Dataarkitektur og logisk design

While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. In particular, the book:Provides comprehensive coverage of the application of machine learning to the domain of indoor localization;Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization;Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.

This book describes the first comprehensive approach to the optimization of interconnect architectures in 3D systems on chips (SoCs), specially addressing the challenges and opportunities arising from heterogeneous integration. Readers learn about the physical implications of using heterogeneous 3D technologies for SoC integration, while also learning to maximize the 3D-technology gains, through a physical-effect-aware architecture design. The book provides a deep theoretical background covering all abstraction-levels needed to research and architect tomorrow's 3D-integrated circuits, an extensive set of optimization methods (for power, performance, area, and yield), as well as an open-source optimization and simulation framework for fast exploration of novel designs.

Go beyond the basics of Kubernetes and explore more advanced concepts, including Kubernetes in production, governance, serverless computing, and service meshes.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesMaster Kubernetes architecture and design to build, deploy, and secure large-scale distributed systemsLearn advanced concepts like autoscaling, multi-cluster management, serverless computing, service meshes and policy enginesExplore Kubernetes 1.25 and its rich ecosystem of tools like Kubectl, Krew, K9s, Lens, and HelmBook DescriptionThe fourth edition of the bestseller Mastering Kubernetes includes the most recent tools and code to enable you to learn the latest features of Kubernetes 1.25. This book contains a thorough exploration of complex concepts and best practices to help you master the skills of designing and deploying large-scale distributed systems on Kubernetes clusters.You'll learn how to run complex stateless and stateful microservices on Kubernetes, including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backends. In addition, you'll understand how to utilize serverless computing and service meshes.Further, two new chapters have been added. "Governing Kubernetes" covers the problem of policy management, how admission control addresses it, and how policy engines provide a powerful governance solution. "Running Kubernetes in Production" shows you what it takes to run Kubernetes at scale across multiple cloud providers, multiple geographical regions, and multiple clusters, and it also explains how to handle topics such as upgrades, capacity planning, dealing with cloud provider limits/quotas, and cost management.By the end of this Kubernetes book, you'll have a strong understanding of, and hands-on experience with, a wide range of Kubernetes capabilities.What you will learnLearn how to govern Kubernetes using policy enginesLearn what it takes to run Kubernetes in production and at scaleBuild and run stateful applications and complex microservicesMaster Kubernetes networking with services, Ingress objects, load balancers, and service meshesAchieve high availability for your Kubernetes clustersImprove Kubernetes observability with tools such as Prometheus, Grafana, and JaegerExtend Kubernetes with the Kubernetes API, plugins, and webhooksWho this book is forIf you're a system administrator or cloud developer who wants to become comfortable with Kubernetes and would like to master its advanced features, then this book is for you. Software and DevOps engineers with a working knowledge of Kubernetes, as well as technical managers of Kubernetes-based systems, will also find this book useful. Those deciding on whether to migrate to Kubernetes and are curious about its inner workings will find plenty of answers here as well. Basic familiarity with networking concepts will prove beneficial.Table of ContentsUnderstanding Kubernetes ArchitectureCreating Kubernetes ClustersHigh Availability and ReliabilitySecuring KubernetesUsing Kubernetes Resources in PracticeManaging StorageRunning Stateful Applications with KubernetesDeploying and Updating ApplicationsPackaging ApplicationsExploring Kubernetes NetworkingRunning Kubernetes on Multiple ClustersServerless Computing on KubernetesMonitoring Kubernetes ClustersUtilizing Service MeshesExtending KubernetesGoverning KubernetesRunning Kubernetes in ProductionThe Future of Kubernetes

