1 Fault Tolerance & Reliability CDA 5140 Chapter 3 – RAID & Sample
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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 .................................................................................................................... -
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. -
Multiple Instruction Issue in the Nonstop Cyclone System
~TANDEM Multiple Instruction Issue in the NonStop Cyclone System Robert W. Horst Richard L. Harris Robert L. Jardine Technical Report 90.6 June 1990 Part Number: 48007 Multiple Instruction Issue in the NonStop Cyclone Processorl Robert W. Horst Richard L. Harris Robert L. Jardine Tandem Computers Incorporated 19333 Vallco Parkway Cupertino, CA 95014 Abstract This paper describes the architecture for issuing multiple instructions per clock in the NonStop Cyclone Processor. Pairs of instructions are fetched and decoded by a dual two-stage prefetch pipeline and passed to a dual six-stage pipeline for execution. Dynamic branch prediction is used to reduce branch penalties. A unique microcode routine for each pair is stored in the large duplexed control store. The microcode controls parallel data paths optimized for executing the most frequent instruction pairs. Other features of the architecture include cache support for unaligned double precision accesses, a virtually-addressed main memory, and a novel precise exception mechanism. lA previous version of this paper was published in the conference proceedings of The 17th Annual International Symposium on Computer Architecture, May 28-31, 1990, Seattle, Washington. Dynabus+ Dynabus X Dvnabus Y IIIIII I 20 MBIS Parallel I I II 100 MbiVS III I Serial Fibers CPU CPU CPU CPU 0 3 14 15 MEMORY ••• MEMORY • •• MEMORY MEMORY ~IIIO PROC110 IIPROC1,0 PROC1,0 ROC PROC110 IIPROC1,0 PROC110 F11IOROC o 1 o 1 o 1 o 1 I DISKCTRL ~ DISKCTRL I I Q~ / \. I DISKCTRL I TAPECTRL : : DISKCTRL : I 0 1 2 3 /\ o 1 2 3 0 1 2 3 0 1 2 3 Section 0 Section 3 Figure 1. -
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. -
Fault Tolerance in Tandem Computer Systems
1'TANDEM Fault Tolerance in Tandem Computer Systems Joel Bartlett * Wendy Bartlett Richard Carr Dave Garcia Jim Gray Robert Horst Robert Jardine Dan Lenoski DixMcGuire • Preselll address: Digital Equipmelll CorporQlioll Western Regional Laboralory. Palo Alto. California Technical Report 90.5 May 1990 Part Number: 40666 ~ TANDEM COMPUTERS Fault Tolerance in Tandem Computer Systems Joel Bartlett* Wendy Bartlett Richard Carr Dave Garcia Jim Gray Robert Horst Robert Jardine Dan Lenoski Dix McGuire * Present address: Digital Equipment Corporation Western Regional Laboratory, Palo Alto, California Technical Report 90.5 May 1990 Part Nurnber: 40666 Fault Tolerance in Tandem Computer Systems! Wendy Bartlett, Richard Carr, Dave Garcia, Jim Gray, Robert Horst, Robert Jardine, Dan Lenoski, Dix McGuire Tandem Computers Incorporated Cupertino, California Joel Bartlett Digital Equipment Corporation, Western Regional Laboratory Palo Alto, California Tandem Technical Report 90.5, Tandem Part Number 40666 March 1990 ABSTRACT Tandem produces high-availability, general-purpose computers that provide fault tolerance through fail fast hardware modules and fault-tolerant software2. This chapter presents a historical perspective of the Tandem systems' evolution and provides a synopsis of the company's current approach to implementing these systems. The article does not cover products announced since January 1990. At the hardware level, a Tandem system is a loosely-coupled multiprocessor with fail-fast modules connected with dual paths. A system can include a range of processors, interconnected through a hierarchical fault-tolerant local network. A system can also include a variety of peripherals, attached with dual-ported controllers. A novel disk subsystem allows a choice between low cost-per-byte and low cost-per-access. -
RAID Technology
RAID Technology Reference and Sources: y The most part of text in this guide has been taken from copyrighted document of Adaptec, Inc. on site (www.adaptec.com) y Perceptive Solutions, Inc. RAID stands for Redundant Array of Inexpensive (or sometimes "Independent") Disks. RAID is a method of combining several hard disk drives into one logical unit (two or more disks grouped together to appear as a single device to the host system). RAID technology was developed to address the fault-tolerance and performance limitations of conventional disk storage. It can offer fault tolerance and higher throughput levels than a single hard drive or group of independent hard drives. While arrays were once considered complex and relatively specialized storage solutions, today they are easy to use and essential for a broad spectrum of client/server applications. Redundant Arrays of Inexpensive Disks (RAID) "KILLS - BUGS - DEAD!" -- TV commercial for RAID bug spray There are many applications, particularly in a business environment, where there are needs beyond what can be fulfilled by a single hard disk, regardless of its size, performance or quality level. Many businesses can't afford to have their systems go down for even an hour in the event of a disk failure; they need large storage subsystems with capacities in the terabytes; and they want to be able to insulate themselves from hardware failures to any extent possible. Some people working with multimedia files need fast data transfer exceeding what current drives can deliver, without spending a fortune on specialty drives. These situations require that the traditional "one hard disk per system" model be set aside and a new system employed. -
Memory Systems : Cache, DRAM, Disk
CHAPTER 24 Storage Subsystems Up to this point, the discussions in Part III of this with how multiple drives within a subsystem can be book have been on the disk drive as an individual organized together, cooperatively, for better reliabil- storage device and how it is directly connected to a ity and performance. This is discussed in Sections host system. This direct attach storage (DAS) para- 24.1–24.3. A second aspect deals with how a storage digm dates back to the early days of mainframe subsystem is connected to its clients and accessed. computing, when disk drives were located close to Some form of networking is usually involved. This is the CPU and cabled directly to the computer system discussed in Sections 24.4–24.6. A storage subsystem via some control circuits. This simple model of disk can be designed to have any organization and use any drive usage and confi guration remained unchanged of the connection methods discussed in this chapter. through the introduction of, fi rst, the mini computers Organization details are usually made transparent to and then the personal computers. Indeed, even today user applications by the storage subsystem presenting the majority of disk drives shipped in the industry are one or more virtual disk images, which logically look targeted for systems having such a confi guration. like disk drives to the users. This is easy to do because However, this simplistic view of the relationship logically a disk is no more than a drive ID and a logical between the disk drive and the host system does not address space associated with it.