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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+","brand":"The Pragmatic Programmers","offers":[{"title":"Default Title","offer_id":48868033855831,"sku":"9781680507607","price":35.14,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781680507607.jpg?v=1722286108"},{"product_id":"grokking-continuous-delivery-9781617298257","title":"Grokking Continuous Delivery","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Does an excellent job at delivering CI\/CD theory without the burden of a specific technology\/tool.' Tony Sweets \u003cbr\u003e'This book is a must read for everyone doing CI\/CD' Tobias Getrost \u003cbr\u003e'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 \u003cbr\u003e'The perfect book to understand the basic concepts of CI\/CD for beginners.'Andrea C. Granata \u003cbr\u003e'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 Honadel\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eBuild and use systems that safely automate software delivery from testing through release with this jargon-busting guide to Continuous Delivery pipelines.\u003c\/b\u003e  \u003cbr\u003e  \u003cbr\u003eIn     \u003ci\u003e\u003cstrong\u003eGrokking Continuous Delivery\u003c\/strong\u003e\u003c\/i\u003e   you will learn how to:  \u003cbr\u003e  \u003cbr\u003e  \u003cul\u003e\n\u003cli\u003eDesign effective CD pipelines for new and legacy projects\u003c\/li\u003e\n\u003cli\u003eKeep your software projects release-ready\u003c\/li\u003e\n\u003cli\u003eMaintain effective tests\u003c\/li\u003e\n\u003cli\u003eScale CD across multiple applications\u003c\/li\u003e\n\u003cli\u003eEnsure pipelines give the right signals at the right time\u003c\/li\u003e\n\u003cli\u003eUse version control as the source of truth\u003c\/li\u003e\n\u003cli\u003eSafely automate deployments with metrics\u003c\/li\u003e\n\u003cli\u003eDescribe CD in a way that makes sense to your colleagues\u003c\/li\u003e\n\u003c\/ul\u003e  \u003cbr\u003e  \u003ci\u003e\u003cstrong\u003eGrokking Continuous Delivery\u003c\/strong\u003e\u003c\/i\u003e   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  \u003cstrong\u003e\u003c\/strong\u003e  \u003ci\u003e\u003cstrong\u003eGrokking Continuous Delivery\u003c\/strong\u003e\u003c\/i\u003e   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.  \u003cbr\u003e  \u003cbr\u003eEach 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.#    \u003cstrong\u003eRETAIL SELLING POINTS\u003c\/strong\u003e  \u003cul\u003e\n\u003cli\u003e    Design effective CD pipelines for new and legacy projects  \u003c\/li\u003e\n\u003cli\u003e    Keep your software projects release-ready  \u003c\/li\u003e\n\u003cli\u003e    Maintain effective tests  \u003c\/li\u003e\n\u003cli\u003e    Scale CD across multiple applications  \u003c\/li\u003e\n\u003cli\u003e    Ensure pipelines give the right signals at the right time  \u003c\/li\u003e\n\u003cli\u003e    Use version control as the source of truth  \u003c\/li\u003e\n\u003cli\u003e    Safely automate deployments with metrics  \u003c\/li\u003e\n\u003cli\u003e    Describe CD in a way that makes sense to your colleagues  \u003c\/li\u003e\n\u003c\/ul\u003e   \u003cstrong\u003eAUDIENCE\u003c\/strong\u003e    For software engineers who want to start adding CD to their development process.","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48886900588887,"sku":"9781617298257","price":41.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617298257.jpg?v=1722542092"},{"product_id":"architecture-modernization-socio-technical-alignment-of-software-strategy-and-structure-9781633438156","title":"Architecture Modernization: Socio-Technical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eAbout the Author:\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eAdam Freeman\u003c\/strong\u003e is an experienced IT professional who started his career as a programmer. 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of pencil drawing style image conversion in High-level Synthesis.- Word2Vec FPGA Accelerator Based on Spatial and Temporal Parallelism.- \u003cb\u003eHPC \u0026amp; AI\u003c\/b\u003e.- 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.- \u003cb\u003eEmbedded systems \u0026amp; Communication\u003c\/b\u003e.- 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.- \u003cb\u003eBlockchain\u003c\/b\u003e.- 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.