In der Informatik sind einige Fachleute der Meinung, dass das Mainframe Computing seine bedeutende Zeit berschritten hat. In den letzten 10 Jahren ist die Mainframe-Ausbildung weiter eingeschränkt worden, d.h., Vorlesungen für interessierte Studenten an deutschen Universitäten und Fachhochschulen existieren teilweise nur noch online im E-Learning. Das betrifft auch die dazugehörigen praktischen Übungen. Diese Entwicklung widerspricht insgesamt dem Bedarf von Großrechner-Fachpersonal in mittleren und großen Unternehmen. In diesem Bereich ist das Problem dadurch gewachsen, dass Mitarbeiter in den Ruhestand gewechselt sind und nicht mehr zur Verfügung stehen. In dem Buch Einführung in z/OS und OS/390 wurde bereits zu diesem Thema Stellung bezogen. Der Ist-Zustand von Mainframe-Fachpersonal auf dem IT-Arbeitsmarkt ist weiter geschrumpft. Die Universität Leipzig hat zusammen mit dem Institut für Informatik der Universitt Tübingen in der Vergangenheit versucht, diesen Prozess aufzuhalten. Es wurden Vorlesungen in Leipzig, Tübingen, Frankfurt (Frankfurt School of Finance & Management) zum Thema Mainframe Server gehalten in der Hoffnung, Studenten und Interessenten in das Mainframe Computing einzuführen. Das vorliegende Lehrbuch Mainframe System z Computing enthält wesentliche Änderungen gegenüber der 1. Auflage des Buches Einführung in z/OS und OS/390. Dazu zählen vordergründig Kapitel zur Hardware aktueller IBM-System-z-Server (z12, z15, z16). Es gibt insbesondere praktische Erkenntnisse und Erfahrungen zu den Themen DB2, IMS, CICS, WebSphere MQ, SMF und z/OS Connect EE. Das betrifft z.T. Nutzer mit normalen und auch solche mit Administrator-Rechten.

All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach

Unlock your organizational potential and scale your business using observability with this information packed guidePurchase of the print or Kindle book includes a free PDF eBookKey Features:Learn the principles of identifying stakeholders, tools, and processes necessary for implementing observabilityDevelop strategies to self-sustain the observability journey in the long runLearn with real-life case studies to set up observability for your enterpriseBook Description:Observability can be implemented in multiple ways within an organization based on the organization's needs. So, it's crucial for organizations to decide whether they need observability and to what extent, what skills and tools will suit them, and how long will it take to implement it. Implementing Enterprise Observability for Success provides a step-by-step approach to help you create an observability strategy, understand the principles behind the creation of the strategy, and logical steps to plan and execute the implementation.You'll learn about observability fundamentals and challenges, the importance of data and analytics along with different tools. Further, you'll discover the various layers from which data should be collected for setting up observability.Through real- life examples distilled from the author's experience in implementing observability at an enterprise level, you'll uncover some of the non-technical & technical drivers of observability like the culture of the organization, the hierarchy of stakeholders, tools at disposal and the willingness to invest.By the end of this book, you'll be well-equipped to plan the observability journey, identify different stakeholders, spot the technology stack required, and lay out an effective plan for organization-wide adoption.What You Will Learn:Understand observability fundamentals from scratchGet to grips with common challenges in implementing observabilitySegregate organizations according to their size for implementing observabilityLeverage analytics to better understand the value of collected dataExplore best practices from case studies to smoothen your observability journeyUnderstand the Observability Maturity Model and the essential people skills necessary to attain each maturity levelWho this book is for:This book is for technology leaders, architects, and initiative leads looking to enhance monitoring and/or implementing observability. Engineers, developers, and professionals already working on monitoring and analytics or are responsible for scaling the observability implementation across multiple teams or at an organizational level can also benefit from this book. A deep understanding of monitoring concepts, general knowledge of IT systems and processes along with familiarity with working across various stakeholders is required.

The long-awaited x64 edition of the bestselling introduction to Intel assembly languageIn the newly revised fourth edition of x64 Assembly Language Step-by-Step: Programming with Linux, author Jeff Duntemann delivers an extensively rewritten introduction to assembly language with a strong focus on 64-bit long-mode Linux assembler. The book offers a lighthearted, robust, and accessible approach to a challenging technical discipline, giving you a step-by-step path to learning assembly code that's engaging and easy to read.x64 Assembly Language Step-by-Step makes quick work of programmable computing basics, the concepts of binary and hexadecimal number systems, the Intel x86/x64 computer architecture, and the process of Linux software development to dive deep into the x64 instruction set, memory addressing, procedures, macros, and interface to the C-language code libraries on which Linux is built.You'll also find:* A set of free and open-source development and debugging tools you can download and put to use immediately* Numerous examples woven throughout the book to illustrate the practical implementation of the ideas discussed within* Practical tips on software design, coding, testing, and debuggingA one-stop resource for aspiring and practicing Intel assembly programmers, the latest edition of this celebrated text provides readers with an authoritative tutorial approach to x64 technology that's ideal for self-paced instruction.

