recording system has been implemented in hardware. Hardware architectures are designed for that system with successful Quartus II fitter which are: Perpendicular Magnetic Recording (PMR) channel, digital filter equaliser with and without Additive White Gaussian Noise (AWGN) and ideal channel architectures. Two different hardware designs are implemented for Viterbi Algorithm (VA), however, Quartus II fitter for both of them was unsuccessful. It is found that, Simulink/Digital Signal Processing (DSP) Builder based designs are not efficient for complex algorithms and the eligible solution for such designs is writing Hardware Description Language (HDL) codes for those algorithms.The Iraqi Governmen

On Coding and Detection Techniques for Two-Dimensional Magnetic Recording

Edited version embargoed until 15.04.2020 Full version: Access restricted permanently due to 3rd party copyright restrictions. Restriction set on 15/04/2019 by AS, Doctoral CollegeThe areal density growth of magnetic recording systems is fast approaching the superparamagnetic limit for conventional magnetic disks. This is due to the increasing demand for high data storage capacity. Two-dimensional Magnetic Recording (TDMR) is a new technology aimed at increasing the areal density of magnetic recording systems beyond the limit of current disk technology using conventional disk media. However, it relies on advanced coding and signal processing techniques to achieve areal density gains. Current state of the art signal processing for TDMR channel employed iterative decoding with Low Density Parity Check (LDPC) codes, coupled with 2D equalisers and full 2D Maximum Likelihood (ML) detectors. The shortcoming of these algorithms is their computation complexity especially with regards to the ML detectors which is exponential with respect to the number of bits involved. Therefore, robust low-complexity coding, equalisation and detection algorithms are crucial for successful future deployment of the TDMR scheme. This present work is aimed at finding efficient and low-complexity coding, equalisation, detection and decoding techniques for improving the performance of TDMR channel and magnetic recording channel in general. A forward error correction (FEC) scheme of two concatenated single parity bit systems along track separated by an interleaver has been presented for channel with perpendicular magnetic recording (PMR) media. Joint detection decoding algorithm using constrained MAP detector for simultaneous detection and decoding of data with single parity bit system has been proposed. It is shown that using the proposed FEC scheme with the constrained MAP detector/decoder can achieve a gain of up to 3dB over un-coded MAP decoder for 1D interference channel. A further gain of 1.5 dB was achieved by concatenating two interleavers with extra parity bit when data density along track is high. The use of single bit parity code as a run length limited code as well as an error correction code is demonstrated to simplify detection complexity and improve system performance. A low-complexity 2D detection technique for TDMR system with Shingled Magnetic Recording Media (SMR) was also proposed. The technique used the concatenation of 2D MAP detector along track with regular MAP detector across tracks to reduce the complexity order of using full 2D detection from exponential to linear. It is shown that using this technique can improve track density with limited complexity. Two methods of FEC for TDMR channel using two single parity bit systems have been discussed. One using two concatenated single parity bits along track only, separated by a Dithered Relative Prime (DRP) interleaver and the other use the single parity bits in both directions without the DRP interleaver. Consequent to the FEC coding on the channel, a 2D multi-track MAP joint detector decoder has been proposed for simultaneous detection and decoding of the coded single parity bit data. A gain of up to 5dB was achieved using the FEC scheme with the 2D multi-track MAP joint detector decoder over un-coded 2D multi-track MAP detector in TDMR channel. In a situation with high density in both directions, it is shown that FEC coding using two concatenated single parity bits along track separated by DRP interleaver performed better than when the single parity bits are used in both directions without the DRP interleaver.9mobile Nigeri

The Applications of Workload Characterization in The World of Massive Data Storage

University of Minnesota Ph.D. dissertation. August 2015. Major: Computer Science. Advisor: David Du. 1 computer file (PDF); x, 116 pages.The digital world is expanding exponentially because of the growth of various applications in domains including scientific fields, enterprise environment and internet services. Importantly, these applications have drastically different storage requirements including parallel I/O performance and storage capacity. Various technologies have been developed in order to better satisfy different storage requirements. I/O middleware software, parallel file systems and storage arrays are developed to improve I/O performance by increasing I/O parallelism at different levels. New storage media and data recording technologies such as shingled magnetic recording (SMR) are also developed to increase the storage capacity. This work focuses on improving existing technologies and designing new schemes based on I/O workload characterizations in corresponding storage environments. The contributions of this work can be summarized into four pieces, two on improving parallel I/O performance and two on increasing storage capacity. First, we design a comprehensive parallel I/O workload characterization and generation framework (called PIONEER) which can be used to synthesize a particular parallel I/O workload with desired I/O characteristics or precisely emulate a High Performance Computing (HPC) application of interest. Second, we propose a non-intrusive I/O middleware (called IO-Engine) to automatically improve a given parallel I/O workload in Lustre which is a widely used HPC or parallel I/O system. IO-Engine can explore the correlations between different software layers in the deep I/O path, as well as workload patterns at runtime to transparently transform the workload patterns and tune related I/O parameters in the system. Third, we design several novel static address mapping schemes for shingled write disks (SWDs) to minimize the write amplification overhead in hard drives adopting SMR technology. Fourth, we propose a track-level shingled translation layer (T-STL) for SWDs with hybrid update strategy (in-place update plus out-of-place update). T-STL uses dynamic address mapping scheme and performs garbage collection operations by migrating selected disk tracks. This scheme can provider larger storage capacity and better overall performance with the same effective storage percentages when compared to the static address mapping schemes