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Copy on Write Based File Systems Performance Analysis and Implementation
Copy On Write Based File Systems Performance Analysis And Implementation Sakis Kasampalis Kongens Lyngby 2010 IMM-MSC-2010-63 Technical University of Denmark Department Of Informatics Building 321, DK-2800 Kongens Lyngby, Denmark Phone +45 45253351, Fax +45 45882673 [email protected] www.imm.dtu.dk Abstract In this work I am focusing on Copy On Write based file systems. Copy On Write is used on modern file systems for providing (1) metadata and data consistency using transactional semantics, (2) cheap and instant backups using snapshots and clones. This thesis is divided into two main parts. The first part focuses on the design and performance of Copy On Write based file systems. Recent efforts aiming at creating a Copy On Write based file system are ZFS, Btrfs, ext3cow, Hammer, and LLFS. My work focuses only on ZFS and Btrfs, since they support the most advanced features. The main goals of ZFS and Btrfs are to offer a scalable, fault tolerant, and easy to administrate file system. I evaluate the performance and scalability of ZFS and Btrfs. The evaluation includes studying their design and testing their performance and scalability against a set of recommended file system benchmarks. Most computers are already based on multi-core and multiple processor architec- tures. Because of that, the need for using concurrent programming models has increased. Transactions can be very helpful for supporting concurrent program- ming models, which ensure that system updates are consistent. Unfortunately, the majority of operating systems and file systems either do not support trans- actions at all, or they simply do not expose them to the users. -
The Google File System (GFS)
The Google File System (GFS), as described by Ghemwat, Gobioff, and Leung in 2003, provided the architecture for scalable, fault-tolerant data management within the context of Google. These architectural choices, however, resulted in sub-optimal performance in regard to data trustworthiness (security) and simplicity. Additionally, the application- specific nature of GFS limits the general scalability of the system outside of the specific design considerations within the context of Google. SYSTEM DESIGN: The authors enumerate their specific considerations as: (1) commodity components with high expectation of failure, (2) a system optimized to handle relatively large files, particularly multi-GB files, (3) most writes to the system are concurrent append operations, rather than internally modifying the extant files, and (4) a high rate of sustained bandwidth (Section 1, 2.1). Utilizing these considerations, this paper analyzes the success of GFS’s design in achieving a fault-tolerant, scalable system while also considering the faults of the system with regards to data-trustworthiness and simplicity. Data-trustworthiness: In designing the GFS system, the authors made the conscious decision not prioritize data trustworthiness by allowing applications to access ‘stale’, or not up-to-date, data. In particular, although the system does inform applications of the chunk-server version number, the designers of GFS encouraged user applications to cache chunkserver information, allowing stale data accesses (Section 2.7.2, 4.5). Although possibly troubling -
Introspection-Based Verification and Validation
Introspection-Based Verification and Validation Hans P. Zima Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 E-mail: [email protected] Abstract This paper describes an introspection-based approach to fault tolerance that provides support for run- time monitoring and analysis of program execution. Whereas introspection is mainly motivated by the need to deal with transient faults in embedded systems operating in hazardous environments, we show in this paper that it can also be seen as an extension of traditional V&V methods for dealing with program design faults. Introspection—a technology supporting runtime monitoring and analysis—is motivated primarily by dealing with faults caused by hardware malfunction or environmental influences such as radiation and thermal effects [4]. This paper argues that introspection can also be used to handle certain classes of faults caused by program design errors, complementing the classical approaches for dealing with design errors summarized under the term Verification and Validation (V&V). Consider a program, P , over a given input domain, and assume that the intended behavior of P is defined by a formal specification. Verification of P implies a static proof—performed before execution of the program—that for all legal inputs, the result of applying P to a legal input conforms to the specification. Thus, verification is a methodology that seeks to avoid faults. Model checking [5, 3] refers to a special verification technology that uses exploration of the full state space based on a simplified program model. Verification techniques have been highly successful when judiciously applied under the right conditions in well-defined, limited contexts. -