Heutige Computer stellen technische Meisterwerke dar. Doch wie ist es eigentlich möglich, dass ein Stück Silizium zusammen mit ein bisschen Strom ganz alleine Berechnungen ausführt und logische Entscheidungen trifft? Die Antwort auf diese Frage führt uns auf eine faszinierende Reise zu den Grundlagen der binären Arithmetik, Booleschen Algebra und Halbleitertechnologie. Im zweiten Teil spielen wir Lego für Fortgeschrittene und bauen aus einfachen Transistoren nach und nach immer komplexere Schaltungen und schließlich ganze Computer zusammen. Dabei konzentrieren wir uns auf das Wesentliche, setzen nichts voraus und lassen überflüssige Details weg. Ein knapper Ausblick auf das Quantencomputing beschließt diese kurze Geschichte der Technischen Informatik.

Dieses Buch beschreibt eine Reihe von SystemC-basierten Analysemethoden für virtuelle Prototypen, einschließlich Entwurfsverständnis, Verifikation, Sicherheitsvalidierung und Entwurfsraumuntersuchung. Der Leser erhält einen Überblick über die neuesten Forschungsergebnisse auf dem Gebiet der Electronic Design Automation (EDA) auf der elektronischen Systemebene (ESL). Die besprochenen Methoden ermöglichen es den Lesern, wichtige Aufgaben und Anwendungen im Entwurfsprozess leicht zu bewältigen.Übersetzt mit www.DeepL.com/Translator (kostenlose Version)

Dieses Buch bietet dem Leser einen einfachen Einstieg in das Quantencomputing sowie in den Entwurf entsprechender Geräte. Die Autoren behandeln verschiedene Entwurfsaufgaben, die für das Quantencomputing wichtig sind, und stellen entsprechende Lösungen vor. Eine Besonderheit des Buches ist, dass diese Aufgaben und Lösungen explizit aus der Perspektive der Entwurfsautomatisierung diskutiert werden, d.h. unter Verwendung von cleveren Algorithmen und Datenstrukturen, die von der Entwurfsautomatisierungs-Community für konventionelle Logik (d.h. für elektronische Geräte und Systeme) entwickelt worden sind und nun für diese neue Technologie angewendet werden. Auf diese Weise können relevante Entwurfsaufgaben wesentlich effizienter als bisher durchgeführt werden, was zu Verbesserungen um mehrere Größenordnungen führt (in Bezug auf die Laufzeit und andere Entwurfsziele). Beschreibt den aktuellen Stand der Technik für den Entwurf von Quantenschaltungen, für deren Simulation und für deren Abbildung auf reale Hardware; Bietet eine erste umfassende Einführung in die Entwurfsautomatisierung für Quantencomputer, die sich mit praxisrelevanten Aufgabenstellungen befasst; Es richtet sich sowohl an die Quantencomputer-Gemeinschaft als auch an die Gemeinschaft der Entwurfsautomatisierung und zeigt, welche beeindruckenden Verbesserungen möglich sind, wenn man das Wissen beider Gemeinschaften kombiniert.

Dieses Buch beschreibt eine neue, grobkörnige rekonfigurierbare Architektur (CGRA), genannt Blocks, und stellt sie in den Kontext von Computerarchitekturen und insbesondere von anderen CGRAs. Das Buch beginnt mit einer ausführlichen Bewertung historischer und bestehender CGRAs und deren Stärken und Schwächen. Dies führt auch zu einem besseren Verständnis und einer neuen Definition dessen, was CGRAs von anderen Architekturansätzen unterscheidet.Die Autoren stellen Blocks als einzigartig vor, da es über separate programmierbare Steuer- und Datenpfade verfügt, so dass leichtgewichtige Befehlsdekodiereinheiten über eine statisch konfigurierte Verbindung beliebig mit einer oder mehreren Funktionseinheiten (FUs) verbunden werden können. In der Diskussion wird erläutert, wie Architekturen modelliert werden können, was zu einem Flächen- und Energiemodell für Blöcke führt. Die Genauigkeit dieses Modells wird anhand vollständig implementierter Architekturen bewertet, wobei sich zeigt, dass die Fehlerspanne sehr akzeptabel ist, obwohl es um drei Größenordnungen schneller ist als die Synthese. Das Buch schließt mit einer Fallstudie zu einem echten System-on-Chip, einschließlich einer RISC-Architektur, der Blocks CGRA und Peripheriegeräten.

