Distributed systems / Distributed computing Books

Book SynopsisAbout the Author: Adam Freeman is an experienced IT professional who started his career as a programmer. He has held senior positions in a range of companies, most recently serving as Chief Technology Officer and Chief Operating Officer of a global bank. He has written 49 programming books, focusing mostly on web application development. This industry-leading guide to ASP.NET Core teaches you everything they need to know to create easy, extensible, and cloud-native web applications. For .NET web developers looking to learn about ASP.NET s key tools and techniques.

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Book SynopsisWhat happens when events and data streams help you discover new activity sources to enhance existing businesses or drive new markets? James Urquhart guides enterprise architects, software developers, and product managers through the process.

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Book SynopsisThis is the book for Gophers who want to learn how to build distributed systems. You know the basics of Go and are eager to put your knowledge to work. Build distributed services that are highly available, resilient, and scalable. This book is just what you need to apply Go to real-world situations. Level up your engineering skills today. Take your Go skills to the next level by learning how to design, develop, and deploy a distributed service. Start from the bare essentials of storage handling, then work your way through networking a client and server, and finally to distributing server instances, deployment, and testing. All this will make coding in your day job or side projects easier, faster, and more fun. Create your own distributed services and contribute to open source projects. Build networked, secure clients and servers with gRPC. Gain insights into your systems and debug issues with observable services instrumented with metrics, logs, and traces. Operate your own Certificate Authority to authenticate internal web services with TLS. Automatically handle when nodes are added or removed to your cluster with service discovery. Coordinate distributed systems with replicated state machines powered by the Raft consensus algorithm. Lay out your applications and libraries to be modular and easy to maintain. Write CLIs to configure and run your applications. Run your distributed system locally and deploy to the cloud with Kubernetes. Test and benchmark your applications to ensure they're correct and fast. Dive into writing Go and join the hundreds of thousands who are using it to build software for the real world. What You Need: Go 1.13+ and Kubernetes 1.16+

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Book SynopsisTrade Review'Does an excellent job at delivering CI/CD theory without the burden of a specific technology/tool.' Tony Sweets 'This book is a must read for everyone doing CI/CD' Tobias Getrost 'A great book for anyone embracing DevOps (potentially everyone!) and will make better DevOps practitioners of all readers, no matter their experience.'Sergio Fernandez Gonzalez 'The perfect book to understand the basic concepts of CI/CD for beginners.'Andrea C. Granata 'It's like having a friend taking the time to fully explain terms, including anticipating where there may be confusion or questions and addressing them.'Brent HonadelTable of ContentsBuild and use systems that safely automate software delivery from testing through release with this jargon-busting guide to Continuous Delivery pipelines. In Grokking Continuous Delivery you will learn how to: Design effective CD pipelines for new and legacy projects Keep your software projects release-ready Maintain effective tests Scale CD across multiple applications Ensure pipelines give the right signals at the right time Use version control as the source of truth Safely automate deployments with metrics Describe CD in a way that makes sense to your colleagues Grokking Continuous Delivery teaches you the design and purpose of continuous delivery systems that you can use with any language or stack. You'll learn directly from your mentor Christie Wilson, Google engineer and co-creator of the Tekton CI/CD framework. Using crystal-clear, well-illustrated examples, Christie lays out the practical nuts and bolts of continuous delivery for developers and pipeline designers. about the technology If you're using version control or automating your tests, you're already on the path to continuous delivery! This collection of development practices and automation tools that make it possible to safely deploy on every change to your codebase is the backbone of modern software development. You'll benefit from faster feature development, easier maintenance, and freedom from time-consuming manual tasks. about the book Grokking Continuous Delivery is a practical guide to implementing and using continuous delivery in your software projects. It's full of tool-agnostic best practices that you can apply to any software project, from libraries to large service applications. You'll get a complete overview of all the pieces of a CD pipeline and learn how to fit them together for both new and legacy codebases. Each chapter takes on a different real-world scenario you'll face when setting up and working with a CD system, with examples including a startup looking to scale up automation and a social network struggling with a legacy testing suite. You'll uncover the proper approaches to solve each challenge. Author Christie Wilson's plain-and-simple language makes abstract theory easy to grok. With this book as your roadmap, you'll have a clear plan for bringing CD to your team without the need for costly trial-and-error experimentation.# RETAIL SELLING POINTS Design effective CD pipelines for new and legacy projects Keep your software projects release-ready Maintain effective tests Scale CD across multiple applications Ensure pipelines give the right signals at the right time Use version control as the source of truth Safely automate deployments with metrics Describe CD in a way that makes sense to your colleagues AUDIENCE For software engineers who want to start adding CD to their development process.

