White Paper for Data Protection with Synology Snapshot Technology
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Achieving Superior Manageability, Efficiency, and Data Protection With
An Oracle White Paper December 2010 Achieving Superior Manageability, Efficiency, and Data Protection with Oracle’s Sun ZFS Storage Software Achieving Superior Manageability, Efficiency, and Data Protection with Oracle’s Sun ZFS Storage Software Introduction ......................................................................................... 2 Oracle’s Sun ZFS Storage Software ................................................... 3 Simplifying Storage Deployment and Management ............................ 3 Browser User Interface (BUI) ......................................................... 3 Built-in Networking and Security ..................................................... 4 Transparent Optimization with Hybrid Storage Pools ...................... 4 Shadow Data Migration ................................................................... 5 Third-party Confirmation of Management Efficiency ....................... 6 Improving Performance with Real-time Storage Profiling .................... 7 Increasing Storage Efficiency .............................................................. 8 Data Compression .......................................................................... 8 Data Deduplication .......................................................................... 9 Thin Provisioning ............................................................................ 9 Space-efficient Snapshots and Clones ......................................... 10 Reducing Risk with Industry-leading Data Protection ........................ 10 Self-Healing -
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]. -
Netbackup ™ Enterprise Server and Server 8.0 - 8.X.X OS Software Compatibility List Created on September 08, 2021
Veritas NetBackup ™ Enterprise Server and Server 8.0 - 8.x.x OS Software Compatibility List Created on September 08, 2021 Click here for the HTML version of this document. <https://download.veritas.com/resources/content/live/OSVC/100046000/100046611/en_US/nbu_80_scl.html> Copyright © 2021 Veritas Technologies LLC. All rights reserved. Veritas, the Veritas Logo, and NetBackup are trademarks or registered trademarks of Veritas Technologies LLC in the U.S. and other countries. Other names may be trademarks of their respective owners. Veritas NetBackup ™ Enterprise Server and Server 8.0 - 8.x.x OS Software Compatibility List 2021-09-08 Introduction This Software Compatibility List (SCL) document contains information for Veritas NetBackup 8.0 through 8.x.x. It covers NetBackup Server (which includes Enterprise Server and Server), Client, Bare Metal Restore (BMR), Clustered Master Server Compatibility and Storage Stacks, Deduplication, File System Compatibility, NetBackup OpsCenter, NetBackup Access Control (NBAC), SAN Media Server/SAN Client/FT Media Server, Virtual System Compatibility and NetBackup Self Service Support. It is divided into bookmarks on the left that can be expanded. IPV6 and Dual Stack environments are supported from NetBackup 8.1.1 onwards with few limitations, refer technote for additional information <http://www.veritas.com/docs/100041420> For information about certain NetBackup features, functionality, 3rd-party product integration, Veritas product integration, applications, databases, and OS platforms that Veritas intends to replace with newer and improved functionality, or in some cases, discontinue without replacement, please see the widget titled "NetBackup Future Platform and Feature Plans" at <https://sort.veritas.com/netbackup> Reference Article <https://www.veritas.com/docs/100040093> for links to all other NetBackup compatibility lists. -
Latency-Aware, Inline Data Deduplication for Primary Storage
iDedup: Latency-aware, inline data deduplication for primary storage Kiran Srinivasan, Tim Bisson, Garth Goodson, Kaladhar Voruganti NetApp, Inc. fskiran, tbisson, goodson, [email protected] Abstract systems exist that deduplicate inline with client requests for latency sensitive primary workloads. All prior dedu- Deduplication technologies are increasingly being de- plication work focuses on either: i) throughput sensitive ployed to reduce cost and increase space-efficiency in archival and backup systems [8, 9, 15, 21, 26, 39, 41]; corporate data centers. However, prior research has not or ii) latency sensitive primary systems that deduplicate applied deduplication techniques inline to the request data offline during idle time [1, 11, 16]; or iii) file sys- path for latency sensitive, primary workloads. This is tems with inline deduplication, but agnostic to perfor- primarily due to the extra latency these techniques intro- mance [3, 36]. This paper introduces two novel insights duce. Inherently, deduplicating data on disk causes frag- that enable latency-aware, inline, primary deduplication. mentation that increases seeks for subsequent sequential Many primary storage workloads (e.g., email, user di- reads of the same data, thus, increasing latency. In addi- rectories, databases) are currently unable to leverage the tion, deduplicating data requires extra disk IOs to access benefits of deduplication, due to the associated latency on-disk deduplication metadata. In this paper, we pro- costs. Since offline deduplication systems impact la- -
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. -
Redefine Blockchain Storage
Redefine Blockchain Storage Redefine Blockchain Storage YottaChain Foundation October 2018 V0.95 www.yottachain.io1/109 Redefine Blockchain Storage CONTENTS ABSTRACT........................................................................................... 7 1. BACKGROUND..............................................................................10 1.1. Storage is the best application scenario for blockchain....... 10 1.1.1 What is blockchain storage?....................................... 10 1.1.2. Storage itself has decentralized requirements........... 10 1.1.3 Amplification effect of data deduplication.................11 1.1.4 Storage can be directly TOKENIZE on the chain......11 1.1.5 chemical reactions of blockchain + storage................12 1.1.6 User value of blockchain storage................................12 1.2 IPFS........................................................................................14 1.2.1 What IPFS Resolved...................................................14 1.2.2 Deficiency of IPFS......................................................15 1.3Data Encryption and Data De-duplication..............................18 1.3.1Data Encryption........................................................... 18 1.3.2Data Deduplication...................................................... 19 1.3.3 Data Encryption OR Data Deduplication, which one to sacrifice?.......................................................................... 20 www.yottachain.io2/109 Redefine Blockchain Storage 2. INTRODUCTION TO YOTTACHAIN..........................................22 -
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. -
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. -
Primary Data Deduplication – Large Scale Study and System Design
Primary Data Deduplication – Large Scale Study and System Design Ahmed El-Shimi Ran Kalach Ankit Kumar Adi Oltean Jin Li Sudipta Sengupta Microsoft Corporation, Redmond, WA, USA Abstract platform where a variety of workloads and services compete for system resources and no assumptions of We present a large scale study of primary data dedupli- dedicated hardware can be made. cation and use the findings to drive the design of a new primary data deduplication system implemented in the Our Contributions. We present a large and diverse Windows Server 2012 operating system. File data was study of primary data duplication in file-based server analyzed from 15 globally distributed file servers hosting data. Our findings are as follows: (i) Sub-file deduplica- data for over 2000 users in a large multinational corpora- tion is significantly more effective than whole-file dedu- tion. plication, (ii) High deduplication savings previously ob- The findings are used to arrive at a chunking and com- tained using small ∼4KB variable length chunking are pression approach which maximizes deduplication sav- achievable with 16-20x larger chunks, after chunk com- ings while minimizing the generated metadata and pro- pression is included, (iii) Chunk compressibility distri- ducing a uniform chunk size distribution. Scaling of bution is skewed with the majority of the benefit of com- deduplication processing with data size is achieved us- pression coming from a minority of the data chunks, and ing a RAM frugal chunk hash index and data partitioning (iv) Primary datasets are amenable to partitioned dedu- – so that memory, CPU, and disk seek resources remain plication with comparable space savings and the par- available to fulfill the primary workload of serving IO.