SNIA MSFS Tutorial
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Base Performance for Beegfs with Data Protection
BeeGFS Data Integrity Improvements without impacting Performance Presenter: Dr. M. K. Jibbe Technical Director , NetApp ESG Presenter: Joey Parnell 1 SW2019 StorageArchitect, Developer Conference. NetApp © 2019 NetApp ESG Inc. NetApp Proprietary All Rights Reserved. Current Problem • Lustre has been a common choice in the HPC market for years but the acquisition of the Lustre intellectual property by storage provider in 2018 has created a challenge for users that want to avoid vendor lock-in. • Parallel file system needs are expanding beyond HPC, but most lack enterprise reliability • Any BeeGFS configuration does Lack Data protection at different locations: • Storage Client and servers do not have cache protection in the event of kernel panics, software crashes or power loss. Potential issues are: • Data cached in the BeeGFS client itself • Data cached at the underlying filesystem layer in the storage server (XFS, ZFS, ext4, etc.) • Data cached by the OS kernel itself underneath the filesystem in the storage server Goal of the Team (Remove Competitor and Expand Market Reach of BeeGFS) Parallel File system Improve solution resiliency to ensure data integrity by ML BeeGFS Swim lane identifying potential component faults that could lead to BeeGFS IB and FC data loss and changing system design to handle the fault Address known Market without any data integrity challenges. Opportunity Simple solution and support 2 2 2019 Storage Developer Conference. © 2019 NetApp Inc. NetApp Proprietary All Rights Reserved. Data Integrity & Availability expectations in HPC (Provide Enterprise Reliability with E-series Storage – BeeGFS) However, . The market we serve goes beyond the HPC scratch file system use cases . -
Spring 2014 • Vol
Editorial is published two times a year by The GAUSS Centre Editorial for Supercomputing (HLRS, LRZ, JSC) Welcome to this new issue of inside, tive of the German Research Society the journal on Innovative Supercomput- (DFG) start to create interesting re- Publishers ing in Germany published by the Gauss sults as is shown in our project section. Centre for Supercomputing (GCS). In Prof. Dr. A. Bode | Prof. Dr. Dr. Th. Lippert | Prof. Dr.-Ing. Dr. h.c. Dr. h.c. M. M. Resch this issue, there is a clear focus on With respect to hardware GCS is con- Editor applications. While the race for ever tinuously following its roadmap. New faster systems is accelerated by the systems will become available at HLRS F. Rainer Klank, HLRS [email protected] challenge to build an Exascale system in 2014 and at LRZ in 2015. System within a reasonable power budget, the shipment for HLRS is planned to start Design increased sustained performance allows on August 8 with a chance to start Linda Weinmann, HLRS [email protected] for the solution of ever more complex general operation as early as October Sebastian Zeeden, HLRS [email protected] problems. The range of applications 2014. We will report on further Pia Heusel, HLRS [email protected] presented is impressive covering a progress. variety of different fields. As usual, this issue includes informa- GCS has been a strong player in PRACE tion about events in supercomputing over the last years. In 2015 will con- in Germany over the last months and tinue to provide access to leading edge gives an outlook of workshops in the systems for all European researchers field. -
Efficient Implementation of Data Objects in the OSD+-Based Fusion
Efficient Implementation of Data Objects in the OSD+-Based Fusion Parallel File System Juan Piernas(B) and Pilar Gonz´alez-F´erez Departamento de Ingenier´ıa y Tecnolog´ıa de Computadores, Universidad de Murcia, Murcia, Spain piernas,pilar @ditec.um.es { } Abstract. OSD+s are enhanced object-based storage devices (OSDs) able to deal with both data and metadata operations via data and direc- tory objects, respectively. So far, we have focused on designing and implementing efficient directory objects in OSD+s. This paper, however, presents our work on also supporting data objects, and describes how the coexistence of both kinds of objects in OSD+s is profited to efficiently implement data objects and to