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Book SynopsisThis book highlights the developments, discoveries, and practical and advanced experiences related to responsive distributed computing and how it can support the deployment of trajectory-based applications in smart systems. Smart Trajectories: Metamodeling, Reactive Architecture for Analytics and Smart Applications deals with the representation and manipulation of smart trajectories in various applications and scenarios. Presented in three parts, the book first discusses the foundation and principles for spatial information systems, complex event processing, and building a reactive architecture. Next, the book discusses modeling and architecture in relation to smart trajectory metamodeling, mining and big trajectory data, and clustering trajectories. The final section discusses advanced applications and trends in the field, including congestion trajectory analytics and real-time Big Data analytics in cloud ecosystems. Metamodeling, distributed architectures, reactive Table of Contents1. Intelligent Distributed Computing Paradigm. 2. Multi Micro-Agent System Middleware Model based on Event Sourcing and CQRS Patterns. 3. Intelligent Distributed Computing Paradigm: Emergence, Challenges and Future Research Directions. 4. Emerging Paradigm of Urban Computing: Challenges, Applications, and Future Research Directions. 5. Complex Event Processing Architectures for Smart City Applications. 6. Portunus: Enhancing smart city application connectivity with complex space-time events distributed system. 7. Smart Trajectories Metamodeling. 8. A Type Level Trajectory Framework. 9. A Distributed Reactive Trajectory Framework for Nearby Events Discovery. 10. A Multidimensional Trajectory Model in the Context of Mobile Crowd Sensing. 11. Trajectory mining based on Process mining in RORO terminals: Performance-Driven Analysis to support trajectories redesign. 12. Aspects from mobility data in fog/cloud era: Directions from a pilot case study of Hazmat transportation telemonitoring in urban area. 13. Utility assessment of line-of-sight traffic jam and queue detection in urban environments for intelligent road vehicles. 14. Risky Trajectory Prediction for Safe Walkability under Intuitionistic Fuzzy Environment. 15. A real-time reactive Service Oriented Architecture for safe urban walkability. 16. Safest Trajectories for Pedestrian using Distributed Architecture based on Spatial Risk Analysis and Voronoï Spatial Accessibility. 17. Towards a predictive simulation framework of accidents risks for pedestrians based on distributed artificial intelligence and intuitionist fuzzy modeling. 18. Trajectory to a new shape of organizational structure, Enterprise Architect and Organizational Audit for Governance of Information Systems Processes. 19. Dynamic detection of fuzzy sub-congested urban traffic networks. 20. Multi-Agent Modeling for Pedestrian Risk Assessment.

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Book SynopsisDive into the capabilities of Wireshark with Wireshark Essentials, a concise guide focused on utilizing packet analysis for network security and troubleshooting. This book is ideal for IT professionals, network administrators, and cybersecurity enthusiasts. It details how to use Wireshark's filtering features to effectively monitor and secure networks. Each chapter includes practical scenarios and MCQs to reinforce concepts, making this an essential resource for anyone looking to enhance their network diagnostic skills. Whether you're a beginner or a seasoned expert, Wireshark Essentials provides the tools needed to master network analysis in real-world situations.

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Book SynopsisThis practical book covers design approaches and technologies that make it possible to scale an application quickly and cost-effectively. Author Ian Gorton takes software architects and developers through the principles of foundational distributed systems.

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Book SynopsisData meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services. Authors Hubert Dulay and Stephen Mooney examine the vast differences between streaming and batch data meshes.

