A Flexible and Adaptable Distributed File System
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Gluster Roadmap: Recent Improvements and Upcoming Features
Gluster roadmap: Recent improvements and upcoming features Niels de Vos GlusterFS co-maintainer [email protected] Agenda ● Introduction into Gluster ● Quick Start ● Current stable releases ● History of feature additions ● Plans for the upcoming 3.8 and 4.0 release ● Detailed description of a few select features FOSDEM, 30 January 2016 2 What is GlusterFS? ● Scalable, general-purpose storage platform ● POSIX-y Distributed File System ● Object storage (swift) ● Distributed block storage (qemu) ● Flexible storage (libgfapi) ● No Metadata Server ● Heterogeneous Commodity Hardware ● Flexible and Agile Scaling ● Capacity – Petabytes and beyond ● Performance – Thousands of Clients FOSDEM, 30 January 2016 3 Terminology ● Brick ● Fundamentally, a filesystem mountpoint ● A unit of storage used as a capacity building block ● Translator ● Logic between the file bits and the Global Namespace ● Layered to provide GlusterFS functionality FOSDEM, 30 January 2016 4 Terminology ● Volume ● Bricks combined and passed through translators ● Ultimately, what's presented to the end user ● Peer / Node ● Server hosting the brick filesystems ● Runs the Gluster daemons and participates in volumes ● Trusted Storage Pool ● A group of peers, like a “Gluster cluster” FOSDEM, 30 January 2016 5 Scale-out and Scale-up FOSDEM, 30 January 2016 6 Distributed Volume ● Files “evenly” spread across bricks ● Similar to file-level RAID 0 ● Server/Disk failure could be catastrophic FOSDEM, 30 January 2016 7 Replicated Volume ● Copies files to multiple bricks ● Similar to file-level -
Storage Virtualization for KVM – Putting the Pieces Together
Storage Virtualization for KVM – Putting the pieces together Bharata B Rao – [email protected] Deepak C Shettty – [email protected] M Mohan Kumar – [email protected] (IBM Linux Technology Center, Bangalore) Balamurugan Aramugam - [email protected] Shireesh Anjal – [email protected] (RedHat, Bangalore) Aug 2012 LPC2012 Linux is a registered trademark of Linus Torvalds. Agenda ● Problems around storage in virtualization ● GlusterFS as virt-ready file system – QEMU-GlusterFS integration – GlusterFS – Block device translator ● Virtualization management - oVirt and VDSM – VDSM-GlusterFS integration ● Storage integration – libstoragemgmt Problems in storage/FS in KVM virtualization ● Multiple choices for file system and virtualization management ● Lack of virtualization aware file systems ● File systems/storage functionality implemented in other layers of virtualization stack – Snapshots, block streaming, image formats in QEMU ● No well defined interface points in the virtualization stack for storage integration ● No standard interface/APIs available for services like backup and restore ● Need for a single FS/storage solution that works for local, SAN and NAS storage – Mixing different types of storage into a single filesystem namespace GlusterFS ● User space distributed file system that scales to several petabytes ● Aggregates storage resources from multiple nodes and presents a unified file system namespace GlusterFS - features ● Replication ● Striping ● Distribution ● Geo-replication/sync ● Online volume extension -
Glusterfs Documentation Release 3.8.0
GlusterFS Documentation Release 3.8.0 Gluster Community Aug 10, 2016 Contents 1 Quick Start Guide 3 1.1 Single Node Cluster...........................................3 1.2 Multi Node Cluster............................................4 2 Overview and Concepts 7 2.1 Volume Types..............................................7 2.2 FUSE................................................... 10 2.3 Translators................................................ 12 2.4 Geo-Replication............................................. 17 2.5 Terminologies.............................................. 19 3 Installation Guide 23 3.1 Getting Started.............................................. 23 3.2 Configuration............................................... 24 3.3 Installing Gluster............................................. 26 3.4 Overview................................................. 27 3.5 Quick Start Guide............................................ 28 3.6 Setup Baremetal............................................. 29 3.7 Deploying in AWS............................................ 30 3.8 Setting up in virtual machines...................................... 31 4 Administrator Guide 33 5 Upgrade Guide 35 6 Contributors Guide 37 6.1 Adding your blog............................................. 37 6.2 Bug Lifecycle.............................................. 37 6.3 Bug Reporting Guidelines........................................ 38 6.4 Bug Triage Guidelines.......................................... 41 7 Changelog 47 8 Presentations 49 i ii GlusterFS -
Andrew File System (AFS) Google File System February 5, 2004
