Zfs: New Features in Replication
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Quantum Dxi4500 User's Guide
User’s Guide User’s Guide User’s Guide User’s Guide QuantumQuantum DXDXii45004500 DX i -Series 6-66904-01 Rev C DXi4500 User’s Guide, 6-66904-01 Rev C, August 2010, Product of USA. Quantum Corporation provides this publication “as is” without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability or fitness for a particular purpose. Quantum Corporation may revise this publication from time to time without notice. COPYRIGHT STATEMENT © 2010 Quantum Corporation. All rights reserved. Your right to copy this manual is limited by copyright law. Making copies or adaptations without prior written authorization of Quantum Corporation is prohibited by law and constitutes a punishable violation of the law. TRADEMARK STATEMENT Quantum, the Quantum logo, DLT, DLTtape, the DLTtape logo, Scalar, and StorNext are registered trademarks of Quantum Corporation, registered in the U.S. and other countries. Backup. Recovery. Archive. It’s What We Do., the DLT logo, DLTSage, DXi, DXi-Series, Dynamic Powerdown, FastSense, FlexLink, GoVault, MediaShield, Optyon, Pocket-sized. Well-armored, SDLT, SiteCare, SmartVerify, StorageCare, Super DLTtape, SuperLoader, and Vision are trademarks of Quantum. LTO and Ultrium are trademarks of HP, IBM, and Quantum in the U.S. and other countries. All other trademarks are the property of their respective companies. Specifications are subject to change without notice. ii Quantum DXi4500 User’s Guide Contents Preface xv Chapter 1 DXi4500 System Description 1 Overview . 1 Features and Benefits . 3 Data Reduction . 3 Data Deduplication . 3 Compression. 4 Space Reclamation . 4 Remote Replication . 5 DXi4500 System . -
Replication Using Group Communication Over a Partitioned Network
Replication Using Group Communication Over a Partitioned Network Thesis submitted for the degree “Doctor of Philosophy” Yair Amir Submitted to the Senate of the Hebrew University of Jerusalem (1995). This work was carried out under the supervision of Professor Danny Dolev ii Acknowledgments I am deeply grateful to Danny Dolev, my advisor and mentor. I thank Danny for believing in my research, for spending so many hours on it, and for giving it the theoretical touch. His warm support and patient guidance helped me through. I hope I managed to adopt some of his professional attitude and integrity. I thank Daila Malki for her help during the early stages of the Transis project. Thanks to Idit Keidar for helping me sharpen some of the issues of the replication server. I enjoyed my collaboration with Ofir Amir on developing the coloring model of the replication server. Many thanks to Roman Vitenberg for his valuable insights regarding the extended virtual synchrony model and the replication algorithm. I benefited a lot from many discussions with Ahmad Khalaila regarding distributed systems and other issues. My thanks go to David Breitgand, Gregory Chokler, Yair Gofen, Nabil Huleihel and Rimon Orni, for their contribution to the Transis project and to my research. I am grateful to Michael Melliar-Smith and Louise Moser from the Department of Electrical and Computer Engineering, University of California, Santa Barbara. During two summers, several mutual visits and extensive electronic correspondence, Louise and Michael were involved in almost every aspect of my research, and unofficially served as my co-advisors. The work with Deb Agarwal and Paul Ciarfella on the Totem protocol contributed a lot to my understanding of high-speed group communication. -
Replication Vs. Caching Strategies for Distributed Metadata Management in Cloud Storage Systems
ShardFS vs. IndexFS: Replication vs. Caching Strategies for Distributed Metadata Management in Cloud Storage Systems Lin Xiao, Kai Ren, Qing Zheng, Garth A. Gibson Carnegie Mellon University {lxiao, kair, zhengq, garth}@cs.cmu.edu Abstract 1. Introduction The rapid growth of cloud storage systems calls for fast and Modern distributed storage systems commonly use an archi- scalable namespace processing. While few commercial file tecture that decouples metadata access from file read and systems offer anything better than federating individually write operations [21, 25, 46]. In such a file system, clients non-scalable namespace servers, a recent academic file sys- acquire permission and file location information from meta- tem, IndexFS, demonstrates scalable namespace processing data servers before accessing file contents, so slower meta- based on client caching of directory entries and permissions data service can degrade performance for the data path. Pop- (directory lookup state) with no per-client state in servers. In ular new distributed file systems such as HDFS [25] and the this paper we explore explicit replication of directory lookup first generation of the Google file system [21] have used cen- state in all servers as an alternative to caching this infor- tralized single-node metadata services and focused on scal- mation in all clients. Both eliminate most repeated RPCs to ing only the data path. However, the single-node metadata different servers in order to resolve hierarchical permission server design limits the scalability of the file system in terms tests. Our realization for server replicated directory lookup of the number of objects stored and concurrent accesses to state, ShardFS, employs a novel file system specific hybrid the file system [40]. -