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Book SynopsisDesigning Distributed Control Systems presents 80 patterns for designing distributed machine control system software architecture (forestry machinery, mining drills, elevators, etc.).Table of ContentsForeword xvii Acknowledgements xxi Chapter 1 Setting the Landscape 1 Chapter 2 The Domain of Distributed Control Systems 9 Chapter 3 Software Architecture and Quality 71 Chapter 4 About Patterns 79 Chapter 5 Pattern Language for Distributed Control Systems 93 Chapter 6 Patterns for Distribution 109 Chapter 7 Messaging Patterns 129 Chapter 8 Event-Handling Patterns 155 Chapter 9 Patterns for Control System Modes 173 Chapter 10 Patterns for Data Management 199 Chapter 11 Patterns to Handle Scarce Resources 227 Chapter 12 Patterns for Decoupling Software and Hardware 263 Chapter 13 Redundancy Patterns 277 Chapter 14 Patterns for System Start-Up 287 Chapter 15 Software Update Patterns 299 Chapter 16 Human–Machine Interface Patterns 311 Chapter 17 High-Level Services Patterns 349 Chapter 18 Fleet Management Patterns 371 Chapter 19 Patterns for System Configuration 379 Chapter 20 Applying Patterns 399 Chapter 21 Concluding Remarks 407 Appendix A Quality Attribute Table 409 Appendix B Patlets 415 Glossary 433 References 439 Index of Patterns 459 Index 465