OpenTelemetry is a revolution in observability data. Instead of running multiple uncoordinated pipelines, OpenTelemetry provides users with a single integrated stream of data, providing multiple sources of high-quality telemetry data: tracing, metrics, logs, RUM, eBPF, and more. This practical guide shows you how to set up, operate, and troubleshoot the OpenTelemetry observability system. Authors Austin Parker, head of developer relations at Lightstep and OpenTelemetry Community Maintainer, and Ted Young, cofounder of the OpenTelemetry project, cover every OpenTelemetry component, as well as observability best practices for many popular cloud, platform, and data services such as Kubernetes and AWS Lambda. You'll learn how OpenTelemetry enables OSS libraries and services to provide their own native instrumentation--a first in the industry. Ideal for application developers, OSS maintainers, operators and infrastructure teams, and managers and team leaders, this book guides you through: The principles of modern observability All OpenTelemetry components--and how they fit together A practical approach to instrumenting platforms and applications Methods for installing, operating, and troubleshooting an OpenTelemetry-based observability solution Ways to roll out and maintain end-to-end observability across a large organization How to write and maintain consistent, high-quality instrumentation without a lot of work.

This book will teach you everything about the powerful techniques and applications of parallel computing, from the basics of parallel programming to the cutting-edge innovations shaping the future of computing. The book starts with an introduction to parallel programming and the different types of parallelism, including parallel programming with threads and processes. The book then delves into asynchronous programming, distributed Python, and GPU programming with Python, providing you with the tools you need to optimize your programs for distributed and high-performance computing. The book also covers a wide range of applications for parallel computing, including data science, artificial intelligence, and other complex scientific simulations. You will learn about the challenges and opportunities presented by parallel computing for these applications and how to overcome them. By the end of the book, you will have insights into the future of parallel computing, the latest research and developments in the field, and explore the exciting possibilities that lie ahead.TABLE OF CONTENTS 1. Introduction to Parallel Programming 2. Building Multithreaded Programs 3. Working with Multiprocessing and mpi4py Library4. Asynchronous Programming with AsyncIO5. Realizing Parallelism with Distributed Systems 6. Maximizing Performance with GPU Programming using CUDA7. Embracing the Parallel Computing Revolution8. Scaling Your Data Science Applications with Dask9. Exploring the Potential of AI with Parallel Computing10. Hands-on Applications of Parallel Computing

"Covers the inner workings of the computer, from the transistor level up to complete CPUs, memory, and beyond, as well as more recent developments, like parallel and quantum architectures. Each chapter includes hands-on exercises and additional resources for exploring topics further"--

The refereed proceedings of the 15th International Conference on Computer Aided Verification, CAV 2003, held in Boulder, CO, USA in July 2003.The 32 revised full papers and 9 tool papers presented were carefully reviewed and selected from a total of 102 submissions. The papers are organized in topical sections on bounded model checking; symbolic model checking; games, trees, and counters; tools; abstraction; dense time; infinite state systems; applications; theorem proving; automata-based verification; invariants; and explicit model checking.

With industrial systems becoming ever more mechanized and reliant on advanced technology, the complexity of equipment, especially in risky industries is increasing on a daily basis. This title offers the knowledge required by safety professionals to provide and maintain the safety of engineering complex systems.

Volume 2 speaks about Smart cities that emanate from a smart renewable energy aided power grid. The smart grid technologies offer an array of benefits like reliability, availability, and resiliency. Smart grids phenomenally contribute to facilitate cities reach those sustainability goals over time.