Advanced Topics in Computer Systems, CS262B Prof Eric A. Brewer Andrew File System (AFS) Google File System February 5, 2004 I. AFS Goal: large-scale campus wide file system (5000 nodes) o must be scalable, limit work of core servers o good performance o meet FS consistency requirements (?) o managable system admin (despite scale) 400 users in the “prototype” -- a great reality check (makes the conclusions meaningful) o most applications work w/o relinking or recompiling Clients: o user-level process, Venus, that handles local caching, + FS interposition to catch all requests o interaction with servers only on file open/close (implies whole-file caching) o always check cache copy on open() (in prototype) Vice (servers): o Server core is trusted; called “Vice” o servers have one process per active client o shared data among processes only via file system (!) o lock process serializes and manages all lock/unlock requests o read-only replication of namespace (centralized updates with slow propagation) o prototype supported about 20 active clients per server, goal was >50 Revised client cache: o keep data cache on disk, metadata cache in memory o still whole file caching, changes written back only on close o directory updates are write through, but cached locally for reads o instead of check on open(), assume valid unless you get an invalidation callback (server must invalidate all copies before committing an update) o allows name translation to be local (since you can now avoid round-trip for each step of the path) Revised servers: 1 o move -
Improving Learning of Programming Through E-Learning by Using Asynchronous Virtual Pair Programming
Improving Learning of Programming Through E-Learning by Using Asynchronous Virtual Pair Programming Abdullah Mohd ZIN Department of Industrial Computing Faculty of Information Science and Technology National University of Malaysia Bangi, MALAYSIA Sufian IDRIS Department of Computer Science Faculty of Information Science and Technology National University of Malaysia Bangi, MALAYSIA Nantha Kumar SUBRAMANIAM Open University Malaysia (OUM) Faculty of Information Technology and Multimedia Communication Kuala Lumpur, MALAYSIA ABSTRACT The problem of learning programming subjects, especially through distance learning and E-Learning, has been widely reported in literatures. Many attempts have been made to solve these problems. This has led to many new approaches in the techniques of learning of programming. One of the approaches that have been proposed is the use of virtual pair programming (VPP). Most of the studies about VPP in distance learning or e-learning environment focus on the use of the synchronous mode of collaboration between learners. Not much research have been done about asynchronous VPP. This paper describes how we have implemented VPP and a research that has been carried out to study the effectiveness of asynchronous VPP for learning of programming. In particular, this research study the effectiveness of asynchronous VPP in the learning of object-oriented programming among students at Open University Malaysia (OUM). The result of the research has shown that most of the learners have given positive feedback, indicating that they are happy with the use of asynchronous VPP. At the same time, learners did recommend some extra features that could be added in making asynchronous VPP more enjoyable. Keywords: Pair-programming; Virtual Pair-programming; Object Oriented Programming INTRODUCTION Delivering program of Information Technology through distance learning or E- Learning is indeed a very challenging task. -
The Joe-E Language Specification (Draft)
The Joe-E Language Specification (draft) Adrian Mettler David Wagner famettler,[email protected] February 3, 2008 Disclaimer: This is a draft version of the Joe-E specification, and is subject to change. Sections 5 - 7 mention some (but not all) of the aspects of the Joe-E language that are future work or current works in progress. 1 Introduction We describe the Joe-E language, a capability-based subset of Java intended to make it easier to build secure systems. The goal of object capability languages is to support the Principle of Least Authority (POLA), so that each object naturally receives the least privilege (i.e., least authority) needed to do its job. Thus, we hope that Joe-E will support secure programming while remaining familiar to Java programmers everywhere. 2 Goals We have several goals for the Joe-E language: • Be familiar to Java programmers. To minimize the barriers to adoption of Joe-E, the syntax and semantics of Joe-E should be familiar to Java programmers. We also want Joe-E programmers to be able to use all of their existing tools for editing, compiling, executing, debugging, profiling, and reasoning about Java code. We accomplish this by defining Joe-E as a subset of Java. In general: Subsetting Principle: Any valid Joe-E program should also be a valid Java program, with identical semantics. This preserves the semantics Java programmers will expect, which are critical to keeping the adoption costs manageable. Also, it means all of today's Java tools (IDEs, debuggers, profilers, static analyzers, theorem provers, etc.) will apply to Joe-E code. -
A Survey of Distributed File Systems