Fault-Tolerant Components on AWS
Fault-Tolerant Components on AWS November 2019 This paper has been archived For the latest technical information, see the AWS Whitepapers & Guides page: Archivedhttps://aws.amazon.com/whitepapers Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Archived Contents Introduction .......................................................................................................................... 1 Failures Shouldn’t Be THAT Interesting ............................................................................. 1 Amazon Elastic Compute Cloud ...................................................................................... 1 Elastic Block Store ........................................................................................................... 3 Auto Scaling .................................................................................................................... -
Disk Array Data Organizations and RAID
Guest Lecture for 15-440 Disk Array Data Organizations and RAID October 2010, Greg Ganger © 1 Plan for today Why have multiple disks? Storage capacity, performance capacity, reliability Load distribution problem and approaches disk striping Fault tolerance replication parity-based protection “RAID” and the Disk Array Matrix Rebuild October 2010, Greg Ganger © 2 Why multi-disk systems? A single storage device may not provide enough storage capacity, performance capacity, reliability So, what is the simplest arrangement? October 2010, Greg Ganger © 3 Just a bunch of disks (JBOD) A0 B0 C0 D0 A1 B1 C1 D1 A2 B2 C2 D2 A3 B3 C3 D3 Yes, it’s a goofy name industry really does sell “JBOD enclosures” October 2010, Greg Ganger © 4 Disk Subsystem Load Balancing I/O requests are almost never evenly distributed Some data is requested more than other data Depends on the apps, usage, time, … October 2010, Greg Ganger © 5 Disk Subsystem Load Balancing I/O requests are almost never evenly distributed Some data is requested more than other data Depends on the apps, usage, time, … What is the right data-to-disk assignment policy? Common approach: Fixed data placement Your data is on disk X, period! For good reasons too: you bought it or you’re paying more … Fancy: Dynamic data placement If some of your files are accessed a lot, the admin (or even system) may separate the “hot” files across multiple disks In this scenario, entire files systems (or even files) are manually moved by the system admin to specific disks October 2010, Greg -
Network Reliability and Fault Tolerance
Network Reliability and Fault Tolerance Muriel Medard´ [email protected] Laboratory for Information and Decision Systems Room 35-212 Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA 02139 Steven S. Lumetta [email protected] Coordinated Science Laboratory University of Illinois Urbana-Champaign 1308 W. Main Street, Urbana, IL 61801 1 Introduction The majority of communications applications, from cellular telephone conversations to credit card transactions, assume the availability of a reliable network. At this level, data are expected to tra- verse the network and to arrive intact at their destination. The physical systems that compose a network, on the other hand, are subjected to a wide range of problems, ranging from signal distor- tion to component failures. Similarly, the software that supports the high-level semantic interface 1 often contains unknown bugs and other latent reliability problems. Redundancy underlies all ap- proaches to fault tolerance. Definitive definitions for all concepts and terms related to reliability, and, more broadly, dependability, can be found in [AAC+92]. Designing any system to tolerate faults first requires the selection of a fault model, a set of possible failure scenarios along with an understanding of the frequency, duration, and impact of each scenario. A simple fault model merely lists the set of faults to be considered; inclusion in the set is decided based on a combination of expected frequency, impact on the system, and feasibility or cost of providing protection. Most reliable network designs address the failure of any single component, and some designs tolerate multiple failures. In contrast, few attempt to handle the adversarial conditions that might occur in a terrorist attack, and cataclysmic events are almost never addressed at any scale larger than a city. -
Fault Tolerance
