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Better Performance Through a Disk/Persistent-RAM Hybrid Design
The Conquest File System: Better Performance Through a Disk/Persistent-RAM Hybrid Design AN-I ANDY WANG Florida State University GEOFF KUENNING Harvey Mudd College PETER REIHER, GERALD POPEK University of California, Los Angeles ________________________________________________________________________ Modern file systems assume the use of disk, a system-wide performance bottleneck for over a decade. Current disk caching and RAM file systems either impose high overhead to access memory content or fail to provide mechanisms to achieve data persistence across reboots. The Conquest file system is based on the observation that memory is becoming inexpensive, which enables all file system services to be delivered from memory, except providing large storage capacity. Unlike caching, Conquest uses memory with battery backup as persistent storage, and provides specialized and separate data paths to memory and disk. Therefore, the memory data path contains no disk-related complexity. The disk data path consists of only optimizations for the specialized disk usage pattern. Compared to a memory-based file system, Conquest incurs little performance overhead. Compared to several disk-based file systems, Conquest achieves 1.3x to 19x faster memory performance, and 1.4x to 2.0x faster performance when exercising both memory and disk. Conquest realizes most of the benefits of persistent RAM at a fraction of the cost of a RAM-only solution. Conquest also demonstrates that disk-related optimizations impose high overheads for accessing memory content in a memory-rich environment. Categories and Subject Descriptors: D.4.2 [Operating Systems]: Storage Management—Storage Hierarchies; D.4.3 [Operating Systems]: File System Management—Access Methods and Directory Structures; D.4.8 [Operating Systems]: Performance—Measurements General Terms: Design, Experimentation, Measurement, and Performance Additional Key Words and Phrases: Persistent RAM, File Systems, Storage Management, and Performance Measurement ________________________________________________________________________ 1. -
System Calls System Calls
System calls We will investigate several issues related to system calls. Read chapter 12 of the book Linux system call categories file management process management error handling note that these categories are loosely defined and much is behind included, e.g. communication. Why? 1 System calls File management system call hierarchy you may not see some topics as part of “file management”, e.g., sockets 2 System calls Process management system call hierarchy 3 System calls Error handling hierarchy 4 Error Handling Anything can fail! System calls are no exception Try to read a file that does not exist! Error number: errno every process contains a global variable errno errno is set to 0 when process is created when error occurs errno is set to a specific code associated with the error cause trying to open file that does not exist sets errno to 2 5 Error Handling error constants are defined in errno.h here are the first few of errno.h on OS X 10.6.4 #define EPERM 1 /* Operation not permitted */ #define ENOENT 2 /* No such file or directory */ #define ESRCH 3 /* No such process */ #define EINTR 4 /* Interrupted system call */ #define EIO 5 /* Input/output error */ #define ENXIO 6 /* Device not configured */ #define E2BIG 7 /* Argument list too long */ #define ENOEXEC 8 /* Exec format error */ #define EBADF 9 /* Bad file descriptor */ #define ECHILD 10 /* No child processes */ #define EDEADLK 11 /* Resource deadlock avoided */ 6 Error Handling common mistake for displaying errno from Linux errno man page: 7 Error Handling Description of the perror () system call. -
Red Hat Data Analytics Infrastructure Solution
TECHNOLOGY DETAIL RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION TABLE OF CONTENTS 1 INTRODUCTION ................................................................................................................ 2 2 RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION ..................................... 2 2.1 The evolution of analytics infrastructure ....................................................................................... 3 Give data scientists and data 2.2 Benefits of a shared data repository on Red Hat Ceph Storage .............................................. 3 analytics teams access to their own clusters without the unnec- 2.3 Solution components ...........................................................................................................................4 essary cost and complexity of 3 TESTING ENVIRONMENT OVERVIEW ............................................................................ 4 duplicating Hadoop Distributed File System (HDFS) datasets. 4 RELATIVE COST AND PERFORMANCE COMPARISON ................................................ 6 4.1 Findings summary ................................................................................................................................. 6 Rapidly deploy and decom- 4.2 Workload details .................................................................................................................................... 7 mission analytics clusters on 4.3 24-hour ingest ........................................................................................................................................8 -
