Coda: a Highly Available File System for a Distributed Workstation Environment
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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. -
Using the Andrew File System on *BSD [email protected], Bsdcan, 2006 Why Another Network Filesystem
Using the Andrew File System on *BSD [email protected], BSDCan, 2006 why another network filesystem 1-slide history of Andrew File System user view admin view OpenAFS Arla AFS on OpenBSD, FreeBSD and NetBSD Filesharing on the Internet use FTP or link to HTTP file interface through WebDAV use insecure protocol over vpn History of AFS 1984: developed at Carnegie Mellon 1989: TransArc Corperation 1994: over to IBM 1997: Arla, aimed at Linux and BSD 2000: IBM releases source 2000: foundation of OpenAFS User view <1> global filesystem rooted at /afs /afs/cern.ch/... /afs/cmu.edu/... /afs/gorlaeus.net/users/h/hugo/... User view <2> authentication through Kerberos #>kinit <username> obtain krbtgt/<realm>@<realm> #>afslog obtain afs@<realm> #>cd /afs/<cell>/users/<username> User view <3> ACL (dir based) & Quota usage runs on Windows, OS X, Linux, Solaris ... and *BSD Admin view <1> <cell> <partition> <server> <volume> <volume> <server> <partition> Admin view <2> /afs/gorlaeus.net/users/h/hugo/presos/afs_slides.graffle gorlaeus.net /vicepa fwncafs1 users hugo h bram <server> /vicepb Admin view <2a> /afs/gorlaeus.net/users/h/hugo/presos/afs_slides.graffle gorlaeus.net /vicepa fwncafs1 users hugo /vicepa fwncafs2 h bram Admin view <3> servers require KeyFile ~= keytab procedure differs for Heimdal: ktutil copy MIT: asetkey add Admin view <4> entry in CellServDB >gorlaeus.net #my cell name 10.0.0.1 <dbserver host name> required on servers required on clients without DynRoot Admin view <5> File locking no databases on AFS (requires byte range locking) -
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 -
A Persistent Storage Model for Extreme Computing Shuangyang Yang Louisiana State University and Agricultural and Mechanical College
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2014 A Persistent Storage Model for Extreme Computing Shuangyang Yang Louisiana State University and Agricultural and Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Computer Sciences Commons Recommended Citation Yang, Shuangyang, "A Persistent Storage Model for Extreme Computing" (2014). LSU Doctoral Dissertations. 2910. https://digitalcommons.lsu.edu/gradschool_dissertations/2910 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. A PERSISTENT STORAGE MODEL FOR EXTREME COMPUTING A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Computer Science by Shuangyang Yang B.S., Zhejiang University, 2006 M.S., University of Dayton, 2008 December 2014 Copyright © 2014 Shuangyang Yang All rights reserved ii Dedicated to my wife Miao Yu and our daughter Emily. iii Acknowledgments This dissertation would not be possible without several contributions. It is a great pleasure to thank Dr. Hartmut Kaiser @ Louisiana State University, Dr. Walter B. Ligon III @ Clemson University and Dr. Maciej Brodowicz @ Indiana University for their ongoing help and support. It is a pleasure also to thank Dr. Bijaya B. Karki, Dr. Konstantin Busch, Dr. Supratik Mukhopadhyay at Louisiana State University for being my committee members and Dr. -
Comparative Analysis of Distributed and Parallel File Systems' Internal Techniques
Comparative Analysis of Distributed and Parallel File Systems’ Internal Techniques Viacheslav Dubeyko Content 1 TERMINOLOGY AND ABBREVIATIONS ................................................................................ 4 2 INTRODUCTION......................................................................................................................... 5 3 COMPARATIVE ANALYSIS METHODOLOGY ....................................................................... 5 4 FILE SYSTEM FEATURES CLASSIFICATION ........................................................................ 5 4.1 Distributed File Systems ............................................................................................................................ 6 4.1.1 HDFS ..................................................................................................................................................... 