Globalfs: a Strongly Consistent Multi-Site File System
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Elastic Storage for Linux on System Z IBM Research & Development Germany
Peter Münch T/L Test and Development – Elastic Storage for Linux on System z IBM Research & Development Germany Elastic Storage for Linux on IBM System z © Copyright IBM Corporation 2014 9.0 Elastic Storage for Linux on System z Session objectives • This presentation introduces the Elastic Storage, based on General Parallel File System technology that will be available for Linux on IBM System z. Understand the concepts of Elastic Storage and which functions will be available for Linux on System z. Learn how you can integrate and benefit from the Elastic Storage in a Linux on System z environment. Finally, get your first impression in a live demo of Elastic Storage. 2 © Copyright IBM Corporation 2014 Elastic Storage for Linux on System z Trademarks The following are trademarks of the International Business Machines Corporation in the United States and/or other countries. AIX* FlashSystem Storwize* Tivoli* DB2* IBM* System p* WebSphere* DS8000* IBM (logo)* System x* XIV* ECKD MQSeries* System z* z/VM* * Registered trademarks of IBM Corporation The following are trademarks or registered trademarks of other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Intel, Intel logo, Intel Inside, Intel Inside logo, Intel Centrino, Intel Centrino logo, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. -
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. -
BESIII Physical Analysis on Hadoop Platform
20th International Conference on Computing in High Energy and Nuclear Physics (CHEP2013) IOP Publishing Journal of Physics: Conference Series 513 (2014) 032044 doi:10.1088/1742-6596/513/3/032044 BESIII Physical Analysis on Hadoop Platform Jing HUO12, Dongsong ZANG12, Xiaofeng LEI12, Qiang LI12, Gongxing SUN1 1Institute of High Energy Physics, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China E-mail: [email protected] Abstract. In the past 20 years, computing cluster has been widely used for High Energy Physics data processing. The jobs running on the traditional cluster with a Data-to-Computing structure, have to read large volumes of data via the network to the computing nodes for analysis, thereby making the I/O latency become a bottleneck of the whole system. The new distributed computing technology based on the MapReduce programming model has many advantages, such as high concurrency, high scalability and high fault tolerance, and it can benefit us in dealing with Big Data. This paper brings the idea of using MapReduce model to do BESIII physical analysis, and presents a new data analysis system structure based on Hadoop platform, which not only greatly improve the efficiency of data analysis, but also reduces the cost of system building. Moreover, this paper establishes an event pre-selection system based on the event level metadata(TAGs) database to optimize the data analyzing procedure. 1. Introduction 1.1 The current BESIII computing architecture High Energy Physics experiment is a typical data-intensive application. The BESIII computing system now consist of 3PB+ data, 6500 CPU cores, and it is estimated that there will be more than 10PB data produced in the future 5 years. -
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]. -
Membrane: Operating System Support for Restartable File Systems Swaminathan Sundararaman, Sriram Subramanian, Abhishek Rajimwale, Andrea C
Membrane: Operating System Support for Restartable File Systems Swaminathan Sundararaman, Sriram Subramanian, Abhishek Rajimwale, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Michael M. Swift Computer Sciences Department, University of Wisconsin, Madison Abstract and most complex code bases in the kernel. Further, We introduce Membrane, a set of changes to the oper- file systems are still under active development, and new ating system to support restartable file systems. Mem- ones are introduced quite frequently. For example, Linux brane allows an operating system to tolerate a broad has many established file systems, including ext2 [34], class of file system failures and does so while remain- ext3 [35], reiserfs [27], and still there is great interest in ing transparent to running applications; upon failure, the next-generation file systems such as Linux ext4 and btrfs. file system restarts, its state is restored, and pending ap- Thus, file systems are large, complex, and under develop- plication requests are serviced as if no failure had oc- ment, the perfect storm for numerous bugs to arise. curred. Membrane provides transparent recovery through Because of the likely presence of flaws in their imple- a lightweight logging and checkpoint infrastructure, and mentation, it is critical to consider how to recover from includes novel techniques to improve performance and file system crashes as well. Unfortunately, we cannot di- correctness of its fault-anticipation and recovery machin- rectly apply previous work from the device-driver litera- ery. We tested Membrane with ext2, ext3, and VFAT. ture to improving file-system fault recovery. File systems, Through experimentation, we show that Membrane in- unlike device drivers, are extremely stateful, as they man- duces little performance overhead and can tolerate a wide age vast amounts of both in-memory and persistent data; range of file system crashes. -
Developments in GFS2
Developments in GFS2 Andy Price <[email protected]> Software Engineer, GFS2 OSSEU 2018 11 GFS2 recap ● Shared storage cluster filesystem ● High availability clusters ● Uses glocks (“gee-locks”) based on DLM locking ● One journal per node ● Divided into resource groups ● Requires clustered lvm: clvmd/lvmlockd ● Userspace tools: gfs2-utils 22 Not-so-recent developments ● Full SELinux support (linux 4.5) ● Previously no way to tell other nodes that cached labels are invalid ● Workaround was to mount with -o context=<label> ● Relabeling now causes security label invalidation across cluster 33 Not-so-recent developments ● Resource group stripe alignment (gfs2-utils 3.1.11) ● mkfs.gfs2 tries to align resource groups to RAID stripes where possible ● Uses libblkid topology information to find stripe unit and stripe width values ● Tries to spread resource groups evenly across disks 44 Not-so-recent developments ● Resource group LVBs (linux 3.6) ● -o rgrplvb (all nodes) ● LVBs: data attached to DLM lock requests ● Allows resource group metadata to be cached and transferred with glocks ● Decreases resource group-related I/O 55 Not-so-recent developments ● Location-based readdir cookies (linux 4.5) ● -o loccookie (all nodes) ● Uniqueness required by NFS ● Previously 31 bits of filename hash ● Problem: collisions with large directories (100K+ files) ● Cookies now based on the location of the directory entry within the directory 66 Not-so-recent developments ● gfs_controld is gone (linux 3.3) ● dlm now triggers gfs2 journal recovery via callbacks ● -
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 . -
Add-Ons for Red Hat Enterprise Linux
DATASHEET ADD-ONS FOR RED HAT ENTERPRISE LINUX WORKLOAD AND MISSION-CRITICAL PLATFORM ENHANCEMENTS In conjunction with Red Hat® Enterprise Linux®, Red Hat offers a portfolio of Add-Ons to extend the features of your Red Hat Enterprise Linux subscription. Add-Ons to Red Hat Enterprise Linux tailor your application environment to suit your particular computing requirements. With increased flexibility and choice, customers can deploy what they need, when they need it. ADD-ON OPTIONS FOR AVAILABILITY MANAGEMENT High Availability Add-On Red Hat’s High Availability Add-On provides on-demand failover services between nodes within a cluster, making applications highly available. The High Availability Add-On supports up to 16 nodes and may be configured for most applications that use customizable agents, as well as for virtual guests. The High Availability Add-On also includes failover support for off-the-shelf applications like Apache, MySQL, and PostgreSQL. When using the High Availability Add-On, a highly available service can fail over from one node to another with no apparent interruption to cluster clients. The High Availability Add-On also ensures data integrity when one cluster node takes over control of a service from another clus- ter node. It achieves this by promptly evicting nodes from the cluster that are deemed to be faulty using a method called “fencing” that prevents data corruption. www.redhat.com DATASHEET ADD-ONS For RED Hat ENterprise LINUX 6 Resilient Storage Add-On Red Hat’s Resilient Storage Add-On enables a shared storage or clustered file system to access the same storage device over a network. -
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 ............................................... -
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