Unlock Bigdata Analytic Efficiency with Ceph Data Lake
Unlock Bigdata Analytic Efficiency With Ceph Data Lake Jian Zhang, Yong Fu, March, 2018 Agenda . Background & Motivations . The Workloads, Reference Architecture Evolution and Performance Optimization . Performance Comparison with Remote HDFS . Summary & Next Step 3 Challenges of scaling Hadoop* Storage BOUNDED Storage and Compute resources on Hadoop Nodes brings challenges Data Capacity Silos Costs Performance & efficiency Typical Challenges Data/Capacity Multiple Storage Silos Space, Spent, Power, Utilization Upgrade Cost Inadequate Performance Provisioning And Configuration Source: 451 Research, Voice of the Enterprise: Storage Q4 2015 *Other names and brands may be claimed as the property of others. 4 Options To Address The Challenges Compute and Large Cluster More Clusters Storage Disaggregation • Lacks isolation - • Cost of • Isolation of high- noisy neighbors duplicating priority workloads hinder SLAs datasets across • Shared big • Lacks elasticity - clusters datasets rigid cluster size • Lacks on-demand • On-demand • Can’t scale provisioning provisioning compute/storage • Can’t scale • compute/storage costs separately compute/storage costs scale costs separately separately Compute and Storage disaggregation provides Simplicity, Elasticity, Isolation 5 Unified Hadoop* File System and API for cloud storage Hadoop Compatible File System abstraction layer: Unified storage API interface Hadoop fs –ls s3a://job/ adl:// oss:// s3n:// gs:// s3:// s3a:// wasb:// 2006 2008 2014 2015 2016 6 Proposal: Apache Hadoop* with disagreed Object Storage SQL …… Hadoop Services • Virtual Machine • Container • Bare Metal HCFS Compute 1 Compute 2 Compute 3 … Compute N Object Storage Services Object Object Object Object • Co-located with gateway Storage 1 Storage 2 Storage 3 … Storage N • Dynamic DNS or load balancer • Data protection via storage replication or erasure code Disaggregated Object Storage Cluster • Storage tiering *Other names and brands may be claimed as the property of others. -
CIS 191 Linux Lab Exercise
CIS 191 Linux Lab Exercise Lab 9: Backup and Restore Fall 2008 Lab 9: Backup and Restore The purpose of this lab is to explore the different types of backups and methods of restoring them. We will look at three utilities often used for backups and learn the advantages and disadvantages of each. Supplies • VMWare Server 1.05 or higher • Benji VM Preconfiguration • Labs 6 and Labs 7 completed on a pristine Benji VM [root@benji /]# fdisk -l Disk /dev/sda: 5368 MB, 5368709120 bytes 255 heads, 63 sectors/track, 652 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Device Boot Start End Blocks Id System /dev/sda1 * 1 382 3068383+ 83 Linux /dev/sda2 383 447 522112+ 82 Linux swap / Solaris /dev/sda3 448 511 514080 83 Linux /dev/sda4 512 652 1132582+ 5 Extended /dev/sda5 512 549 305203+ 83 Linux /dev/sda6 550 556 56196 83 Linux /dev/sda7 557 581 200781 83 Linux [root@benji /]# [root@benji /]# mount /dev/sda1 on / type ext3 (rw) proc on /proc type proc (rw) sysfs on /sys type sysfs (rw) devpts on /dev/pts type devpts (rw,gid=5,mode=620) tmpfs on /dev/shm type tmpfs (rw) /dev/sda5 on /opt type ext3 (rw) /dev/sda3 on /var type ext3 (rw) none on /proc/sys/fs/binfmt_misc type binfmt_misc (rw) sunrpc on /var/lib/nfs/rpc_pipefs type rpc_pipefs (rw) /dev/sda7 on /home type ext3 (rw,usrquota) [root@benji /]# cat /etc/fstab LABEL=/1 / ext3 defaults 1 1 devpts /dev/pts devpts gid=5,mode=620 0 0 tmpfs /dev/shm tmpfs defaults 0 0 LABEL=/opt /opt ext3 defaults 1 2 proc /proc proc defaults 0 0 sysfs /sys sysfs defaults 0 0 LABEL=/var /var ext3 defaults 1 2 LABEL=SWAP-sda2 swap swap defaults 0 0 LABEL=/home /home ext3 usrquota,defaults 1 2 [root@benji /]# Forum If you get stuck on one of the steps below don’t beat your head against the wall. -
Connecting the Storage System to the Solaris Host