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Book SynopsisThis book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics.Table of ContentsPreface xv Acknowledgment xxi Part I OVERVIEW 1 Introduction 3 1.1 Embedded Systems Applications 6 1.1.1 Cyber-Physical Systems 6 1.1.2 Space 6 1.1.3 Medical 7 1.1.4 Automotive 8 1.2 Characteristics of Embedded Systems Applications 9 1.2.1 Throughput-Intensive 9 1.2.2 Thermal-Constrained 9 1.2.3 Reliability-Constrained 10 1.2.4 Real-Time 10 1.2.5 Parallel and Distributed 10 1.3 Embedded Systems—Hardware and Software 11 1.3.1 Embedded Systems Hardware 11 1.3.2 Embedded Systems Software 14 1.4 Modeling—An Integral Part of the Embedded Systems Design Flow 15 1.4.1 Modeling Objectives 16 1.4.2 Modeling Paradigms 18 1.4.3 Strategies for Integration of Modeling Paradigms 20 1.5 Optimization in Embedded Systems 21 1.5.1 Optimization of Embedded Systems Design Metrics 23 1.5.2 Multiobjective Optimization 26 1.6 Chapter Summary 27 2 Multicore-Based EWSNs—An Example of Parallel and Distributed Embedded Systems 29 2.1 Multicore Embedded Wireless Sensor Network Architecture 31 2.2 Multicore Embedded Sensor Node Architecture 33 2.2.1 Sensing Unit 34 2.2.2 Processing Unit 34 2.2.3 Storage Unit 34 2.2.4 Communication Unit 35 2.2.5 Power Unit 35 2.2.6 Actuator Unit 35 2.2.7 Location Finding Unit 36 2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs 36 2.3.1 Information Fusion 36 2.3.2 Encryption 38 2.3.3 Network Coding 38 2.3.4 Software-Defined Radio (SDR) 38 2.4 MCEWSN Application Domains 38 2.4.1 Wireless Video Sensor Networks (WVSNs) 39 2.4.2 Wireless Multimedia Sensor Networks (WMSNs) 39 2.4.3 Satellite-Based Wireless Sensor Networks (SBWSN) 40 2.4.4 Space Shuttle Sensor Networks (3SN) 41 2.4.5 Aerial–Terrestrial Hybrid Sensor Networks (ATHSNs) 42 2.4.6 Fault-Tolerant (FT) Sensor Networks 43 2.5 Multicore Embedded Sensor Nodes 43 2.5.1 InstraNode 43 2.5.2 Mars Rover Prototype Mote 43 2.5.3 Satellite-Based Sensor Node (SBSN) 44 2.5.4 Multi-CPU-Based Sensor Node Prototype 44 2.5.5 Smart Camera Mote 44 2.6 Research Challenges and Future Research Directions 45 2.7 Chapter Summary 47 Part II MODELING 3 An Application Metrics Estimation Model for Embedded Wireless Sensor Networks 51 3.1 Application Metrics Estimation Model 52 3.1.1 Lifetime Estimation 53 3.1.2 Throughput Estimation 56 3.1.3 Reliability Estimation 57 3.1.4 Models Validation 57 3.2 Experimental Results 58 3.2.1 Experimental Setup 58 3.2.2 Results 59 3.3 Chapter Summary 61 4 Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks 63 4.1 Related Work 67 4.1.1 Fault Detection 67 4.1.2 Fault Tolerance 68 4.1.3 WSN Reliability Modeling 69 4.2 Fault Diagnosis in WSNs 70 4.2.1 Sensor Faults 70 4.2.2 Taxonomy for Fault Diagnosis Techniques 72 4.3 Distributed Fault Detection Algorithms 74 4.3.1 Fault Detection Algorithm 1: The Chen Algorithm 74 4.3.2 Fault Detection Algorithm 2: The Ding Algorithm 76 4.4 Fault-Tolerant Markov Models 77 4.4.1 Fault-Tolerance Parameters 77 4.4.2 Fault-Tolerant Sensor Node Model 79 4.4.3 Fault-Tolerant WSN Cluster Model 81 4.4.4 Fault-Tolerant WSN Model 83 4.5 Simulation of Distributed Fault Detection Algorithms 85 4.5.1 Using ns−2 to Simulate Faulty Sensors 85 4.5.2 Experimental Setup for Simulated Data 86 4.5.3 Experiments Using Real-World Data 86 4.6 Numerical Results 91 4.6.1 Experimental Setup 91 4.6.2 Reliability and MTTF for an NFT and an FT Sensor Node 91 4.6.3 Reliability and MTTF for an NFT and an FT WSN Cluster 95 4.6.4 Reliability and MTTF for an NFT and an FT WSN 98 4.7 Research Challenges and Future Research Directions 101 4.7.1 Accurate