Scaling Distributed Hierarchical File Systems Using Newsql Databases
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Globalfs: a Strongly Consistent Multi-Site File System
GlobalFS: A Strongly Consistent Multi-Site File System Leandro Pacheco Raluca Halalai Valerio Schiavoni University of Lugano University of Neuchatelˆ University of Neuchatelˆ Fernando Pedone Etienne Riviere` Pascal Felber University of Lugano University of Neuchatelˆ University of Neuchatelˆ Abstract consistency, availability, and tolerance to partitions. Our goal is to ensure strongly consistent file system operations This paper introduces GlobalFS, a POSIX-compliant despite node failures, at the price of possibly reduced geographically distributed file system. GlobalFS builds availability in the event of a network partition. Weak on two fundamental building blocks, an atomic multicast consistency is suitable for domain-specific applications group communication abstraction and multiple instances of where programmers can anticipate and provide resolution a single-site data store. We define four execution modes and methods for conflicts, or work with last-writer-wins show how all file system operations can be implemented resolution methods. Our rationale is that for general-purpose with these modes while ensuring strong consistency and services such as a file system, strong consistency is more tolerating failures. We describe the GlobalFS prototype in appropriate as it is both more intuitive for the users and detail and report on an extensive performance assessment. does not require human intervention in case of conflicts. We have deployed GlobalFS across all EC2 regions and Strong consistency requires ordering commands across show that the system scales geographically, providing replicas, which needs coordination among nodes at performance comparable to other state-of-the-art distributed geographically distributed sites (i.e., regions). Designing file systems for local commands and allowing for strongly strongly consistent distributed systems that provide good consistent operations over the whole system. -
11.7 the Windows 2000 File System
830 CASE STUDY 2: WINDOWS 2000 CHAP. 11 11.7 THE WINDOWS 2000 FILE SYSTEM Windows 2000 supports several file systems, the most important of which are FAT-16, FAT-32, and NTFS (NT File System). FAT-16 is the old MS-DOS file system. It uses 16-bit disk addresses, which limits it to disk partitions no larger than 2 GB. FAT-32 uses 32-bit disk addresses and supports disk partitions up to 2 TB. NTFS is a new file system developed specifically for Windows NT and car- ried over to Windows 2000. It uses 64-bit disk addresses and can (theoretically) support disk partitions up to 264 bytes, although other considerations limit it to smaller sizes. Windows 2000 also supports read-only file systems for CD-ROMs and DVDs. It is possible (even common) to have the same running system have access to multiple file system types available at the same time. In this chapter we will treat the NTFS file system because it is a modern file system unencumbered by the need to be fully compatible with the MS-DOS file system, which was based on the CP/M file system designed for 8-inch floppy disks more than 20 years ago. Times have changed and 8-inch floppy disks are not quite state of the art any more. Neither are their file systems. Also, NTFS differs both in user interface and implementation in a number of ways from the UNIX file system, which makes it a good second example to study. NTFS is a large and complex system and space limitations prevent us from covering all of its features, but the material presented below should give a reasonable impression of it. -
Big Data Storage Workload Characterization, Modeling and Synthetic Generation
BIG DATA STORAGE WORKLOAD CHARACTERIZATION, MODELING AND SYNTHETIC GENERATION BY CRISTINA LUCIA ABAD DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2014 Urbana, Illinois Doctoral Committee: Professor Roy H. Campbell, Chair Professor Klara Nahrstedt Associate Professor Indranil Gupta Assistant Professor Yi Lu Dr. Ludmila Cherkasova, HP Labs Abstract A huge increase in data storage and processing requirements has lead to Big Data, for which next generation storage systems are being designed and implemented. As Big Data stresses the storage layer in new ways, a better understanding of these workloads and the availability of flexible workload generators are increas- ingly important to facilitate the proper design and performance tuning of storage subsystems like data replication, metadata management, and caching. Our hypothesis is that the autonomic modeling of Big Data storage system workloads through a combination of measurement, and statistical and machine learning techniques is feasible, novel, and useful. We consider the case of one common type of Big Data storage cluster: A cluster dedicated to supporting a mix of MapReduce jobs. We analyze 6-month traces from two large clusters at Yahoo and identify interesting properties of the workloads. We present a novel model for capturing popularity and short-term temporal correlations in object re- quest streams, and show how unsupervised statistical clustering can be used to enable autonomic type-aware workload generation that is suitable for emerging workloads. We extend this model to include other relevant properties of stor- age systems (file creation and deletion, pre-existing namespaces and hierarchical namespaces) and use the extended model to implement MimesisBench, a realistic namespace metadata benchmark for next-generation storage systems. -
