Towards Fast De-Duplication Using Low Energy Coprocessor
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Nxadmin CLI Reference Guide Unity Iv Contents
HYPER-UNIFIED STORAGE nxadmin Command Line Interface Reference Guide NEXSAN | 325 E. Hillcrest Drive, Suite #150 | Thousand Oaks, CA 91360 USA Printed Thursday, July 26, 2018 | www.nexsan.com Copyright © 2010—2018 Nexsan Technologies, Inc. All rights reserved. Trademarks Nexsan® is a trademark or registered trademark of Nexsan Technologies, Inc. The Nexsan logo is a registered trademark of Nexsan Technologies, Inc. All other trademarks and registered trademarks are the property of their respective owners. Patents This product is protected by one or more of the following patents, and other pending patent applications worldwide: United States patents US8,191,841, US8,120,922; United Kingdom patents GB2466535B, GB2467622B, GB2467404B, GB2296798B, GB2297636B About this document Unauthorized use, duplication, or modification of this document in whole or in part without the written consent of Nexsan Corporation is strictly prohibited. Nexsan Technologies, Inc. reserves the right to make changes to this manual, as well as the equipment and software described in this manual, at any time without notice. This manual may contain links to web sites that were current at the time of publication, but have since been moved or become inactive. It may also contain links to sites owned and operated by third parties. Nexsan is not responsible for the content of any such third-party site. Contents Contents Contents iii Chapter 1: Accessing the nxadmin and nxcmd CLIs 15 Connecting to the Unity Storage System using SSH 15 Prerequisite 15 Connecting to the Unity -
Smaller Flight Data Recorders{
Available online at http://docs.lib.purdue.edu/jate Journal of Aviation Technology and Engineering 2:2 (2013) 45–55 Smaller Flight Data Recorders{ Yair Wiseman and Alon Barkai Bar-Ilan University Abstract Data captured by flight data recorders are generally stored on the system’s embedded hard disk. A common problem is the lack of storage space on the disk. This lack of space for data storage leads to either a constant effort to reduce the space used by data, or to increased costs due to acquisition of additional space, which is not always possible. File compression can solve the problem, but carries with it the potential drawback of the increased overhead required when writing the data to the disk, putting an excessive load on the system and degrading system performance. The author suggests the use of an efficient compressed file system that both compresses data in real time and ensures that there will be minimal impact on the performance of other tasks. Keywords: flight data recorder, data compression, file system Introduction A flight data recorder is a small line-replaceable computer unit employed in aircraft. Its function is recording pilots’ inputs, electronic inputs, sensor positions and instructions sent to any electronic systems on the aircraft. It is unofficially referred to as a "black box". Flight data recorders are designed to be small and thoroughly fabricated to withstand the influence of high speed impacts and extreme temperatures. A flight data recorder from a commercial aircraft can be seen in Figure 1. State-of-the-art high density flash memory devices have permitted the solid state flight data recorder (SSFDR) to be implemented with much larger memory capacity. -
The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed. -
The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
sensors Article The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities Ammar Nasif *, Zulaiha Ali Othman and Nor Samsiah Sani Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi 43600, Malaysia; [email protected] (Z.A.O.); [email protected] (N.S.S.) * Correspondence: [email protected] Abstract: Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, Citation: Nasif, A.; Othman, Z.A.; we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Sani, N.S. -
The Illusion of Space and the Science of Data Compression And
The Illusion of Space and the Science of Data Compression and Deduplication Bruce Yellin EMC Proven Professional Knowledge Sharing 2010 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents What Was Old Is New Again ......................................................................................................... 4 The Business Benefits of Saving Space ....................................................................................... 6 Data Compression Strategies ....................................................................................................... 9 Data Compression Basics ....................................................................................................... 10 Compression Bakeoff .............................................................................................................. 13 Data Deduplication Strategies .................................................................................................... 16 Deduplication - Theory of Operation ....................................................................................... 16 File Level Deduplication - Single Instance Storage ................................................................. 21 Fixed-Block Deduplication ....................................................................................................... 23 Variable-Block Deduplication .................................................................................................. 24 Content-Aware Deduplication -
Data Compression in Solid State Storage