- \u003cb\u003eDeep Learning\u003c\/b\u003e.- 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.- \u003cb\u003eQuantum Computing \u0026amp; Programming Language\u003c\/b\u003e.- Analysis of Precision Vectors for Ising-based Linear Regression.- Evaluating and Analyzing Irregular Tree Search in the Tascell and HOPE Parallel Programming Languages.- \u003cb\u003eBest Papers\u003c\/b\u003e.- 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.- \u003cb\u003eHeterogeneous System (2)\u003c\/b\u003e.- 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.- \u003cb\u003eEquivalence Checking \u0026amp; Model checking\u003c\/b\u003e.- Equivalence Checking of Code Transformation by Numerical and Symbolic Approaches.- MEA: A Framework for Model Checking of Mutual Exclusion Algorithms Focusing on Atomicity.- \u003cb\u003eInterconnect\u003c\/b\u003e.- 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.- \u003cb\u003eOptimization (1)\u003c\/b\u003e.- Optimizing Depthwise Convolutions on ARMv8 Architecture.- A Profiling-based Approach to Cache Partitioning of Program Data.- \u003cb\u003eOptimization (2)\u003c\/b\u003e.- Memory Bandwidth Conservation for SpMV Kernels through Adaptive Lossy Data Compression.- SimdFSM: An Adaptive Vectorization of Finite State Machines for Speculative Execution.- \u003cb\u003ePrivacy\u003c\/b\u003e.- Broad Learning Inference Based on Fully Homomorphic Encryption.- Application of probabilistic common set in the open world set for vertical federated learning.- \u003cb\u003eWorkflow\u003c\/b\u003e.- Towards a Standard Process Management Infrastructure for Workflows. \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e　  \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e   \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e   \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e   \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e    \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e   \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e      \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cbr\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415706444119,"sku":"9783031299261","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031299261.jpg?v=1730527853"},{"product_id":"modeling-and-optimization-of-parallel-and-distributed-embedded-systems-9781119086413","title":"Modeling and Optimization of Parallel and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgment xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I OVERVIEW\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Embedded Systems Applications 6\u003c\/p\u003e \u003cp\u003e1.1.1 Cyber-Physical Systems 6\u003c\/p\u003e \u003cp\u003e1.1.2 Space 6\u003c\/p\u003e \u003cp\u003e1.1.3 Medical 7\u003c\/p\u003e \u003cp\u003e1.1.4 Automotive 8\u003c\/p\u003e \u003cp\u003e1.2 Characteristics of Embedded Systems Applications 9\u003c\/p\u003e \u003cp\u003e1.2.1 Throughput-Intensive 9\u003c\/p\u003e \u003cp\u003e1.2.2 Thermal-Constrained 9\u003c\/p\u003e \u003cp\u003e1.2.3 Reliability-Constrained 10\u003c\/p\u003e \u003cp\u003e1.2.4 Real-Time 10\u003c\/p\u003e \u003cp\u003e1.2.5 Parallel and Distributed 10\u003c\/p\u003e \u003cp\u003e1.3 Embedded Systems—Hardware and Software 11\u003c\/p\u003e \u003cp\u003e1.3.1 Embedded Systems Hardware 11\u003c\/p\u003e \u003cp\u003e1.3.2 Embedded Systems Software 14\u003c\/p\u003e \u003cp\u003e1.4 Modeling—An Integral Part of the Embedded Systems Design Flow 15\u003c\/p\u003e \u003cp\u003e1.4.1 Modeling Objectives 16\u003c\/p\u003e \u003cp\u003e1.4.2 Modeling Paradigms 18\u003c\/p\u003e \u003cp\u003e1.4.3 Strategies for Integration of Modeling Paradigms 20\u003c\/p\u003e \u003cp\u003e1.5 Optimization in Embedded Systems 21\u003c\/p\u003e \u003cp\u003e1.5.1 Optimization of Embedded Systems Design Metrics 23\u003c\/p\u003e \u003cp\u003e1.5.2 Multiobjective Optimization 26\u003c\/p\u003e \u003cp\u003e1.6 Chapter Summary 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Multicore-Based EWSNs—An Example of Parallel and Distributed Embedded Systems 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Multicore Embedded Wireless Sensor Network Architecture 31\u003c\/p\u003e \u003cp\u003e2.2 Multicore Embedded Sensor Node Architecture 33\u003c\/p\u003e \u003cp\u003e2.2.1 Sensing Unit 34\u003c\/p\u003e \u003cp\u003e2.2.2 Processing Unit 34\u003c\/p\u003e \u003cp\u003e2.2.3 Storage Unit 34\u003c\/p\u003e \u003cp\u003e2.2.4 