ZFS 2018 and Onward
SEE TEXT ONLY 2018 and Onward BY ALLAN JUDE 2018 is going to be a big year for ZFS RAID-Z Expansion not only FreeBSD, but for OpenZFS • 2018Q4 as well. OpenZFS is the collaboration of developers from IllumOS, FreeBSD, The ability to add an additional disk to an existing RAID-Z VDEV to grow its size with- Linux, Mac OS X, many industry ven- out changing its redundancy level. For dors, and a number of other projects example, a RAID-Z2 consisting of 6 disks to maintain and advance the open- could be expanded to contain 7 disks, source version of Sun’s ZFS filesystem. increasing the available space. This is accomplished by reflowing the data across Improved Pool Import the disks, so as to not change an individual • 2018Q1 block’s offset from the start of the VDEV. The new disk will appear as a new column on the A new patch set changes the way pools are right, and the data will be relocated to main- imported, such that they depend less on con- tain the offset within the VDEV. Similar to figuration data from the system. Instead, the reflowing a paragraph by making it wider, possibly outdated configuration from the sys- without changing the order of the words. In tem is used to find the pool, and partially open the end this means all of the new space shows it read-only. Then the correct on-disk configura- up at the end of the disk, without any frag- tion is loaded. This reduces the number of mentation. -
Replication As a Prelude to Erasure Coding in Data-Intensive Scalable Computing
DiskReduce: Replication as a Prelude to Erasure Coding in Data-Intensive Scalable Computing Bin Fan Wittawat Tantisiriroj Lin Xiao Garth Gibson fbinfan , wtantisi , lxiao , garth.gibsong @ cs.cmu.edu CMU-PDL-11-112 October 2011 Parallel Data Laboratory Carnegie Mellon University Pittsburgh, PA 15213-3890 Acknowledgements: We would like to thank several people who made significant contributions. Robert Chansler, Raj Merchia and Hong Tang from Yahoo! provide various help. Dhruba Borthakur from Facebook provides statistics and feedback. Bin Fu and Brendan Meeder gave us their scientific applications and data-sets for experimental evaluation. The work in this paper is based on research supported in part by the Betty and Gordon Moore Foundation, by the Department of Energy, under award number DE-FC02-06ER25767, by the Los Alamos National Laboratory, by the Qatar National Research Fund, under award number NPRP 09-1116-1-172, under contract number 54515- 001-07, by the National Science Foundation under awards CCF-1019104, CCF-0964474, OCI-0852543, CNS-0546551 and SCI-0430781, and by Google and Yahoo! research awards. We also thank the members and companies of the PDL Consortium (including APC, EMC, Facebook, Google, Hewlett-Packard, Hitachi, IBM, Intel, Microsoft, NEC, NetApp, Oracle, Panasas, Riverbed, Samsung, Seagate, STEC, Symantec, and VMware) for their interest, insights, feedback, and support. Keywords: Distributed File System, RAID, Cloud Computing, DISC Abstract The first generation of Data-Intensive Scalable Computing file systems such as Google File System and Hadoop Distributed File System employed n (n ≥ 3) replications for high data reliability, therefore delivering users only about 1=n of the total storage capacity of the raw disks. -
Middleware-Based Database Replication: the Gaps Between Theory and Practice
Appears in Proceedings of the ACM SIGMOD Conference, Vancouver, Canada (June 2008) Middleware-based Database Replication: The Gaps Between Theory and Practice Emmanuel Cecchet George Candea Anastasia Ailamaki EPFL EPFL & Aster Data Systems EPFL & Carnegie Mellon University Lausanne, Switzerland Lausanne, Switzerland Lausanne, Switzerland [email protected] [email protected] [email protected] ABSTRACT There exist replication “solutions” for every major DBMS, from Oracle RAC™, Streams™ and DataGuard™ to Slony-I for The need for high availability and performance in data Postgres, MySQL replication and cluster, and everything in- management systems has been fueling a long running interest in between. The naïve observer may conclude that such variety of database replication from both academia and industry. However, replication systems indicates a solved problem; the reality, academic groups often attack replication problems in isolation, however, is the exact opposite. Replication still falls short of overlooking the need for completeness in their solutions, while customer expectations, which explains the continued interest in developing new approaches, resulting in a dazzling variety of commercial teams take a holistic approach that often misses offerings. opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. Even the “simple” cases are challenging at large scale. We deployed a replication system for a large travel ticket brokering This paper aims to characterize the gap along three axes: system at a Fortune-500 company faced with a workload where performance, availability, and administration. We build on our 95% of transactions were read-only. Still, the 5% write workload own experience developing and deploying replication systems in resulted in thousands of update requests per second, which commercial and academic settings, as well as on a large body of implied that a system using 2-phase-commit, or any other form of prior related work. -