Fault Detection 101 4.7.2 Benchmarks for Comparing Fault Detection Algorithms 101 4.7.3 Energy-Efficient Fault Detection and Tolerance 101 4.7.4 Machine-Learning-Inspired Fault Detection 102 4.7.5 FT in Multimedia Sensor Networks 102 4.7.6 Security 102 4.7.7 WSN Design and Tuning for Reliability 104 4.7.8 Novel WSN Architectures 104 4.8 Chapter Summary 105 5 A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems 107 5.1 Related Work 110 5.2 Queueing Network Modeling of Multicore Embedded Architectures 112 5.2.1 Queueing Network Terminology 112 5.2.2 Modeling Approach 113 5.2.3 Assumptions 119 5.3 Queueing Network Model Validation 120 5.3.1 Theoretical Validation 120 5.3.2 Validation with a Multicore Simulator 120 5.3.3 Speedup 124 5.4 Queueing Theoretic Model Insights 125 5.4.1 Model Setup 125 5.4.2 The Effects of Cache Miss Rates on Performance 129 5.4.3 The Effects of Workloads on Performance 132 5.4.4 Performance per Watt and Performance per Unit Area Computations 135 5.5 Chapter Summary 139 Part III OPTIMIZATION 6 Optimization Approaches in Distributed Embedded Wireless Sensor Networks 143 6.1 Architecture-Level Optimizations 144 6.2 Sensor Node Component-Level Optimizations 146 6.2.1 Sensing Unit 146 6.2.2 Processing Unit 148 6.2.3 Transceiver Unit 148 6.2.4 Storage Unit 148 6.2.5 Actuator Unit 148 6.2.6 Location Finding Unit 149 6.2.7 Power Unit 149 6.3 Data Link-Level Medium Access Control Optimizations 149 6.3.1 Load Balancing and Throughput Optimizations 149 6.3.2 Power/Energy Optimizations 150 6.4 Network-Level Data Dissemination and Routing Protocol Optimizations 152 6.4.1 Query Dissemination Optimizations 152 6.4.2 Real-Time Constrained Optimizations 154 6.4.3 Network Topology Optimizations 154 6.4.4 Resource-Adaptive Optimizations 154 6.5 Operating System-Level Optimizations 155 6.5.1 Event-Driven Optimizations 155 6.5.2 Dynamic Power Management 155 6.5.3 Fault Tolerance 155 6.6 Dynamic Optimizations 156 6.6.1 Dynamic Voltage and Frequency Scaling 156 6.6.2 Software-Based Dynamic Optimizations 156 6.6.3 Dynamic Network Reprogramming 157 6.7 Chapter Summary 157 7 High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing 159 7.1 Characteristics of Embedded Systems Applications 163 7.1.1 Throughput-Intensive 163 7.1.2 Thermal-Constrained 165 7.1.3 Reliability-Constrained 165 7.1.4 Real-Time 165 7.1.5 Parallel and Distributed 165 7.2 Architectural Approaches 166 7.2.1 Core Layout 166 7.2.2 Memory Design 168 7.2.3 Interconnection Network 170 7.2.4 Reduction Techniques 172 7.3 Hardware-Assisted Middleware Approaches 173 7.3.1 Dynamic Voltage and Frequency Scaling 174 7.3.2 Advanced Configuration and Power Interface 174 7.3.3 Gating Techniques 175 7.3.4 Threading Techniques 176 7.3.5 Energy Monitoring and Management 177 7.3.6 Dynamic Thermal Management 178 7.3.7 Dependable Techniques 179 7.4 Software Approaches 180 7.4.1 Data Forwarding 180 7.4.2 Load Distribution 180 7.5 High-Performance Energy-Efficient Multicore Processors 182 7.5.1 ARM11 MPCore 183 7.5.2 ARM Cortex A-9 MPCore 184 7.5.3 MPC8572E PowerQUICC III 184 7.5.4 Tilera TILEPro64 and TILE-Gx 184 7.5.5 AMD Opteron Processor 185 7.5.6 Intel Xeon Processor 185 7.5.7 Intel Sandy Bridge Processor 185 7.5.8 Graphics Processing Units 186 7.6 Challenges and Future Research Directions 186 7.7 Chapter Summary 189 8 An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks 191 8.1 Related Work 193 8.2 MDP-Based Tuning Overview 195 8.2.1 MDP-Based Tuning Methodology for Embedded Wireless Sensor Networks 195 8.2.2 MDP Overview with Respect to Embedded Wireless Sensor Networks 197 8.3 Application-Specific