Distributed Systems Fault Tolerance Paul Krzyzanowski Except as otherwise noted, the content of this presentation is licensed under the Creative Commons Attribution 2.5 License. Page Faults • Deviation from expected behavior • Due to a variety of factors: – Hardware failure – Software bugs – Operator errors – Network errors/outages Page A fault in a system is some deviation from the expected behavior of the system -- a malfunction. Faults may be due to a variety of factors, including hardware, software, operator (user), and network errors. Faults • Three categories – transient faults – intermittent faults – permanent faults • Any fault may be – fail-silent (fail-stop) – Byzantine • synchronous system vs. asynchronous system – E.g., IP packet versus serial port transmission Page Faults can be classified into one of three categories: transient faults: these occur once and then disappear. For example, a network message transmission times out but works fine when attempted a second time. Intermittent faults: these are the most annoying of component faults. This fault is characterized by a fault occurring, then vanishing again, then occurring, … An example of this kind of fault is a loose connection. Permanent faults: this fault is persistent: it continues to exist until the faulty component is repaired or replaced. Examples of this fault are disk head crashes, software bugs, and burnt-out hardware. Any of these faults may be either a fail-silent failure (also known as fail-stop) or a Byzantine failure. A fail-silent fault is one where the faulty unit stops functioning and produces no ill output (it produces no output or produces output to indicate failure). A Byzantine fault is one where the faulty unit continues to run but produces incorrect results. -
Summer Student Project Report
Summer Student Project Report Dimitris Kalimeris National and Kapodistrian University of Athens June { September 2014 Abstract This report will outline two projects that were done as part of a three months long summer internship at CERN. In the first project we dealt with Worldwide LHC Computing Grid (WLCG) and its information sys- tem. The information system currently conforms to a schema called GLUE and it is evolving towards a new version: GLUE2. The aim of the project was to develop and adapt the current information system of the WLCG, used by the Large Scale Storage Systems at CERN (CASTOR and EOS), to the new GLUE2 schema. During the second project we investigated different RAID configurations so that we can get performance boost from CERN's disk systems in the future. RAID 1 that is currently in use is not an option anymore because of limited performance and high cost. We tried to discover RAID configurations that will improve the performance and simultaneously decrease the cost. 1 Information-provider scripts for GLUE2 1.1 Introduction The Worldwide LHC Computing Grid (WLCG, see also 1) is an international collaboration consisting of a grid-based computer network infrastructure incor- porating over 170 computing centres in 36 countries. It was originally designed by CERN to handle the large data volume produced by the Large Hadron Col- lider (LHC) experiments. This data is stored at CERN Storage Systems which are responsible for keeping and making available more than 100 Petabytes (105 Terabytes) of data to the physics community. The data is also replicated from CERN to the main computing centres within the WLCG. -
RAID Configuration Guide Motherboard E14794 Revised Edition V4 August 2018
RAID Configuration Guide Motherboard E14794 Revised Edition V4 August 2018 Copyright © 2018 ASUSTeK COMPUTER INC. All Rights Reserved. No part of this manual, including the products and software described in it, may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into any language in any form or by any means, except documentation kept by the purchaser for backup purposes, without the express written permission of ASUSTeK COMPUTER INC. (“ASUS”). Product warranty or service will not be extended if: (1) the product is repaired, modified or altered, unless such repair, modification of alteration is authorized in writing by ASUS; or (2) the serial number of the product is defaced or missing. ASUS PROVIDES THIS MANUAL “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OR CONDITIONS OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. IN NO EVENT SHALL ASUS, ITS DIRECTORS, OFFICERS, EMPLOYEES OR AGENTS BE LIABLE FOR ANY INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, LOSS OF BUSINESS, LOSS OF USE OR DATA, INTERRUPTION OF BUSINESS AND THE LIKE), EVEN IF ASUS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES ARISING FROM ANY DEFECT OR ERROR IN THIS MANUAL OR PRODUCT. SPECIFICATIONS AND INFORMATION CONTAINED IN THIS MANUAL ARE FURNISHED FOR INFORMATIONAL USE ONLY, AND ARE SUBJECT TO CHANGE AT ANY TIME WITHOUT NOTICE, AND SHOULD NOT BE CONSTRUED AS A COMMITMENT BY ASUS. ASUS ASSUMES NO RESPONSIBILITY OR LIABILITY FOR ANY ERRORS OR INACCURACIES THAT MAY APPEAR IN THIS MANUAL, INCLUDING THE PRODUCTS AND SOFTWARE DESCRIBED IN IT. -
Detecting and Tolerating Byzantine Faults in Database Systems Benjamin Mead Vandiver