A Decentralized Cloud Storage Network Framework
Storj: A Decentralized Cloud Storage Network Framework Storj Labs, Inc. October 30, 2018 v3.0 https://github.com/storj/whitepaper 2 Copyright © 2018 Storj Labs, Inc. and Subsidiaries This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 license (CC BY-SA 3.0). All product names, logos, and brands used or cited in this document are property of their respective own- ers. All company, product, and service names used herein are for identification purposes only. Use of these names, logos, and brands does not imply endorsement. Contents 0.1 Abstract 6 0.2 Contributors 6 1 Introduction ...................................................7 2 Storj design constraints .......................................9 2.1 Security and privacy 9 2.2 Decentralization 9 2.3 Marketplace and economics 10 2.4 Amazon S3 compatibility 12 2.5 Durability, device failure, and churn 12 2.6 Latency 13 2.7 Bandwidth 14 2.8 Object size 15 2.9 Byzantine fault tolerance 15 2.10 Coordination avoidance 16 3 Framework ................................................... 18 3.1 Framework overview 18 3.2 Storage nodes 19 3.3 Peer-to-peer communication and discovery 19 3.4 Redundancy 19 3.5 Metadata 23 3.6 Encryption 24 3.7 Audits and reputation 25 3.8 Data repair 25 3.9 Payments 26 4 4 Concrete implementation .................................... 27 4.1 Definitions 27 4.2 Peer classes 30 4.3 Storage node 31 4.4 Node identity 32 4.5 Peer-to-peer communication 33 4.6 Node discovery 33 4.7 Redundancy 35 4.8 Structured file storage 36 4.9 Metadata 39 4.10 Satellite 41 4.11 Encryption 42 4.12 Authorization 43 4.13 Audits 44 4.14 Data repair 45 4.15 Storage node reputation 47 4.16 Payments 49 4.17 Bandwidth allocation 50 4.18 Satellite reputation 53 4.19 Garbage collection 53 4.20 Uplink 54 4.21 Quality control and branding 55 5 Walkthroughs ............................................... -
Filesystems HOWTO Filesystems HOWTO Table of Contents Filesystems HOWTO
Filesystems HOWTO Filesystems HOWTO Table of Contents Filesystems HOWTO..........................................................................................................................................1 Martin Hinner < [email protected]>, http://martin.hinner.info............................................................1 1. Introduction..........................................................................................................................................1 2. Volumes...............................................................................................................................................1 3. DOS FAT 12/16/32, VFAT.................................................................................................................2 4. High Performance FileSystem (HPFS)................................................................................................2 5. New Technology FileSystem (NTFS).................................................................................................2 6. Extended filesystems (Ext, Ext2, Ext3)...............................................................................................2 7. Macintosh Hierarchical Filesystem − HFS..........................................................................................3 8. ISO 9660 − CD−ROM filesystem.......................................................................................................3 9. Other filesystems.................................................................................................................................3 -
File Systems
“runall” 2002/9/24 page 305 CHAPTER 10 File Systems 10.1 BASIC FUNCTIONS OF FILE MANAGEMENT 10.2 HIERARCHICAL MODEL OF A FILE SYSTEM 10.3 THE USER’S VIEW OF FILES 10.4 FILE DIRECTORIES 10.5 BASIC FILE SYSTEM 10.6 DEVICE ORGANIZATION METHODS 10.7 PRINCIPLES OF DISTRIBUTED FILE SYSTEMS 10.8 IMPLEMENTING DISTRIBUTED FILE SYSTEM Given that main memory is volatile, i.e., does not retain information when power is turned off, and is also limited in size, any computer system must be equipped with secondary memory on which the user and the system may keep information for indefinite periods of time. By far the most popular secondary memory devices are disks for random access purposes and magnetic tapes for sequential, archival storage. Since these devices are very complex to interact with, and, in multiuser systems are shared among different users, operating systems (OS) provide extensive services for managing data on secondary memory. These data are organized into files, which are collections of data elements grouped together for the purposes of access control, retrieval, and modification. A file system is the part of the operating system that is responsible for managing files and the resources on which these reside. Without a file system, efficient computing would essentially be impossible. This chapter discusses the organization of file systems and the tasks performed by the different components. The first part is concerned with general user and implementation aspects of file management emphasizing centralized systems; the last sections consider extensions and methods for distributed systems. 10.1 BASIC FUNCTIONS OF FILE MANAGEMENT The file system, in collaboration with the I/O system, has the following three basic functions: 1. -