Embedded Sensor Node Tuning Formulation as an MDP 200 8.3.1 State Space 200 8.3.2 Decision Epochs and Actions 200 8.3.3 State Dynamics 201 8.3.4 Policy and Performance Criterion 201 8.3.5 Reward Function 202 8.3.6 Optimality Equation 204 8.3.7 Policy Iteration Algorithm 205 8.4 Implementation Guidelines and Complexity 205 8.4.1 Implementation Guidelines 205 8.4.2 Computational Complexity 206 8.4.3 Data Memory Analysis 207 8.5 Model Extensions 207 8.6 Numerical Results 210 8.6.1 Fixed Heuristic Policies for Performance Comparisons 210 8.6.2 MDP Specifications 210 8.6.3 Results for a Security/Defense System Application 213 8.6.4 Results for a Healthcare Application 216 8.6.5 Results for an Ambient Conditions Monitoring Application 220 8.6.6 Sensitivity Analysis 222 8.6.7 Number of Iterations for Convergence 223 8.7 Chapter Summary 223 9 Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks 225 9.1 Related Work 227 9.2 Dynamic Optimization Methodology 228 9.2.1 Methodology Overview 228 9.2.2 State Space 229 9.2.3 Objective Function 229 9.2.4 Online Optimization Algorithms 230 9.3 Experimental Results 233 9.3.1 Experimental Setup 233 9.3.2 Results 235 9.4 Chapter Summary 239 10 A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks 241 10.1 Related Work 243 10.2 Dynamic Optimization Methodology 244 10.2.1 Overview 244 10.2.2 State Space 246 10.2.3 Optimization Objection Function 246 10.3 Algorithms for Dynamic Optimization Methodology 248 10.3.1 Initial Tunable Parameter Value Settings and Exploration Order 248 10.3.2 Parameter Arrangement 249 10.3.3 Online Optimization Algorithm 251 10.3.4 Computational Complexity 252 10.4 Experimental Results 252 10.4.1 Experimental Setup 253 10.4.2 Results 255 10.5 Chapter Summary 266 11 Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems 269 11.1 Related Work 271 11.2 Multicore Architectures and Benchmarks 272 11.2.1 Multicore Architectures 272 11.2.2 Benchmark Applications and Kernels 273 11.3 Parallel Computing Device Metrics 275 11.4 Results 277 11.4.1 Quantitative Comparison of SMPs and TMAs 277 11.4.2 Benchmark-Driven Results for SMPs 278 11.4.3 Benchmark-Driven Results for TMAs 280 11.4.4 Comparison of SMPs and TMAs 282 11.5 Chapter Summary 285 12 High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study 287 12.1 Related Work 290 12.1.1 Performance Analysis and Optimization 290 12.1.2 Parallelized MM Algorithms 290 12.1.3 Cache Blocking 291 12.1.4 Tiled Manycore Architectures 292 12.2 Tiled Manycore Architecture (TMA) Overview 293 12.2.1 Intel’s TeraFLOPS Research Chip 294 12.2.2 IBM’s Cyclops-64 (C64) 296 12.2.3 Tilera’s TILEPro64 297 12.2.4 Tilera’s TILE64 300 12.3 Parallel Computing Metrics and Matrix Multiplication (MM) Case Study 301 12.3.1 Parallel Computing Metrics for TMAs 301 12.3.2 Matrix Multiplication (MM) Case Study 302 12.4 Matrix Multiplication Algorithms’ Code Snippets for Tilera’s TILEPro64 303 12.4.1 Serial Non-blocked Matrix Multiplication Algorithm 303 12.4.2 Serial Blocked Matrix Multiplication Algorithm 304 12.4.3 Parallel Blocked Matrix Multiplication Algorithm 307 12.4.4 Parallel Blocked Cannon’s Algorithm for Matrix Multiplication 309 12.5 Performance Optimization on a Manycore Architecture 314 12.5.1 Performance Optimization on a Single Tile 314 12.5.2 Parallel Performance Optimizations 315 12.5.3 Compiler-Based Optimizations 319 12.6 Results 323 12.6.1 Data Allocation, Data Decomposition, Data Layout, and Communication 324 12.6.2 Performance Optimizations on a Single Tile 327 12.6.3 Parallel Performance Optimizations 332 12.7 Chapter Summary 339 13 Conclusions 343 References 349 Index 369