6 4.1.2 GFS (Google File System) ....................................................................................................................... 7 4.1.3 InterMezzo ............................................................................................................................................ 9 4.1.4 CodA .................................................................................................................................................... 10 4.1.5 Ceph.................................................................................................................................................... 12 4.1.6 DDFS .................................................................................................................................................. -
Using the Andrew File System with BSD
Using the Andrew File System with BSD H. Meiland May 4, 2006 Abstract Since the beginning of networks, one of the basic idea’s has been sharing of files; even though with the Internet as advanced as today, simple platform independent file sharing is not common. Why is the closest thing we use WebDAV, a ’neat trick over http’, instead of a real protocol? In this paper the Andrew File System will be described which has been (and is) the file sharing core of many universities and companies world- wide. Also the reason for it’s relative unawareness in the community will be answered, and it’s actual features and performance in comparison with alternative network filesystems. Finally some information will be given on how to use it with our favorite OS: BSD. 1 History • 1984 Carnegie Mellon University, first release • 1989 TransArc Corporation formed by part of original team members • 1994 TransArc purchased by IBM • 1997 Start of Arla development at stacken.kth.se • 2000 IBM releases AFS in opensource (IBM License) • 2000 http://www.OpenAFS.org • 2006 good support for lot’s of platforms, many new features etc. 1 2 Overview 2.1 User point of view 2.1.1 Global namespace While discussing global filesystem, it is easy to dive into a organization, and explain wonderfull features like having replicas of often accessed data in branch-offices, and moving home-directories to local fileservers when mov- ing employees between departments. An essential feature of AFS is often overlooked: a common root as accesspoint of all AFS stored data. -
Zfs-Ascalabledistributedfilesystemusingobjectdisks
zFS-AScalableDistributedFileSystemUsingObjectDisks Ohad Rodeh Avi Teperman [email protected] [email protected] IBM Labs, Haifa University, Mount Carmel, Haifa 31905, Israel. Abstract retrieves the data block from the remote machine. zFS also uses distributed transactions and leases, instead of group- zFS is a research project aimed at building a decentral- communication and clustering software. We intend to test ized file system that distributes all aspects of file and stor- and show the effectiveness of these two features in our pro- age management over a set of cooperating machines inter- totype. connected by a high-speed network. zFS is designed to be zFS has six components: a Front End (FE), a Cooper- a file system that scales from a few networked computers to ative Cache (Cache), a File Manager (FMGR), a Lease several thousand machines and to be built from commodity Manager (LMGR), a Transaction Server (TSVR), and an off-the-shelf components. Object Store (OSD). These components work together to The two most prominent features of zFS are its coop- provide applications/users with a distributed file system. erative cache and distributed transactions. zFS integrates The design of zFS addresses, and is influenced by, issues the memory of all participating machines into one coher- of fault tolerance, security and backup/mirroring. How- ent cache. Thus, instead of going to the disk for a block ever, in this article, we focus on the zFS high-level archi- of data already in one of the machine memories, zFS re- tecture and briefly describe zFS’s fault tolerance character- trieves the data block from the remote machine. -
IC2E/Iotdi 2018 Program