Using Hierarchical Folders and Tags for File Management
Using Hierarchical Folders and Tags for File Management A Thesis Submitted to the Faculty of Drexel University by Shanshan Ma in partial fulfillment of the requirement for the degree of Doctor of Philosophy March 2010 © Copyright 2010 Shanshan Ma. All Rights Reserved. ii Dedications This dissertation is dedicated to my mother. iii Acknowledgments I would like to express my sincerest gratitude to my advisor Dr. Susan Wiedenbeck. She encouraged me when I had struggles. She inspired me when I had doubts. The dissertation is nowhere to be found if it had not been for our weekly meetings and numerous discussions. I’m in great debts to all the time and effort that she spent with me in this journey. Thank you to my dissertation committee members, Dr. Michael Atwood, Dr. Xia Lin, Dr. Denise Agosto, and Dr. Deborah Barreau, who have guided me and supported me in the research. The insights and critiques from the committee are invaluable in the writing of this dissertation. I am grateful to my family who love me unconditionally. Thank you my mother for teaching me to be a strong person. Thank you my father and my brother for always being there for me. I would like to thank the iSchool at Drexel University for your generosity in supporting my study and research, for your faculty and staff members who I always had fun to work with, and for the alumni garden that is beautiful all year round. Thank you my friends in Philadelphia and my peer Ph.D. students in the iSchool at Drexel University. -
Introduction to ISO 9660
Disc Manufacturing, Inc. A QUIXOTE COMPANY Introduction to ISO 9660, what it is, how it is implemented, and how it has been extended. Clayton Summers Copyright © 1993 by Disc Manufacturing, Inc. All rights reserved. WHO IS DMI? Disc Manufacturing, Inc. (DMI) manufactures all compact disc formats (i.e., CD-Audio, CD-ROM, CD-ROM XA, CDI, PHOTO CD, 3DO, KARAOKE, etc.) at two plant sites in the U.S.; Huntsville, AL, and Anaheim, CA. To help you, DMI has one of the largest Product Engineering/Technical Support staff and sales force dedicated solely to CD-ROM in the industry. The company has had a long term commitment to optical disc technology and has performed developmental work and manufactured (laser) optical discs of various types since 1981. In 1983, DMI manufactured the first compact disc in the United States. DMI has developed extensive mastering expertise during this time and is frequently called upon by other companies to provide special mastering services for products in development. In August 1991, DMI purchased the U.S. CD-ROM business from the Philips and Du Pont Optical Company (PDO). PDO employees in sales, marketing and technical services were retained. DMI is a wholly-owned subsidiary of Quixote Corporation, a publicly owned corporation whose stock is traded on the NASDAQ exchange as QUIX. Quixote is a diversified technology company composed of Energy Absorption Systems, Inc. (manufactures highway crash cushions), Stenograph Corporation (manufactures shorthand machines and computer systems for court reporting) and Disc Manufacturing, Inc. We would be pleased to help you with your CD project or answer any questions you may have. -
430 File Systems Chap
430 FILE SYSTEMS CHAP. 6 6.4 EXAMPLE FILE SYSTEMS In the following sections we will discuss several example file systems, rang- ing from quite simple to highly sophisticated. Since modern UNIX file systems and Windows 2000’s native file system are covered in the chapter on UNIX (Chap. 10) and the chapter on Windows 2000 (Chap. 11) we will not cover those systems here. We will, however, examine their predecessors below. 6.4.1 CD-ROM File Systems As our first example of a file system, let us consider the file systems used on CD-ROMs. These systems are particularly simple because they were designed for write-once media. Among other things, for example, they have no provision for keeping track of free blocks because on a CD-ROM files cannot be freed or added after the disk has been manufactured. Below we will take a look at the main CD- ROM file system type and two extensions to it. The ISO 9660 File System The most common standard for CD-ROM file systems was adopted as an International Standard in 1988 under the name ISO 9660. Virtually every CD- ROM currently on the market is compatible with this standard, sometimes with the extensions to be discussed below. One of the goals of this standard was to make every CD-ROM readable on every computer, independent of the byte order- ing used and independent of the operating system used. As a consequence, some limitations were placed on the file system to make it possible for the weakest operating systems then in use (such as MS-DOS) to read it. -
File Systems Performance Analysis