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Book SynopsisIf you want to gain insight into the real-world uses of VMware ESXi, this cookbook provides scores of step-by-step solutions for working with this product in a wide range of network environments.

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Book SynopsisHow can Apache Mesos make a difference in your organization? With this practical guide, you'll learn how this cluster manager directs your datacenter's resources, and provides real time APIs for interacting with (and developing for) the entire cluster

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Book SynopsisBuild your expertise in the BPF virtual machine in the Linux kernel with this practical guide for systems engineers. You'll not only dive into the BPF program lifecycle but also learn to write applications that observe and modify the kernel's behavior; inject code to monitor, trace, and securely observe events in the kernel; and more.

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Book SynopsisThis practical guide helps anyone in operations-sysadmins, automation engineers, IT professionals, and site reliability engineers-understand the essential concepts of the role today.

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Book SynopsisIn this book, recognized SLO expert Alex Hidalgo explains how to build an SLO culture from the ground up. Ideal as a primer and daily reference for anyone creating both the culture and tooling necessary for SLO-based approaches to reliability, this guide provides detailed analysis of advanced SLO and service-level indicator (SLI) techniques.

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Book SynopsisElixir's straightforward syntax and this guided tour give you a clean, simple path to learn modern functional programming techniques. No previous functional programming experience required! This book walks you through the right concepts at the right pace, as you explore immutable values and explicit data transformation, functions, modules, recursive functions, pattern matching, high-order functions, polymorphism, and failure handling, all while avoiding side effects. Don't board the Elixir train with an imperative mindset! To get the most out of functional languages, you need to think functionally. This book will get you there. Functional programming offers useful techniques for building maintainable and scalable software that solves today's difficult problems. The demand for software written in this way is increasing - you don't want to miss out. In this book, you'll not only learn Elixir and its features, you'll also learn the mindset required to program functionally. Elixir's clean syntax is excellent for exploring the critical skills of using functions and concurrency. Start with the basic techniques of the functional way: working with immutable data, transforming data in discrete steps, and avoiding side effects. Next, take a deep look at values, expressions, functions, and modules. Then extend your programming with pattern matching and flow control with case, if, cond, and functions. Use recursive functions to create iterations. Work with data types such as lists, tuples, and maps. Improve code reusability and readability with Elixir's most common high-order functions. Explore how to use lazy computation with streams, design your data, and take advantage of polymorphism with protocols. Combine functions and handle failures in a maintainable way using Elixir features and libraries. Learn techniques that matter to make code that lives harmoniously with the language. What You Need: You'll need a computer and Elixir 1.4 or newer version installed. No previous functional programming or Elixir experience is required. Some experience with any programming language is recommended.

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Book SynopsisThis book provides a comprehensive overview of the fundamental principles, concepts, and technologies of distributed computing. It covers various topics including the architecture of distributed systems, communication protocols, distributed file systems, distributed databases, and distributed algorithms. The book is written to help readers understand the complexities of distributed systems and how to design and develop distributed applications that are reliable, scalable, and efficient. This book is an essential text for understanding the concepts and principles of distributed systems.Table of Contents Chapter 1 Introduction to Distributed System Chapter 2 Design Patterns of Distributed Systems Chapter 3 Data Communication and Computer Networks Chapter 4 Consistency and Replication Chapter 5 Distributed File System Chapter 6 Data Storage in Distributed System Chapter 7 Scalability and Performance in Distributed System Chapter 8 Distributed System Security

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Book SynopsisThis edited book aims to present the state of the art in research and development of the convergence of high-performance computing and parallel programming for various engineering and scientific applications. The book has consolidated algorithms, techniques, and methodologies to bridge the gap between the theoretical foundations of academia and implementation for research, which might be used in business and other real-time applications in the future.The book outlines techniques and tools used for emergent areas and domains, which include acceleration of large-scale electronic structure simulations with heterogeneous parallel computing, characterizing power and energy efficiency of a data-centric high-performance computing runtime and applications, security applications of GPUs, parallel implementation of multiprocessors on MPI using FDTD, particle-based fused rendering, design and implementation of particle systems for mesh-free methods with high performance, and evolving topics on heterogeneous computing. In the coming days the need to converge HPC, IoT, cloud-based applications will be felt and this volume tries to bridge that gap.

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Book SynopsisThe past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to clusters. Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out their computations over clusters. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.Table of Contents Preface 1. Introduction 2. Resilient Distributed Datasets 3. Models Built over RDDs 4. Discretized Streams 5. Generality of RDDs 6. Conclusion References Author's Biography

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Book SynopsisThe past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to clusters. Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out their computations over clusters. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.Table of Contents Preface 1. Introduction 2. Resilient Distributed Datasets 3. Models Built over RDDs 4. Discretized Streams 5. Generality of RDDs 6. Conclusion References Author's Biography