Configuration Guide for Solaris™ Host Attachment Hitachi Virtual Storage Platform Hitachi Universal Storage Platform V/VM FASTFIND LINKS Document Organization Product Version Getting Help Contents MK-96RD632-05 Copyright © 2010 Hitachi, Ltd., all rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or stored in a database or retrieval system for any purpose without the express written permission of Hitachi, Ltd. (hereinafter referred to as “Hitachi”) and Hitachi Data Systems Corporation (hereinafter referred to as “Hitachi Data Systems”). Hitachi Data Systems reserves the right to make changes to this document at any time without notice and assumes no responsibility for its use. This document contains the most current information available at the time of publication. When new and/or revised information becomes available, this entire document will be updated and distributed to all registered users. All of the features described in this document may not be currently available. Refer to the most recent product announcement or contact your local Hitachi Data Systems sales office for information about feature and product availability. Notice: Hitachi Data Systems products and services can be ordered only under the terms and conditions of the applicable Hitachi Data Systems agreement(s). The use of Hitachi Data Systems products is governed by the terms of your agreement(s) with Hitachi Data Systems. Hitachi is a registered trademark of Hitachi, Ltd. in the United States and other countries. Hitachi Data Systems is a registered trademark and service mark of Hitachi, Ltd. -
Key Exchange Authentication Protocol for Nfs Enabled Hdfs Client
Journal of Theoretical and Applied Information Technology 15 th April 2017. Vol.95. No 7 © 2005 – ongoing JATIT & LLS ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 KEY EXCHANGE AUTHENTICATION PROTOCOL FOR NFS ENABLED HDFS CLIENT 1NAWAB MUHAMMAD FASEEH QURESHI, 2*DONG RYEOL SHIN, 3ISMA FARAH SIDDIQUI 1,2 Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea 3 Department of Software Engineering, Mehran UET, Pakistan *Corresponding Author E-mail: [email protected], 2*[email protected], [email protected] ABSTRACT By virtue of its built-in processing capabilities for large datasets, Hadoop ecosystem has been utilized to solve many critical problems. The ecosystem consists of three components; client, Namenode and Datanode, where client is a user component that requests cluster operations through Namenode and processes data blocks at Datanode enabled with Hadoop Distributed File System (HDFS). Recently, HDFS has launched an add-on to connect a client through Network File System (NFS) that can upload and download set of data blocks over Hadoop cluster. In this way, a client is not considered as part of the HDFS and could perform read and write operations through a contrast file system. This HDFS NFS gateway has raised many security concerns, most particularly; no reliable authentication support of upload and download of data blocks, no local and remote client efficient connectivity, and HDFS mounting durability issues through untrusted connectivity. To stabilize the NFS gateway strategy, we present in this paper a Key Exchange Authentication Protocol (KEAP) between NFS enabled client and HDFS NFS gateway. The proposed approach provides cryptographic assurance of authentication between clients and gateway. -
Life in the Fast Lane: Optimisation for Interactive and Batch Jobs Nikola Marković, Boemska T.S
Paper 11825-2016 Life in the Fast Lane: Optimisation for Interactive and Batch Jobs Nikola Marković, Boemska T.S. Ltd.; Greg Nelson, ThotWave Technologies LLC. ABSTRACT We spend so much time talking about GRID environments, distributed jobs, and huge data volumes that we ignore the thousands of relatively tiny programs scheduled to run every night, which produce only a few small data sets, but make all the difference to the users who depend on them. Individually, these jobs might place a negligible load on the system, but by their sheer number they can often account for a substantial share of overall resources, sometimes even impacting the performance of the bigger, more important jobs. SAS® programs, by their varied nature, use available resources in a varied way. Depending on whether a SAS® procedure is CPU-, disk- or memory-intensive, chunks of memory or CPU can sometimes remain unused for quite a while. Bigger jobs leave bigger chunks, and this is where being small and able to effectively exploit those leftovers can be a great advantage. We call our main optimization technique the Fast Lane, which is a queue configuration dedicated to jobs with consistently small SASWORK directories, that, when available, lets them use unused RAM in place of their SASWORK disk. The approach improves overall CPU saturation considerably while taking loads off the I/O subsystem, and without failure results in improved runtimes for big jobs and small jobs alike, without requiring any changes to deployed code. This paper explores the practical aspects of implementing the Fast Lane on your environment. -
Winreporter Documentation