File Systems Performance Analysis Benchmarking project for the lecture Computer Performance Analysing And Benchmarking lectured by Prof. Thomas M. Stricker at Swiss Federal Institute of Technology Written by Stefan Rondinelli File Systems Performance Analysis Project CONTENTS 0 Introduction 2 1 The Environment 2 1.1 The Hardware 2 1.2 The Software 3 2 Performing the Benchmark 4 2.1 The Proceeding 4 2.2 The Output 5 3 Statistical Analysis of the Data 6 3.1 Computation of Effects 6 3.2 Confidence Intervals 7 3.3 Are the File Systems Significantly Different? 8 4 References 10 - 1 - File Systems Performance Analysis Project 0 Introduction Every operating system has its own file systems. For example Windows uses FAT16(File Allocation Table 16(bits)), FAT32(File Allocation Table 32) and NTFS(Windows NT File System), Linux uses Minix fs, Extended fs and ext2 and a Mac has its (discarded) MFS(Macintosh file system) and HFS(Hierarchical File System) file systems. Sometimes file systems of other operating systems are supported what for example is desired in a dual boot system (e.g. Linux and Windows). When using such a dual boot machine with Linux and Windows, the performance of a file system could be one of the considerations when choosing what file systems to use for the different partitions. In this project I am going to benchmark some file systems and finally to analyze the gathered data statistically as learned in the lecture. 1 The Environment 1.1 The Hardware I used the following hardware for the performance analysis: - CPU: AMD K6II 450Mhz (has a 64kB cache) - RAM: 64MB SDRAM - Hard disk 1: Western Digital Caviar 36400 (6 GB) - Hard disk 2: Western Digital Caviar 33200 (3 GB) - Disk controller: IDE for both hard disks To have the same conditions these components affecting the I/O speed must be the same ones for all the measurements of the performance of the different file systems otherwise the differences in the measured data would rather be due to unequal hardware then to the different implementation of a file system. -
A File Management System Based on Overlapping Sets of Tags
VennTags: A File Management System based on Overlapping Sets of Tags N. Albadri1, Stijn Dekeyser1, Richard Watson1 1University of Southern Queensland, Australia Abstract File systems (FS) are an essential part of operating systems in that they are responsible for storing and organising files and then retrieving those when needed. Because of the high capacity of modern storage devices and the growing number of files stored, the traditional FS model is no longer able to meet modern users’ needs in terms of storing and retrieving files. So using metadata emerges as an efficacy solution for the limitations of file systems. In this paper we propose a new model dubbed VennTags to solve the FS problems. We do this by utilising the idea of overlapping the sets as in Venn diagram, and adopting DAG structure (instead of tree) to achieve that we have used tagging capability and exposed a query language at the level of the API. We evaluate the expressive power of VennTags model that shows its ability to resolve the FS limitations compared to other solutions. Citation: Albadri, N. Dekeyser, S., & Watson, R. (2017). VennTags: A File Management System based on Overlapping Sets of Tags. In iConference 2017 Proceedings, Vol. 2 (pp. 1-14). https://doi.org/10.9776/17002 Keywords: Operating Systems, File systems, metadata, tagging, Directed Acyclic Graph Contact: [email protected], [email protected], [email protected] 1 Introduction Personal computer systems, mobile and cloud-based as well as desktop oriented, are permanent companions in users daily lives, for both private and professional activities. -
The Quantcast File System
The Quantcast File System Michael Ovsiannikov Silvius Rus Damian Reeves Quantcast Quantcast Google movsiannikov@quantcast [email protected] [email protected] Paul Sutter Sriram Rao Jim Kelly Quantcast Microsoft Quantcast [email protected] [email protected] [email protected] ABSTRACT chine writing it, another on the same rack, and a third on a The Quantcast File System (QFS) is an efficient alterna- distant rack. Thus HDFS is network efficient but not par- tive to the Hadoop Distributed File System (HDFS). QFS is ticularly storage efficient, since to store a petabyte of data, written in C++, is plugin compatible with Hadoop MapRe- it uses three petabytes of raw storage. At today’s cost of duce, and offers several efficiency improvements relative to $40,000 per PB that means $120,000 in disk alone, plus ad- HDFS: 50% disk space savings through erasure coding in- ditional costs for servers, racks, switches, power, cooling, stead of replication, a resulting doubling of write through- and so on. Over three years, operational costs bring the put, a faster name node, support for faster sorting and log- cost close to $1 million. For reference, Amazon currently ging through a concurrent append feature, a native com- charges $2.3 million to store 1 PB for three years. mand line client much faster than hadoop fs, and global Hardware evolution since then has opened new optimiza- feedback-directed I/O device management. As QFS works tion possibilities. 10 Gbps networks are now commonplace, out of the box with Hadoop, migrating data from HDFS and cluster racks can be much chattier.