speed up some commonfile operations. We compare our OSD+-based Fusion Parallel File System (FPFS) with Lustre and OrangeFS. Results show that FPFS provides a performance up to 37 better than Lustre, and up to 95 better than OrangeFS, × × for metadata workloads. FPFS also provides 34% more bandwidth than OrangeFS for data workloads, and competes with Lustre for data writes. Results also show serious scalability problems in Lustre and OrangeFS. Keywords: FPFS OSD+ Data objects Lustre OrangeFS · · · · 1 Introduction File systems for HPC environment have traditionally used a cluster of data servers for achieving high rates in read and write operations, for providing fault tolerance and scalability, etc. However, due to a growing number offiles, and an increasing use of huge directories with millions or billions of entries accessed by thousands of processes at the same time [3,8,12], some of thesefile systems also utilize a cluster of specialized metadata servers [6,10,11] and have recently added support for distributed directories [7,10]. -
Respecting the Block Interface – Computational Storage Using Virtual Objects
Respecting the block interface – computational storage using virtual objects Ian F. Adams, John Keys, Michael P. Mesnier Intel Labs Abstract immediately obvious, and trade-offs need to be made. Unfor- tunately, this often means that the parallel bandwidth of all Computational storage has remained an elusive goal. Though drives within a storage server will not be available. The same minimizing data movement by placing computation close to problem exists within a single SSD, as the internal NAND storage has quantifiable benefits, many of the previous at- flash bandwidth often exceeds that of the storage controller. tempts failed to take root in industry. They either require a Enter computational storage, an oft-attempted approach to departure from the widespread block protocol to one that address the data movement problem. By keeping computa- is more computationally-friendly (e.g., file, object, or key- tion physically close to its data, we can avoid costly I/O. Var- value), or they introduce significant complexity (state) on top ious designs have been pursued, but the benefits have never of the block protocol. been enough to justify a new storage protocol. In looking We participated in many of these attempts and have since back at the decades of computational storage research, there concluded that neither a departure from nor a significant ad- is a common requirement that the block protocol be replaced dition to the block protocol is needed. Here we introduce a (or extended) with files, objects, or key-value pairs – all of block-compatible design based on virtual objects. Like a real which provide a convenient handle for performing computa- object (e.g., a file), a virtual object contains the metadata that tion. -
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 -
Data Integration and Analysis 10 Distributed Data Storage
1 SCIENCE PASSION TECHNOLOGY Data Integration and Analysis 10 Distributed Data Storage Matthias Boehm Graz University of Technology, Austria Computer Science and Biomedical Engineering Institute of Interactive Systems and Data Science BMK endowed chair for Data Management Last update: Dec 11, 2020 2 Announcements/Org . #1 Video Recording . Link in TeachCenter & TUbe (lectures will be public) . Optional attendance (independent of COVID) . #2 COVID‐19 Restrictions (HS i5) . Corona Traffic Light: RED ORANGE (Dec 07) . Webex lectures revaluated over holidays . #3 Projects and Exercises . 34x SystemDS projects, 11x exercise projects . Final kickoff discussions last week . #4 Course Evaluation . Evaluation period: Dec 15 – Jan 31 . Exam date: TBD, but likely end of January 706.520 Data Integration and Large‐Scale Analysis – 10 Distributed Data Storage Matthias Boehm, Graz University of Technology, WS 2020/21 3 Course Outline Part B: Large‐Scale Data Management and Analysis 12 Distributed Stream 13 Distributed Machine Processing [Jan 15] Learning Systems [Jan 22] 11 Distributed Data‐Parallel Computation [Jan 08] Compute/ Storage 10 Distributed Data Storage [Dec 11] 09 Cloud Resource Management and Scheduling [Dec 04] Infra 08 Cloud Computing Fundamentals [Nov 27] 706.520 Data Integration and Large‐Scale Analysis – 10 Distributed Data Storage Matthias Boehm, Graz University of Technology, WS 2020/21 4 Agenda . Motivation and Terminology . Object Stores and Distributed File Systems . Key‐Value Stores and Cloud DBMS 706.520 Data Integration and Large‐Scale Analysis – 10 Distributed Data Storage Matthias Boehm, Graz University of Technology, WS 2020/21 5 Motivation and Terminology 706.520 Data Integration and Large‐Scale Analysis – 10 Distributed Data Storage Matthias Boehm, Graz University of Technology, WS 2020/21 Motivation and Terminology 6 Overview Distributed Data Storage Global . -