WinReporter documentation Table Of Contents 1.1 WinReporter overview ............................................................................................... 1 1.1.1.................................................................................................................................... 1 1.1.2 Web site support................................................................................................. 1 1.2 Requirements.............................................................................................................. 1 1.3 License ....................................................................................................................... 1 2.1 Welcome to WinReporter........................................................................................... 2 2.2 Scan requirements ...................................................................................................... 2 2.3 Simplified Wizard ...................................................................................................... 3 2.3.1 Simplified Wizard .............................................................................................. 3 2.3.2 Computer selection............................................................................................. 3 2.3.3 Validate .............................................................................................................. 4 2.4 Advanced Wizard....................................................................................................... 5 2.4.1 -
007-2007: the FILENAME Statement Revisited
SAS Global Forum 2007 Applications Development Paper 007-2007 The FILENAME Statement Revisited Yves DeGuire, Statistics Canada, Ottawa, Ontario, Canada ABSTRACT The FILENAME statement has been around for a long time for connecting external files with SAS®. Over the years, it has grown quite a bit to encompass capabilities that go beyond the simple sequential file. Most SAS programmers have used the FILENAME statement to read/write external files stored on disk. However, few programmers have used it to access devices other than disk or to interface with different access methods. This paper focuses on some of those capabilities by showing you some interesting devices and access methods supported by the FILENAME statement. INTRODUCTION – FILENAME STATEMENT 101 The FILENAME statement is a declarative statement that is used to associate a fileref with a physical file or a device. A fileref is a logical name that can be used subsequently in lieu of a physical name. The syntax for the FILENAME statement is as follows: FILENAME fileref <device-type> <'external-file'> <options>; This is the basic syntax to associate an external file or a device to a fileref. There are other forms that allow you to list or clear filerefs. Please refer to SAS OnlineDoc® documentation for a complete syntax of the FILENAME statement. Once the association has been made between a physical file and a fileref, the fileref can be used in as many DATA steps as necessary without having to re-run a FILENAME statement. Within a DATA step, the use of an INFILE or a FILE statement allows the programmer to determine which file to read or write using an INPUT or a PUT statement. -
Managing File Systems in Oracle® Solaris 11.4
® Managing File Systems in Oracle Solaris 11.4 Part No: E61016 November 2020 Managing File Systems in Oracle Solaris 11.4 Part No: E61016 Copyright © 2004, 2020, Oracle and/or its affiliates. License Restrictions Warranty/Consequential Damages Disclaimer This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. Warranty Disclaimer The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. Restricted Rights Notice If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial -
HFAA: a Generic Socket API for Hadoop File Systems
HFAA: A Generic Socket API for Hadoop File Systems Adam Yee Jeffrey Shafer University of the Pacific University of the Pacific Stockton, CA Stockton, CA [email protected] jshafer@pacific.edu ABSTRACT vices: one central NameNode and many DataNodes. The Hadoop is an open-source implementation of the MapReduce NameNode is responsible for maintaining the HDFS direc- programming model for distributed computing. Hadoop na- tory tree. Clients contact the NameNode in order to perform tively integrates with the Hadoop Distributed File System common file system operations, such as open, close, rename, (HDFS), a user-level file system. In this paper, we intro- and delete. The NameNode does not store HDFS data itself, duce the Hadoop Filesystem Agnostic API (HFAA) to allow but rather maintains a mapping between HDFS file name, Hadoop to integrate with any distributed file system over a list of blocks in the file, and the DataNode(s) on which TCP sockets. With this API, HDFS can be replaced by dis- those blocks are stored. tributed file systems such as PVFS, Ceph, Lustre, or others, thereby allowing direct comparisons in terms of performance Although HDFS stores file data in a distributed fashion, and scalability. Unlike previous attempts at augmenting file metadata is stored in the centralized NameNode service. Hadoop with new file systems, the socket API presented here While sufficient for small-scale clusters, this design prevents eliminates the need to customize Hadoop’s Java implementa- Hadoop from scaling beyond the resources of a single Name- tion, and instead moves the implementation responsibilities Node. Prior analysis of CPU and memory requirements for to the file system itself.