Data Compression in Solid State Storage John Fryar [email protected] Santa Clara, CA August 2013 1 Acknowledgements This presentation would not have been possible without the counsel, hard work and graciousness of the following individuals and/or organizations: Raymond Savarda Sandgate Technologies Santa Clara, CA August 2013 2 Disclaimers The opinions expressed herein are those of the author and do not necessarily represent those of any other organization or individual unless specifically cited. A thorough attempt to acknowledge all sources has been made. That said, we’re all human… Santa Clara, CA August 2013 3 Learning Objectives At the conclusion of this tutorial the audience will have been exposed to: • The different types of Data Compression • Common Data Compression Algorithms • The Deflate/Inflate (GZIP/GUNZIP) algorithms in detail • Implementation Options (Software/Hardware) • Impacts of design parameters in Performance • SSD benefits and challenges • Resources for Further Study Santa Clara, CA August 2013 4 Agenda • Background, Definitions, & Context • Data Compression Overview • Data Compression Algorithm Survey • Deflate/Inflate (GZIP/GUNZIP) in depth • Software Implementations • HW Implementations • Tradeoffs & Advanced Topics • SSD Benefits and Challenges • Conclusions Santa Clara, CA August 2013 5 Definitions Item Description Comments Open A system which will compress Must strictly adhere to standards System data for use by other entities. on compress / decompress I.E. the compressed data will algorithms exit the system Interoperability among vendors mandated for Open Systems Closed A system which utilizes Can support a limited, optimized System compressed data internally but subset of standard. does not expose compressed Also allows custom algorithms data to the outside world No Interoperability req’d. -
Use Style: Paper Title
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Diponegoro University Institutional Repository On The Implementation of ZFS (Zettabyte File System) Storage System Eko D. Widianto, Agung B. Prasetijo, and Ahmad Ghufroni Department of Computer Engineering, Faculty of Engineering – Diponegoro University Jl. Prof. Soedarto, SH, Tembalang, Semarang, Indonesia E-mail: [email protected]; [email protected]; [email protected] Abstract – Digital data storage is very critical in computer 32 KB, ZFS is 200MB/s faster than EXT3. The throughput of systems. Storage devices used to store data may at any time suffer write process in ZFS is as good as EXT3 for the size of I/O from damage caused by its lifetime, resource failure or factory less than 32 KB, whilst, for the size of I/O larger than 32 KB defects. Such damage may lead to loss of important data. The risk ZFS is 150 MB/s faster compared to EXT3. of data loss in the event of device damage can be minimized by Literature [2] explored the performance, capacity and building a storage system that supports redundancy. The design of storage based on ZFS (Zettabyte File System) aims at building a integrity of ZFS raidz. The tests for its read and write speed is storage system that supports redundancy and data integrity without as follows: in mirror mode using two 4TB hard drive, ZFS has requiring additional RAID controllers. When the system fails on 488 MB/s for read speed and 106MB/s for write speed. -
Mimicking Datasets for Content Generation in Storage Benchmarks
SDGen: Mimicking Datasets for Content Generation in Storage Benchmarks Raúl Gracia-Tinedo, Universitat Rovira i Virgili; Danny Harnik, Dalit Naor, and Dmitry Sotnikov, IBM Research Haifa; Sivan Toledo and Aviad Zuck, Tel-Aviv University https://www.usenix.org/conference/fast15/technical-sessions/presentation/gracia-tinedo This paper is included in the Proceedings of the 13th USENIX Conference on File and Storage Technologies (FAST ’15). February 16–19, 2015 • Santa Clara, CA, USA ISBN 978-1-931971-201 Open access to the Proceedings of the 13th USENIX Conference on File and Storage Technologies is sponsored by USENIX SDGen: Mimicking Datasets for Content Generation in Storage Benchmarks Raul´ Gracia-Tinedo Danny Harnik, Dalit Naor, Dmitry Sotnikov Universitat Rovira i Virgili (Spain) IBM Research-Haifa (Israel) [email protected] dannyh, dalit, dmitrys @il.ibm.com { } Sivan Toledo, Aviad Zuck Tel-Aviv University (Israel) stoledo, aviadzuc @tau.ac.il { } Abstract However, most storage benchmarks do not pay partic- Storage system benchmarks either use samples of pro- ular attention to the contents generated during their exe- prietary data or synthesize artificial data in simple ways cution [35] (see examples in Table 1). For instance, Im- (such as using zeros or random data). However, many pressions [7] implements accurate statistical methods to storage systems behave completely differently on such model the structure of a file system, but the contents of artificial data than they do on real-world data. This is the files are by default zeros or statically generated by third- case with systems that include data reduction techniques, party applications. Another example is OLTPBench [15], such as compression and/or deduplication. -
QZFS: QAT Accelerated Compression in File System for Application