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. -
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. -
Storage Systems and Input/Output 2018 Pre-Workshop Document
Storage Systems and Input/Output 2018 Pre-Workshop Document Gaithersburg, Maryland September 19-20, 2018 Meeting Organizers Robert Ross (ANL) (lead organizer) Glenn Lockwood (LBL) Lee Ward (SNL) (co-lead) Kathryn Mohror (LLNL) Gary Grider (LANL) Bradley Settlemyer (LANL) Scott Klasky (ORNL) Pre-Workshop Document Contributors Philip Carns (ANL) Quincey Koziol (LBL) Matthew Wolf (ORNL) 1 Table of Contents 1 Table of Contents 2 2 Executive Summary 4 3 Introduction 5 4 Mission Drivers 7 4.1 Overview 7 4.2 Workload Characteristics 9 4.2.1 Common observations 9 4.2.2 An example: Adjoint-based sensitivity analysis 10 4.3 Input/Output Characteristics 11 4.4 Implications of In Situ Analysis on the SSIO Community 14 4.5 Data Organization and Archiving 15 4.6 Metadata and Provenance 18 4.7 Summary 20 5 Computer Science Challenges 21 5.1 Hardware/Software Architectures 21 5.1.1 Storage Media and Interfaces 21 5.1.2 Networks 21 5.1.3 Active Storage 22 5.1.4 Resilience 23 5.1.5 Understandability 24 5.1.6 Autonomics 25 5.1.7 Security 26 5.1.8 New Paradigms 27 5.2 Metadata, Name Spaces, and Provenance 28 5.2.1 Metadata 28 5.2.2 Namespaces 30 5.2.3 Provenance 30 5.3 Supporting Science Workflows - SAK 32 5.3.1 DOE Extreme Scale Use cases - SAK 33 5.3.2 Programming Model Integration - (Workflow Composition for on line workflows, and for offline workflows ) - MW 33 5.3.3 Workflows (Engine) - Provision and Placement MW 34 5.3.4 I/O Middleware and Libraries (Connectivity) - both on-and offline, (not or) 35 2 Storage Systems and Input/Output 2018 Pre-Workshop -
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. -
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. -
Towards Transparent Data Access with Context Awareness
Computer Science • 19(2) 2018 https://doi.org/10.7494/csci.2018.19.2.2844 Micha lWrzeszcz Renata G. S lota Jacek Kitowski TOWARDS TRANSPARENT DATA ACCESS WITH CONTEXT AWARENESS Abstract Open-data research is an important factor accelerating the production and analysis of scientific results as well as worldwide collaboration; still, very little dataEarly is being shared at scale. The aim of this bird article is to analyze existing data-access solutions along with their usage limitations. After analyzing the existing solutions and data-access stakeholder needs, the authors propose their own vision of a data-access model. Keywords distributed, transparent data access, distributed data storage, context awareness Citation Computer Science 19(2) 2018: 201{221 201 202 Micha Wrzeszcz, Jacek Kitowski, Renata S lota 1. Introduction Data access and management can be analyzed on several levels; from personal data to globally distributed shared data with different problems to be overcome by data access and management tools. The simplest case is access to local data; i.e., data stored on direct attached storage (DAS), where the focus is on providing the device drivers and solutions that use the hardware optimally. The next level is to provide data access for a group of users working for a single organization; e.g., provision of network attached storage (NAS). The problems en- countered in this case pertain to possible network failures, higher latencies [36], or the simultaneous work of many users influencing the quality of service [48, 59]. The balance of use of available storage systems to maintain QoS and cost effectiveness in an organization while request scheduling and resource allocation is also expected [28,56].