IC2E/IoTDI 2018 Program Room Plaza Int’l Date Time Columbia Columbia Columbia Challenger 40-41 Challenger 38-39 Ballroom G 37 36 34 Level 3 Level 3 Convention Level 3 Level 3 Level 3 Level IC2E Workshop 1 IC2E Workshop 2 IoTDI Workshop 1 IoTDI Workshop 2 (Globe-IoT) (Container) (SocialSens) (IoTSec) Opening remarks Opening remarks 8:30 AM - 8:45 AM 8:30 AM – 8:45 AM Opening: Timothy P Hanratty, Welcome Remarks Keynote 1: “High-Level Keynote: “Container Security” ARL Interoperability in IoT” Salman Baset 8:45 AM – 9:45 AM 8:30 – Damla Turgut, University of IC2E Tutorial 1 8:45 AM - 10:00 AM Keynote: “The Role of 10:00pm Central Florida (Blockchain) Keynote Talk: “Understanding Modeling and Simulation in and Engineering Social IoT Security Research” Signals: A Network- and Data- Prof. David M. Nicol, Driven Perspective” Univeristy of Illinois at Prof. Radu Marculescu, Urbana-Champaign Carnegie Mellon University 9:45 AM – 10:00 AM Q & A Coffee Break IC2E Workshop 1 IC2E Workshop 2 IoTDI Workshop 1 IoTDI Workshop 2 (Globe-IoT: Development (Container) (SocialSens) (IoTSec) Methodologies) Containerizing for Vision Discussions (Session Session I: Attacks and A Metamodel Framework for heterogeneous HPC Chair: Lu Su) Defenses Edge-based Smart applications in the Cloud 10:30 AM - 12 :00 P M 10:30 AM – 10:50 AM Environments Authors: Malik Khan Yuan Gong and Christian Authors: Franco and Anne C. Elster Operating in the New Poellabauer. An Overview of Cicirelli, Giancarlo Fortino, Information Environment: An Vulnerabilities of Voice Antonio Guerrieri, Alessandro Container-based Performance Army Vision of Social Sensing? Controlled Systems Mercuri, Giandomenico Evaluation: A Survey and Authors: Sue Kase and Spezzano and Andrea Vinci Challenges Elizabeth Bowman 10:50 AM – 11:10 AM Authors: Naylor G. -
Mobile Computing: the Next Decade and Beyond Mahadev Satyanarayanan School of Computer Science Carnegie Mellon University
Mobile Computing: the Next Decade and Beyond Mahadev Satyanarayanan School of Computer Science Carnegie Mellon University Abstract “Information at your fingertips anywhere, anytime” has been the driving vision of mobile computing for the past two decades. Through relentless pursuit of this vision, spurring innovations in wireless technology, energy-efficient portable hardware and adaptive software, we have now largely attained this goal. Ubiquitous email and Web access is a reality that is experienced by millions of users worldwide through their BlackBerries, iPhones, Windows Phone, and other portable devices. What will inspire our research in mobile computing over the next decade and beyond? We observe that our future is being shaped by two different tectonic forces, each with the potential to radically change the mobile computing landscape. One force is the emergence of mobile devices as rich sensors, a role that may soon dominate their current function as communication devices and information appliances. We use the term “rich” to connote the depth and complexity about the real world that is recorded, typically through image capture. This is in contrast to simple scalar data that has typically been the focus of the sensor network community in the context of energy-impoverished “smart-dust” sensors. The other force is the convergence of mobile computing and cloud computing. This will enable the emergence of new near-real-time applications that are no longer limited by energy or computational constraints that are inherent to mobility. The intersection of these immense forces in the next decade will lead to many new research challenges and business opportunities. Bio Satya is an experimental computer scientist who has pioneered research in mobile and pervasive computing. -
Ursa Major Starting to Take Shape
THE THE NEWSLETTER ON PDL ACTIVITIES AND EVENTS • FALL 2 0 0 4 http://www.pdl.cmu.edu/ AN INFORMAL PUBLICATION FROM Ursa Major Starting to Take Shape ACADEMIA’S PREMIERE STORAGE Greg Ganger SYSTEMS RESEARCH CENTER Ursa Major will be the first full system constructed on PDL’s journey towards DEVOTED TO ADVANCING THE self-* storage. Ursa Major will be a large-scale object store, in the NASD and STATE OF THE ART IN STORAGE OSD style, based on standard server technologies (e.g., rack-mounted servers and SYSTEMS AND INFORMATION gigabit-per-second Ethernet). This article discusses the long-range project goals, some of the progress that has been made on Ursa Major, and near-term plans. INFRASTRUCTURES. The Self-* Storage Vision Human administration of storage systems is a large and growing issue in modern CONTENTS IT infrastructures. PDL’s Self-* Storage project explores new storage architec- tures that integrate automated management functions and simplify the human Ursa Major ........................................... 