This book provides an integrative overview of Internet-of-Things (IoT) and Cloud Computing to be used for the various futuristic and intelligent applications. It includes a broad and integrated perspective on the collaboration, security, growth of cloud infrastructure and real-time data monitoring.

This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.

This is the first book to focus on the problem of ensuring the correctness of floating-point hardware designs through mathematical methods. Formal Verification of Floating-Point Hardware Design, Second Edition advances a verification methodology based on a unified theory of register-transfer logic and floating-point arithmetic that has been developed and applied to the formal verification of commercial floating-point units over the course of more than two decades, during which the author was employed by several major microprocessor design companies. The theory is extended to the analysis of several algorithms and optimization techniques that are commonly used in commercial implementations of elementary arithmetic operations. As a basis for the formal verification of such implementations, high-level specifications of the basic arithmetic instructions of several major industry-standard floating-point architectures are presented, including all details pertaining to the handling of exceptional conditions. The methodology is illustrated in the comprehensive verification of a variety of state-of-the-art commercial floating-point designs developed by Arm Holdings.This revised edition reflects the evolving microarchitectures and increasing sophistication of Arm processors, and the variation in the design goals of execution speed, hardware area requirements, and power consumption. Many new results have been added to Parts I-III (Register-Transfer Logic, Floating-Point Arithmetic, and Implementation of Elementary Operations), extending the theory and describing new techniques. These were derived as required in the verification of the new RTL designs described in Part V.

This book presents various novel architectures for FPGA-optimized accurate and approximate operators, their detailed accuracy and performance analysis, various techniques to model the behavior of approximate operators, and thorough application-level analysis to evaluate the impact of approximations on the final output quality and performance metrics. As multiplication is one of the most commonly used and computationally expensive operations in various error-resilient applications such as digital signal and image processing and machine learning algorithms, this book particularly focuses on this operation. The book starts by elaborating on the various sources of error resilience and opportunities available for approximations on various layers of the computation stack. It then provides a detailed description of the state-of-the-art approximate computing-related works and highlights their limitations.

This book discusses the two fundamental elements that underline the science and design of artificial intelligence (AI) systems: the learning and acquisition of knowledge from observational data, and the reasoning of that knowledge together with whatever information is available about the application at hand. It then presents a mathematical treatment of the core issues that arise when unifying first-order logic and probability, especially in the presence of dynamics, including physical actions, sensing actions and their effects. A model for expressing causal laws describing dynamics is also considered, along with computational ideas for reasoning with such laws over probabilistic logical knowledge.

In Cloud Observability in Action, you will learn how to set up an observability system that learns from a cloud application's signals, logging, and monitoring using free and open-source tools. You will go beyond the traditional monitoring and build observability systems that turn application telemetry into actionable insight.

The book introduces basic programming of ARM Cortex-M cores in assembly and C at the register level, and the fundamentals of embedded system design. It presents basic concepts such as data representations (integer, fixed-point, floating-point), assembly instructions, stack, and implementing basic controls and functions of C language at the assembly level. It covers advanced topics such as interrupts, mixing C and assembly, direct memory access (DMA), system timers (SysTick), multi-tasking, SIMD instructions for digital signal processing (DSP), and instruction encoding/decoding. The book also gives detailed examples of interfacing peripherals, such as general purpose I/O (GPIO), LCD driver, keypad interaction, stepper motor control, PWM output, timer input capture, DAC, ADC, real-time clock (RTC), and serial communication (USART, I2C, SPI, and USB).

This book constitutes the refereed conference proceedings of the workshops held at the 37th International ISC High Performance 2022 Conference, in Hamburg, Germany, in June 2, 2022.The 27 full papers were included in this book were carefully reviewed and selected from 43 submissions. ISC High Performance 2022 presents the following workshops:Compiler-assisted Correctness Checking and Performance Optimization for HPCHPC on Heterogeneous Hardware (H3)Malleability Techniques Applications in High Performance ComputingFifth Workshop on Interactive High Performance Computing3rd ISC HPC International Workshop on Monitoring & Operational Data Analytics6th International Workshop on In Situ Visualization17th Workshop on Virtualization in High Performance Cloud ComputingChapter ¿Compiler-Assisted Instrumentation Selection for Large-Scale C++ Codes¿ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.