A Survey of Distributed File Systems M. Satyanarayanan Department of Computer Science Carnegie Mellon University February 1989 Abstract Abstract This paper is a survey of the current state of the art in the design and implementation of distributed file systems. It consists of four major parts: an overview of background material, case studies of a number of contemporary file systems, identification of key design techniques, and an examination of current research issues. The systems surveyed are Sun NFS, Apollo Domain, Andrew, IBM AIX DS, AT&T RFS, and Sprite. The coverage of background material includes a taxonomy of file system issues, a brief history of distributed file systems, and a summary of empirical research on file properties. A comprehensive bibliography forms an important of the paper. Copyright (C) 1988,1989 M. Satyanarayanan The author was supported in the writing of this paper by the National Science Foundation (Contract No. CCR-8657907), Defense Advanced Research Projects Agency (Order No. 4976, Contract F33615-84-K-1520) and the IBM Corporation (Faculty Development Award). The views and conclusions in this document are those of the author and do not represent the official policies of the funding agencies or Carnegie Mellon University. 1 1. Introduction The sharing of data in distributed systems is already common and will become pervasive as these systems grow in scale and importance. Each user in a distributed system is potentially a creator as well as a consumer of data. A user may wish to make his actions contingent upon information from a remote site, or may wish to update remote information. -
Shared File Systems: Determining the Best Choice for Your Distributed SAS® Foundation Applications Margaret Crevar, SAS Institute Inc., Cary, NC
Paper SAS569-2017 Shared File Systems: Determining the Best Choice for your Distributed SAS® Foundation Applications Margaret Crevar, SAS Institute Inc., Cary, NC ABSTRACT If you are planning on deploying SAS® Grid Manager and SAS® Enterprise BI (or other distributed SAS® Foundation applications) with load balanced servers on multiple operating systems instances, , a shared file system is required. In order to determine the best shared file system choice for a given deployment, it is important to understand how the file system is used, the SAS® I/O workload characteristics performed on it, and the stressors that SAS Foundation applications produce on the file system. For the purposes of this paper, we use the term "shared file system" to mean both a clustered file system and shared file system, even though" shared" can denote a network file system and a distributed file system – not clustered. INTRODUCTION This paper examines the shared file systems that are most commonly used with SAS and reviews their strengths and weaknesses. SAS GRID COMPUTING REQUIREMENTS FOR SHARED FILE SYSTEMS Before we get into the reasons why a shared file system is needed for SAS® Grid Computing, let’s briefly discuss the SAS I/O characteristics. GENERAL SAS I/O CHARACTERISTICS SAS Foundation creates a high volume of predominately large-block, sequential access I/O, generally at block sizes of 64K, 128K, or 256K, and the interactions with data storage are significantly different from typical interactive applications and RDBMSs. Here are some major points to understand (more details about the bullets below can be found in this paper): SAS tends to perform large sequential Reads and Writes. -
Texttext Introduction Into Glusterfs Martin Alfke <Martin.Alfke@Buero20
Introduction into GlusterFS Martin Alfke <[email protected]> TextText Wednesday, July 7, 2010 1 Agenda • General Information on GlusterFS • Architecture Overview • GlusterFS Translators • GlusterFS Configuration Wednesday, July 7, 2010 2 General Information Wednesday, July 7, 2010 3 General Information I File System Solutions Shared Disk File System •San or Block Access •Mostly used in HA setup (e.g. DRBD) Distributed File System •Network File System •NFS •SMB/CIFS •9P Distributed replicated File System •Replication •HA and offline operation •Coda •MS DFS •MooseFS 4 Wednesday, July 7, 2010 4 General Information II File System Solutions Distributed parallel File System •Setup across multiple servers •HPC Distributed replicated parallel File System •HPC and HA •Cosmos •MogileFS •GPFS (IBM) •GFS (Google) •Hadoop •GlusterFS 5 Wednesday, July 7, 2010 5 Customer Platform Shared Storage Issues Bad performance of NFS kernel stack Limitations of concurrent NFS accesses Customer data already on NFS system Environment and Requirements Debian GNU/Linux Version 4 (etch) – 3 years old Most D f-t p FS need complex data migration Solution has to be expandable 6 Wednesday, July 7, 2010 6 Why GlusterFS ? Decision basics Possibility to run NFS and GlusterFS in parallel No data migration necessary Easy setup Extendable (e.g. new storage nodes) min. Kernel 2.6.3x --> optimization for FUSE context switches ! 7 Wednesday, July 7, 2010 7 Architecture Overview Wednesday, July 7, 2010 8 GlusterFS Basics Hardware Any x86 Hardware Direct attached storage, RAID FC, Infiniband or iSCSI SAN Gigabit or 10 Gigabit network or Infiniband OS Linux, Solaris, OpenSolaris, OS X, FreeBSD ext3 or ext4 Filesystem (tested) Other POSIX compliant filesystems should also work Architecture No Meta-Data Server (fully distributed architecture - Elastic Hash) Replication (RAID 1) Distribution (RAID 0) FUSE (Standard) NFS (unfs3 - depreciated) SMB/CIFS DAV 9 Wednesday, July 7, 2010 9 GlusterFS Architecture Overview I Distribution Replication Images Copyright by Gluster.Inc. -
The Influence of Scale on Distributed File System Design