QZFS: QAT Accelerated Compression in File System for Application Agnostic and Cost Efficient Data Storage Xiaokang Hu and Fuzong Wang, Shanghai Jiao Tong University, Intel Asia-Pacific R&D Ltd.; Weigang Li, Intel Asia-Pacific R&D Ltd.; Jian Li and Haibing Guan, Shanghai Jiao Tong University https://www.usenix.org/conference/atc19/presentation/wang-fuzong This paper is included in the Proceedings of the 2019 USENIX Annual Technical Conference. July 10–12, 2019 • Renton, WA, USA ISBN 978-1-939133-03-8 Open access to the Proceedings of the 2019 USENIX Annual Technical Conference is sponsored by USENIX. QZFS: QAT Accelerated Compression in File System for Application Agnostic and Cost Efficient Data Storage Xiaokang Hu Fuzong Wang∗ Weigang Li Shanghai Jiao Tong University Shanghai Jiao Tong University Intel Asia-Pacific R&D Ltd. Intel Asia-Pacific R&D Ltd. Intel Asia-Pacific R&D Ltd. Jian Li Haibing Guan Shanghai Jiao Tong University Shanghai Jiao Tong University Abstract Hadoop [3], Spark [4] or stream processing job [40] is com- pressed, the data processing performance can be effectively Data compression can not only provide space efficiency with enhanced as the compression not only saves bandwidth but lower Total Cost of Ownership (TCO) but also enhance I/O also decreases the number of read/write operations from/to performance because of the reduced read/write operations. storage systems. However, lossless compression algorithms with high com- It is widely recognized that the benefits of data compression pression ratio (e.g. gzip) inevitably incur high CPU resource come at the expense of computational cost [1,9], especially consumption. -
Data Compression for Climate Data
Data compression for climate data Article Published Version Creative Commons: Attribution-Noncommercial 3.0 Open access Kuhn, M., Kunkel, J. M. and Ludwig, T. (2016) Data compression for climate data. Supercomputing Frontiers and Innovations, 3 (1). pp. 75-94. ISSN 2313-8734 doi: https://doi.org/10.14529/jsfi160105 Available at http://centaur.reading.ac.uk/77684/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . Identification Number/DOI: https://doi.org/10.14529/jsfi160105 <https://doi.org/10.14529/jsfi160105> Publisher: South Urals University All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online DOI: 10.14529/jsfi160105 Data Compression for Climate Data Michael Kuhn1, Julian Kunkel2, Thomas Ludwig2 © The Authors 2016. This paper is published with open access at SuperFri.org The different rates of increase for computational power and storage capabilities of super- computers turn data storage into a technical and economical problem. As storage capabilities are lagging behind, investments and operational costs for storage systems have increased to keep up with the supercomputers’ I/O requirements. One promising approach is to reduce the amount of data that is stored. In this paper, we take a look at the impact of compression on performance and costs of high performance systems. -
High Speed Lossless Image Compression
High Speed Lossless Image Compression Hendrik Siedelmann12, Alexander Wender1, and Martin Fuchs1 1 University of Stuttgart, Germany 2 Heidelberg University, Germany Abstract. We introduce a simple approach to lossless image compression, which makes use of SIMD vectorization at every processing step to provide very high speed on modern CPUs. This is achieved by basing the compression on delta cod- ing for prediction and bit packing for the actual compression, allowing a tuneable tradeoff between efficiency and speed, via the block size used for bit packing. The maximum achievable speed surpasses main memory bandwidth on the tested CPU, as well as the speed of all previous methods that achieve at least the same coding efficiency. 1 Introduction For applications which need to process large amounts of image data such as high speed video or high resolution light fields, the I/O subsystem can easily become the bottle- neck, even when using a RAID0 or SSDs for storage. This makes compression an at- tractive tool to widen this bottleneck, increasing overall performance. As lossy com- pression incurs signal degradation and may interfere with further processing, we will only consider lossless compression in the following. While dictionary based compres- sion methods like lzo [18] can reach a high bandwidth, the compression ratio of such generic compression methods is quite low compared to dedicated image compression methods. However, research in lossless image compression has mainly been concerned with the maximization of the compression ratio, and even relatively fast image com- pression schemes like jpeg-ls are not able to keep up with the transfer rates of fast I/O configurations. -
Smaller Flight Data Recorders{
Available online at http://docs.lib.purdue.edu/jate Journal of Aviation Technology and Engineering 2:2 (2013) 45–55 Smaller Flight Data Recorders{ Yair Wiseman and Alon Barkai Bar-Ilan University Abstract Data captured by flight data recorders are generally stored on the system’s embedded hard disk. A common problem is the lack of storage space on the disk. This lack of space for data storage leads to either a constant effort to reduce the space used by data, or to increased costs due to acquisition of additional space, which is not always possible. File compression can solve the problem, but carries with it the potential drawback of the increased overhead required when writing the data to the disk, putting an excessive load on the system and degrading system performance. The author suggests the use of an efficient compressed file system that both compresses data in real time and ensures that there will be minimal impact on the performance of other tasks. Keywords: flight data recorder, data compression, file system Introduction A flight data recorder is a small line-replaceable computer unit employed in aircraft. Its function is recording pilots’ inputs, electronic inputs, sensor positions and instructions sent to any electronic systems on the aircraft. It is unofficially referred to as a "black box". Flight data recorders are designed to be small and thoroughly fabricated to withstand the influence of high speed impacts and extreme temperatures. A flight data recorder from a commercial aircraft can be seen in Figure 1. State-of-the-art high density flash memory devices have permitted the solid state flight data recorder (SSFDR) to be implemented with much larger memory capacity.