Manufacturing Equipment Technologies for Hard Disk's
Manufacturing Equipment Technologies for Hard Disk’s Challenge of Physical Limits 222 Manufacturing Equipment Technologies for Hard Disk’s Challenge of Physical Limits Kyoichi Mori OVERVIEW: To meet the world’s growing demands for volume information, Brian Rattray not just with computers but digitalization and video etc. the HDD must Yuichi Matsui, Dr. Eng. continually increase its capacity and meet expectations for reduced power consumption and green IT. Up until now the HDD has undergone many innovative technological developments to achieve higher recording densities. To continue this increase, innovative new technology is again required and is currently being developed at a brisk pace. The key components for areal density improvements, the disk and head, require high levels of performance and reliability from production and inspection equipment for efficient manufacturing and stable quality assurance. To meet this demand, high frequency electronics, servo positioning and optical inspection technology is being developed and equipment provided. Hitachi High-Technologies Corporation is doing its part to meet market needs for increased production and the adoption of next-generation technology by developing the technology and providing disk and head manufacturing/inspection equipment (see Fig. 1). INTRODUCTION higher efficiency storage, namely higher density HDDS (hard disk drives) have long relied on the HDDs will play a major role. computer market for growth but in recent years To increase density, the performance and quality of there has been a shift towards cloud computing and the HDD’s key components, disks (media) and heads consumer electronics etc. along with a rapid expansion have most effect. Therefore further technological of data storage applications (see Fig. -
Best Practices for Openzfs L2ARC in the Era of Nvme
Best Practices for OpenZFS L2ARC in the Era of NVMe Ryan McKenzie iXsystems 2019 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. 1 Agenda ❏ Brief Overview of OpenZFS ARC / L2ARC ❏ Key Performance Factors ❏ Existing “Best Practices” for L2ARC ❏ Rules of Thumb, Tribal Knowledge, etc. ❏ NVMe as L2ARC ❏ Testing and Results ❏ Revised “Best Practices” 2019 Storage Developer Conference. © iXsystems. All Rights Reserved. 2 ARC Overview ● Adaptive Replacement Cache ● Resides in system memory NIC/HBA ● Shared by all pools ● Used to store/cache: ARC OpenZFS ○ All incoming data ○ “Hottest” data and Metadata (a tunable ratio) SLOG vdevs ● Balances between ○ Most Frequently Used (MFU) Data vdevs L2ARC vdevs ○ Most Recently zpool Used (MRU) FreeBSD + OpenZFS Server 2019 Storage Developer Conference. © iXsystems. All Rights Reserved. 3 L2ARC Overview ● Level 2 Adaptive Replacement Cache ● Resides on one or more storage devices NIC/HBA ○ Usually Flash ● Device(s) added to pool ARC OpenZFS ○ Only services data held by that pool ● Used to store/cache: ○ “Warm” data and SLOG vdevs metadata ( about to be evicted from ARC) Data vdevs L2ARC vdevs ○ Indexes to L2ARC zpool blocks stored in ARC headers FreeBSD + OpenZFS Server 2019 Storage Developer Conference. © iXsystems. All Rights Reserved. 4 ZFS Writes ● All writes go through ARC, written blocks are “dirty” until on stable storage ACK NIC/HBA ○ async write ACKs immediately ARC OpenZFS ○ sync write copied to ZIL/SLOG then ACKs ○ copied to data SLOG vdevs vdev in TXG ● When no longer dirty, written blocks stay in Data vdevs L2ARC vdevs ARC and move through zpool MRU/MFU lists normally FreeBSD + OpenZFS Server 2019 Storage Developer Conference. -
A Crash Course in Wide Area Data Replication
A Crash Course In Wide Area Data Replication Jacob Farmer, CTO, Cambridge Computer SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies and individuals may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced without modification The SNIA must be acknowledged as source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. A Crash Course in Wide Area Data Replication 2 © 2008 Storage Networking Industry Association. All Rights Reserved. Abstract A Crash Course in Wide Area Data Replication Replicating data over a WAN sounds pretty straight-forward, but it turns out that there are literally dozens of different approaches, each with it's own pros and cons. Which approach is the best? Well, that depends on a wide variety of factors! This class is a fast-paced crash course in the various ways in which data can be replicated with some commentary on each major approach. We trace the data path from applications to disk drives and examine all of the points along the way wherein replication logic can be inserted. We look at host based replication (application, database, file system, volume level, and hybrids), SAN replication (disk arrays, virtualization appliances, caching appliances, and storage switches), and backup system replication (block level incremental backup, CDP, and de-duplication). This class is not only the fastest way to understand replication technology it also serves as a foundation for understanding the latest storage virtualization techniques. -