1 administrative task. Self-* (pronounced “self-star”—a play on the unix shell wild-card character) storage systems should be self-configuring, self-tuning, Director’s Letter ................................... 2 self-organizing, self-healing, self-managing, etc. Ideally, human administrators Year in Review ..................................... 4 should have to do nothing more than provide muscle for component additions, Recent Publications .............................. 5 guidance on acceptable risk levels (e.g., reliability goals), and current levels of satisfaction. PDL News & Awards ........................... 8 We think in terms of systems composed of networked “intelligent” storage bricks, New PDL Faculty ............................... 11 which are small-scale servers consisting of CPU(s), RAM, and a number of disks. -
Contributors to This Issue
Contributors to This Issue Brian N. Bershad is an Assistant Professor of Computer Science at the University of Washington in Seattle, V/ashington. Dr. Bershad's research interests include archi- tecture, operating system support for parallel and distributed computation, and high performance networking. Prior to his appointment in Seattle, he was on the faculty at Carngie Mellon University in Pittsburgh, PA. He received his M.S. (1989) and Ph.D. (1990) degrees in Computer Science from the University of Washington. He received his Bachelor's Degree (1986) in Electrical Engineering and Computer Sci- ence from the University of California at Berkeley. Dr. Bershad received an NSF PYI award in 1990, an ONR TIA award in 1994, and is a member of IEEE and ACM. Dr. Bershad's cuffent research includes the SPIN project and Memory system design. Fred Douglis is an MTS at AT&T Bell Laboratories in Murray Hill, NJ. Prior to that he was a scientist at the Matsushita Information Technology Laboratory, and a post- doctoral fellow at the Vrije Universiteit in Amsterdam. His interests include the V/orld Wide Web, mobile computing, distributed computing, and file systems. He worked on the Sprite network operating system from its inception in 1984 until the fall of 1990, and built its process migration facility as part of his doctoral research. He received a B.S. in Computer Science from Yale University in 1984, an M.S. in Computer Science from the University of California, Berkeley, in 1987, and a Ph.D. in Computer Science from U.C. Berkeley in 1990. -
Edge Analytics in the Internet of Things
Edge Analytics in the Internet of Things Mahadev Satyanarayanany, Pieter Simoensz, Yu Xiao•, Padmanabhan Pillai?, Zhuo Cheny, Kiryong Hay, Wenlu Huy, Brandon Amosy yCarnegie Mellon University, zGhent University - iMinds, •Aalto University, ?Intel Labs ABSTRACT itory for crowd-sourced video content, with strong enforce- High data rate sensors such as video cameras are becoming ubiq- ment of privacy preferences and access controls, would be uitous in the Internet of Things. This paper describes GigaSight, a valuable global resource. In this paper, we examine the an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls. The technical challenges involved in creating such a repository. GigaSight architecture is a federated system of VM-based cloudlets For reasons that will become clear in Section 2, this will be that perform video analytics at the edge of the Internet, thus re- ducing demand for ingress bandwidth into the cloud. Denaturing, a physically distributed and federated repository rather than which is the owner-specific reduction in fidelity of video content to a monolithic entity. preserve privacy, is one form of analytics on cloudlets. Content-based indexing for search is another form of cloudlet-based analytics. Video is not the only data type that could benefit from such a global repository. Any data type in the IoT that has 1. Ubiquitous Imaging and Sensing high data rate can potentially benefit. For example, each GE jet aircraft engine generates 1 TB of sensor data every 24 Many of the “things” in the Internet of Things (IoT) are hours [6]. The airframe of the Boeing 787 generates half video cameras.