Computer Science and Artificial Intelligence Laboratory Technical Report MIT-CSAIL-TR-2008-040 June 30, 2008 Detecting and Tolerating Byzantine Faults in Database Systems Benjamin Mead Vandiver massachusetts institute of technology, cambridge, ma 02139 usa — www.csail.mit.edu Detecting and Tolerating Byzantine Faults in Database Systems by Benjamin Mead Vandiver Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2008 c Massachusetts Institute of Technology 2008. All rights reserved. Author.............................................................. Department of Electrical Engineering and Computer Science May 23, 2008 Certified by.......................................................... Barbara Liskov Ford Professor of Engineering Thesis Supervisor Accepted by......................................................... Terry P. Orlando Chairman, Department Committee on Graduate Students 2 Detecting and Tolerating Byzantine Faults in Database Systems by Benjamin Mead Vandiver Submitted to the Department of Electrical Engineering and Computer Science on May 23, 2008, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science Abstract This thesis describes the design, implementation, and evaluation of a replication scheme to handle Byzantine faults in transaction processing database systems. The scheme compares answers from queries and updates on multiple replicas which are off-the-shelf database systems, to provide a single database that is Byzantine fault tolerant. The scheme works when the replicas are homogeneous, but it also allows heterogeneous replication in which replicas come from different vendors. Heteroge- neous replicas reduce the impact of bugs and security compromises because they are implemented independently and are thus less likely to suffer correlated failures. -
Fault Tolerance Techniques for Scalable Computing∗
Fault Tolerance Techniques for Scalable Computing∗ Pavan Balaji, Darius Buntinas, and Dries Kimpe Mathematics and Computer Science Division Argonne National Laboratory fbalaji, buntinas, [email protected] Abstract The largest systems in the world today already scale to hundreds of thousands of cores. With plans under way for exascale systems to emerge within the next decade, we will soon have systems comprising more than a million processing elements. As researchers work toward architecting these enormous systems, it is becoming increas- ingly clear that, at such scales, resilience to hardware faults is going to be a prominent issue that needs to be addressed. This chapter discusses techniques being used for fault tolerance on such systems, including checkpoint-restart techniques (system-level and application-level; complete, partial, and hybrid checkpoints), application-based fault- tolerance techniques, and hardware features for resilience. 1 Introduction and Trends in Large-Scale Computing Sys- tems The largest systems in the world already use close to a million cores. With upcoming systems expected to use tens to hundreds of millions of cores, and exascale systems going up to a billion cores, the number of hardware components these systems would comprise would be staggering. Unfortunately, the reliability of each hardware component is not improving at ∗This work was supported by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract DE-AC02-06CH11357. 1 the same rate as the number of components in the system is growing. Consequently, faults are increasingly becoming common. For the largest supercomputers that will be available over the next decade, faults will become a norm rather than an exception. -
Data Storage and High-Speed Streaming
FYS3240 PC-based instrumentation and microcontrollers Data storage and high-speed streaming Spring 2013 – Lecture #8 Bekkeng, 8.1.2013 Data streaming • Data written to or read from a hard drive at a sustained rate is often referred to as streaming • Trends in data storage – Ever-increasing amounts of data – Record “everything” and play it back later – Hard drives: faster, bigger, and cheaper – Solid state drives – RAID hardware – PCI Express • PCI Express provides higher, dedicated bandwidth Overview • Hard drive performance and alternatives • File types • RAID • DAQ software design for high-speed acquisition and storage Streaming Data with the PCI Express Bus • A PCI Express device receives dedicated bandwidth (250 MB/s or more). • Data is transferred from onboard device memory (typically less than 512 MB), across a dedicated PCI Express link, across the I/O bus, and into system memory (RAM; 3 GB or more possible). It can then be transferred from system memory, across the I/O bus, onto hard drives (TB´s of data). The CPU/DMA-controller is responsible for managing this process. • Peer-to-peer data streaming is also possible between two PCI Express devices. PXI: Streaming to/from Hard Disk Drives RAM – Random Access Memory • SRAM – Static RAM: Each bit stored in a flip-flop • DRAM – Dynamic RAM: Each bit stored in a capacitor (transistor). Has to be refreshed (e.g. each 15 ms) – EDO DRAM – Extended Data Out DRAM. Data available while next bit is being set up – Dual-Ported DRAM (VRAM – Video RAM). Two locations can be accessed at the same time – SDRAM – Synchronous DRAM.