Referência Debian I
Referência Debian i Referência Debian Osamu Aoki Referência Debian ii Copyright © 2013-2021 Osamu Aoki Esta Referência Debian (versão 2.85) (2021-09-17 09:11:56 UTC) pretende fornecer uma visão geral do sistema Debian como um guia do utilizador pós-instalação. Cobre muitos aspetos da administração do sistema através de exemplos shell-command para não programadores. Referência Debian iii COLLABORATORS TITLE : Referência Debian ACTION NAME DATE SIGNATURE WRITTEN BY Osamu Aoki 17 de setembro de 2021 REVISION HISTORY NUMBER DATE DESCRIPTION NAME Referência Debian iv Conteúdo 1 Manuais de GNU/Linux 1 1.1 Básico da consola ................................................... 1 1.1.1 A linha de comandos da shell ........................................ 1 1.1.2 The shell prompt under GUI ......................................... 2 1.1.3 A conta root .................................................. 2 1.1.4 A linha de comandos shell do root ...................................... 3 1.1.5 GUI de ferramentas de administração do sistema .............................. 3 1.1.6 Consolas virtuais ............................................... 3 1.1.7 Como abandonar a linha de comandos .................................... 3 1.1.8 Como desligar o sistema ........................................... 4 1.1.9 Recuperar uma consola sã .......................................... 4 1.1.10 Sugestões de pacotes adicionais para o novato ................................ 4 1.1.11 Uma conta de utilizador extra ........................................ 5 1.1.12 Configuração -
Replication Under Scalable Hashing: a Family of Algorithms for Scalable Decentralized Data Distribution
Replication Under Scalable Hashing: A Family of Algorithms for Scalable Decentralized Data Distribution R. J. Honicky Ethan L. Miller [email protected] [email protected] Storage Systems Research Center Jack Baskin School of Engineering University of California, Santa Cruz Abstract block management is handled by the disk as opposed to a dedicated server. Because block management on individ- Typical algorithms for decentralized data distribution ual disks requires no inter-disk communication, this redis- work best in a system that is fully built before it first used; tribution of work comes at little cost in performance or ef- adding or removing components results in either exten- ficiency, and has a huge benefit in scalability, since block sive reorganization of data or load imbalance in the sys- layout is completely parallelized. tem. We have developed a family of decentralized algo- There are differing opinions about what object-based rithms, RUSH (Replication Under Scalable Hashing), that storage is or should be, and how much intelligence belongs maps replicated objects to a scalable collection of storage in the disk. In order for a device to be an OSD, however, servers or disks. RUSH algorithms distribute objects to each disk or disk subsystem must have its own filesystem; servers according to user-specified server weighting. While an OSD manages its own allocation of blocks and disk lay- all RUSH variants support addition of servers to the sys- out. To store data on an OSD, a client providesthe data and tem, different variants have different characteristics with a key for the data. To retrieve the data, a client provides a respect to lookup time in petabyte-scale systems, perfor- key. -
Openzfs on Linux Hepix 2014 October 16, 2014 Brian Behlendorf [email protected]
OpenZFS on Linux HEPiX 2014 October 16, 2014 Brian Behlendorf [email protected] LLNL-PRES-XXXXXX This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC 1 Lawrence Livermore National Laboratory LLNL-PRES-xxxxxx High Performance Computing Advanced Simulation Massive Scale Data Intensive Top 500 (June 2014) #3 Sequoia 20.1 Peak PFLOP/s 1,572,864 cores 55 PB of storage at 850 GB/s #9 Vulcan 5.0 Peak PFLOPS/s 393,216 cores 6.7 PB of storage at 106 GB/s World class computing resources 2 Lawrence Livermore National Laboratory LLNL-PRES-xxxxxx Linux Clusters 2001 – First Linux cluster Today – ~10 large Linux clusters (100– 3,000 nodes) and 10-20 smaller ones Near-commodity hardware Open source software stack (TOSS) Tri-Labratory Operating System Stack Modified RHEL targeted for HPC RHEL kernel optimized for clusters Moab and SLURM for scheduling Lustre parallel filesystem Additional packages for monitoring, power/console, compilers, etc LLNL Loves Linux 3 Lawrence Livermore National Laboratory LLNL-PRES-xxxxxx Lustre Lustre is our parallel filesystem of choice Livermore Computing Filesystems: Open – 20PB in 5 filesystems Secure – 22PB in 3 filesystems Plus the 55PB Sequoia filesystem 1000+ storage servers Billions of files Users want: Access to their data from every cluster Good performance High availability How do we design a system to handle this? Lustre is used extensively