IEEE TRANSAmIONS ON SOFIWARE ENGINEERING, VOL. 18, NO. I, JANUARY lY92 The Influence of Scale on Distributed File System Design Mahadev Satyanarayanan, Member, IEEE Abstract- Scale should be recognized as a primary factor into autonomous or semi-autonomous organizations for man- influencing the architecture and implementation of distributed agement purposes. Hence a distributed system that has many systems. This paper uses Andrew and Coda, distributed file users or nodes will also span many organizations. Regardless systems built at Carnegie Mellon University, to validate this proposition. Performance, operability, and security are dominant of the specific metric of scale, the designs of distributed considerations in the design of these systems. Availability is a systems that scale well are fundamentally different from less further consideration in the design of Coda. Client caching, scalable designs. bulk data transfer, token-based mutual authentication, and hi- In this paper we describe the lessons we have learned erarchical organization of the protection domain have emerged about scalability from the Andrew File System and the Codu as mechanisms that enhance scalability. The separation of con- cerns made possible by functional specialization has also proved File System. These systems are particularly appropriate for valuable in scaling. Heterogeneity is an important by-product our discussion, because they have been designed for growth of growth, but the mechanisms available to cope with it are to thousands of nodes and users. We focus on distributed rudimentary. Physical separation of clients and servers turns out file systems, because they are the most widely used kind of to be a critical requirement for scalability. -
Distributed File Systems
Please note: Please start working your research project (proposal will be due on Feb. 19 in class) Each group needs to turn in a printed version of their proposal and intermediate report. Also, before class each group needs to email me a DOC version. Instructor’s Guide for Coulouris, Dollimore and Kindberg Distributed Systems: Concepts and Design Edn. 4 © Pearson Education 2005 Remote Procedure Call (1): at-least-once or at-most-once semantics client: "stub" instead of "proxy" (same function, different names) behaves like a local procedure, marshal arguments, communicate the request server: dispatcher "stub": unmarshal arguments, communicate the results back Instructor’s Guide for Coulouris, Dollimore and Kindberg Distributed Systems: Concepts and Design Edn. 4 © Pearson Education 2005 Remote Procedure Call (2) client process server process Request Reply client stub server stub procedure procedure client service program Communication Communication procedure module module dispatcher Instructor’s Guide for Coulouris, Dollimore and Kindberg Distributed Systems: Concepts and Design Edn. 4 © Pearson Education 2005 Sun RPC (1): Designed for client-server communication in the SUN NFS (network file system) Supplied as a part of SUN and other UNIX operating systems Over either UDP or TCP Provides an interface definition language (IDL) initially XDR is for data representation, extended to be IDL less modern than CORBA IDL and Java program numbers (obtained from a central authority) instead of interface names procedure numbers (used as a procedure identifier) instead of procedure names only a single input parameter is allowed (then we have to use a ?) Offers an interface compiler (rpcgen) for C language, which generates the following: client stub server main procedure, dispatcher, and server stub XDR marshalling, unmarshaling Instructor’s Guide for Coulouris, Dollimore and Kindberg Distributed Systems: Concepts and Design Edn. -
Design and Evolution of the Apache Hadoop File System(HDFS)
Design and Evolution of the Apache Hadoop File System(HDFS) Dhruba Borthakur Engineer@Facebook Committer@Apache HDFS SDC, Sept 19 2011 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Outline Introduction Yet another file-system, why? Goals of Hadoop Distributed File System (HDFS) Architecture Overview Rational for Design Decisions 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Who Am I? Apache Hadoop FileSystem (HDFS) Committer and PMC Member Core contributor since Hadoop’s infancy Facebook (Hadoop, Hive, Scribe) Yahoo! (Hadoop in Yahoo Search) Veritas (San Point Direct, Veritas File System) IBM Transarc (Andrew File System) Univ of Wisconsin Computer Science Alumni (Condor Project) 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Hadoop, Why? Need to process Multi Petabyte Datasets Data may not have strict schema Expensive to build reliability in each application. Failure is expected, rather than exceptional. Elasticity, # of nodes in a cluster is never constant. Need common infrastructure Efficient, reliable, Open Source Apache License 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Goals of HDFS Very Large Distributed File System 10K nodes, 1 billion files, 100 PB Assumes Commodity Hardware Files are replicated to handle hardware failure Detect failures and recovers from them Optimized for Batch Processing Data locations exposed so that computations can move to where data resides Provides very high aggregate bandwidth User Space, runs on heterogeneous OS 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Commodity Hardware Typically in 2 level architecture – Nodes are commodity PCs – 20-40 nodes/rack – Uplink from rack is 4 gigabit – Rack-internal is 1 gigabit 2011 Storage Developer Conference.