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Book SynopsisThis book constitutes the proceedings of the 23rd International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2022, which took place in Sendai, Japan, during December 7-9, 2022.The 24 full papers and 16 short papers included in this volume were carefully reviewed and selected from 95 submissions. The papers are categorized into the following topical sub-headings: Heterogeneous System (1; HPC & AI; Embedded systems & Communication; Blockchain; Deep Learning; Quantum Computing & Programming Language; Best Papers; Heterogeneous System (2); Equivalence Checking & Model checking; Interconnect; Optimization (1); Optimization (2); Privacy; and Workflow.Table of ContentsHeterogeneous System (1).- Towards Priority-Flexible Task Mapping for Heterogeneous Multi-Core NUMA systems.- Multi-GPU Scaling of a Conservative Weakly Compressible Solver for Large-scale Two-phase Flow Simulation.- Improving the Performance of Lattice Boltzmann Method with Pipelined Algorithm on A Heterogeneous Multi-zone Processor.- FPGA.- DEEPFAKE CLI: Accelerated Deepfake Detection using FPGAs.- Memory access optimization for former process of pencil drawing style image conversion in High-level Synthesis.- Word2Vec FPGA Accelerator Based on Spatial and Temporal Parallelism.- HPC & AI.- Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning.- An Advantage Actor-Critic Deep Reinforcement Learning Method for Power Management in HPC Systems.- An AutoML Based Algorithm for Performance Prediction in HPC Systems.- Embedded systems & Communication.- Edge-Gateway Intrusion Detection for Smart Home.- Energy-Delay Tradeoff in Parallel Task Allocation and Execution for Autonomous Platooning Applications.- A Reservation-based List Scheduling for Embedded Systems with Memory Constraints.- Formalization and Verification of SIP Using CSP.- Blockchain.- Towards a Blockchain and Fog-Based Proactive Data Distribution Framework for ICN.- Research on user influence weighted scoring algorithm incorporating incentive mechanism.- BloodMan-Chain: A Management of Blood and Its Products Transportation based on Blockchain Approach.- Deep Learning.- A Systematic Comparison on Prevailing Intrusion Detection Models.- Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet Networks.- Detecting Network Intrusions with Resilient Approaches Based on Convolutional Neural Networks.- Quantum Computing & Programming Language.- Analysis of Precision Vectors for Ising-based Linear Regression.- Evaluating and Analyzing Irregular Tree Search in the Tascell and HOPE Parallel Programming Languages.- Best Papers.- Distributed Parallel Tall-Skinny QR factorization: Performance Evaluation of Various Algorithms on Various Systems.- A Partitioned Memory Architecture with Prefetching for Efficient Video Encoders .- A Hardware Trojan Exploiting Coherence Protocol on NoCs.- A System-Wide Communication to Couple Multiple MPI Programs for Heterogeneous Computing.- Heterogeneous System (2).- A task-parallel runtime for heterogeneous multi-node vector systems.- Accelerating Radiative Transfer Simulation on NVIDIA GPUs with OpenACC.- QR Factorization of Block Low-Rank Matrices on Multi-Instance GPU.- Equivalence Checking & Model checking.- Equivalence Checking of Code Transformation by Numerical and Symbolic Approaches.- MEA: A Framework for Model Checking of Mutual Exclusion Algorithms Focusing on Atomicity.- Interconnect.- A High-Radix Non-Random Shortcut Network Topology for Efficient Collective Communication.- Fault Tolerance and Packet Latency of Peer Fat-Trees.- Accelerating Imbalanced Many-to-Many Communication with Systematic Delay Insertion.- Optimization (1).- Optimizing Depthwise Convolutions on ARMv8 Architecture.- A Profiling-based Approach to Cache Partitioning of Program Data.- Optimization (2).- Memory Bandwidth Conservation for SpMV Kernels through Adaptive Lossy Data Compression.- SimdFSM: An Adaptive Vectorization of Finite State Machines for Speculative Execution.- Privacy.- Broad Learning Inference Based on Fully Homomorphic Encryption.- Application of probabilistic common set in the open world set for vertical federated learning.- Workflow.- Towards a Standard Process Management Infrastructure for Workflows. 　

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Book Synopsis

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