Communication Unit 35\u003c\/p\u003e \u003cp\u003e2.2.5 Power Unit 35\u003c\/p\u003e \u003cp\u003e2.2.6 Actuator Unit 35\u003c\/p\u003e \u003cp\u003e2.2.7 Location Finding Unit 36\u003c\/p\u003e \u003cp\u003e2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs 36\u003c\/p\u003e \u003cp\u003e2.3.1 Information Fusion 36\u003c\/p\u003e \u003cp\u003e2.3.2 Encryption 38\u003c\/p\u003e \u003cp\u003e2.3.3 Network Coding 38\u003c\/p\u003e \u003cp\u003e2.3.4 Software-Defined Radio (SDR) 38\u003c\/p\u003e \u003cp\u003e2.4 MCEWSN Application Domains 38\u003c\/p\u003e \u003cp\u003e2.4.1 Wireless Video Sensor Networks (WVSNs) 39\u003c\/p\u003e \u003cp\u003e2.4.2 Wireless Multimedia Sensor Networks (WMSNs) 39\u003c\/p\u003e \u003cp\u003e2.4.3 Satellite-Based Wireless Sensor Networks (SBWSN) 40\u003c\/p\u003e \u003cp\u003e2.4.4 Space Shuttle Sensor Networks (3SN) 41\u003c\/p\u003e \u003cp\u003e2.4.5 Aerial–Terrestrial Hybrid Sensor Networks (ATHSNs) 42\u003c\/p\u003e \u003cp\u003e2.4.6 Fault-Tolerant (FT) Sensor Networks 43\u003c\/p\u003e \u003cp\u003e2.5 Multicore Embedded Sensor Nodes 43\u003c\/p\u003e \u003cp\u003e2.5.1 InstraNode 43\u003c\/p\u003e \u003cp\u003e2.5.2 Mars Rover Prototype Mote 43\u003c\/p\u003e \u003cp\u003e2.5.3 Satellite-Based Sensor Node (SBSN) 44\u003c\/p\u003e \u003cp\u003e2.5.4 Multi-CPU-Based Sensor Node Prototype 44\u003c\/p\u003e \u003cp\u003e2.5.5 Smart Camera Mote 44\u003c\/p\u003e \u003cp\u003e2.6 Research Challenges and Future Research Directions 45\u003c\/p\u003e \u003cp\u003e2.7 Chapter Summary 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II MODELING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 An Application Metrics Estimation Model for Embedded Wireless Sensor Networks 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Application Metrics Estimation Model 52\u003c\/p\u003e \u003cp\u003e3.1.1 Lifetime Estimation 53\u003c\/p\u003e \u003cp\u003e3.1.2 Throughput Estimation 56\u003c\/p\u003e \u003cp\u003e3.1.3 Reliability Estimation 57\u003c\/p\u003e \u003cp\u003e3.1.4 Models Validation 57\u003c\/p\u003e \u003cp\u003e3.2 Experimental Results 58\u003c\/p\u003e \u003cp\u003e3.2.1 Experimental Setup 58\u003c\/p\u003e \u003cp\u003e3.2.2 Results 59\u003c\/p\u003e \u003cp\u003e3.3 Chapter Summary 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Related Work 67\u003c\/p\u003e \u003cp\u003e4.1.1 Fault Detection 67\u003c\/p\u003e \u003cp\u003e4.1.2 Fault Tolerance 68\u003c\/p\u003e \u003cp\u003e4.1.3 WSN Reliability Modeling 69\u003c\/p\u003e \u003cp\u003e4.2 Fault Diagnosis in WSNs 70\u003c\/p\u003e \u003cp\u003e4.2.1 Sensor Faults 70\u003c\/p\u003e \u003cp\u003e4.2.2 Taxonomy for Fault Diagnosis Techniques 72\u003c\/p\u003e \u003cp\u003e4.3 Distributed Fault Detection Algorithms 74\u003c\/p\u003e \u003cp\u003e4.3.1 Fault Detection Algorithm 1: The Chen Algorithm 74\u003c\/p\u003e \u003cp\u003e4.3.2 Fault Detection Algorithm 2: The Ding Algorithm 76\u003c\/p\u003e \u003cp\u003e4.4 Fault-Tolerant Markov Models 77\u003c\/p\u003e \u003cp\u003e4.4.1 Fault-Tolerance Parameters 77\u003c\/p\u003e \u003cp\u003e4.4.2 Fault-Tolerant Sensor Node Model 79\u003c\/p\u003e \u003cp\u003e4.4.3 Fault-Tolerant WSN Cluster Model 81\u003c\/p\u003e \u003cp\u003e4.4.4 Fault-Tolerant WSN Model 83\u003c\/p\u003e \u003cp\u003e4.5 Simulation of Distributed Fault Detection Algorithms 85\u003c\/p\u003e \u003cp\u003e4.5.1 Using ns−2 to Simulate Faulty Sensors 85\u003c\/p\u003e \u003cp\u003e4.5.2 Experimental Setup for Simulated Data 86\u003c\/p\u003e \u003cp\u003e4.5.3 Experiments Using Real-World Data 86\u003c\/p\u003e \u003cp\u003e4.6 Numerical Results 91\u003c\/p\u003e \u003cp\u003e4.6.1 Experimental Setup 91\u003c\/p\u003e \u003cp\u003e4.6.2 Reliability and MTTF for an NFT and an FT Sensor Node 91\u003c\/p\u003e \u003cp\u003e4.6.3 Reliability and MTTF for an NFT and an FT WSN Cluster 95\u003c\/p\u003e \u003cp\u003e4.6.4 Reliability and MTTF for an NFT and an FT WSN 98\u003c\/p\u003e \u003cp\u003e4.7 Research Challenges and Future Research Directions 101\u003c\/p\u003e \u003cp\u003e4.7.1 Accurate Fault Detection 101\u003c\/p\u003e \u003cp\u003e4.7.2 Benchmarks for Comparing Fault Detection Algorithms 101\u003c\/p\u003e \u003cp\u003e4.7.3 Energy-Efficient Fault Detection and Tolerance 101\u003c\/p\u003e \u003cp\u003e4.7.4 Machine-Learning-Inspired Fault Detection 102\u003c\/p\u003e \u003cp\u003e4.7.5 FT in Multimedia Sensor Networks 102\u003c\/p\u003e \u003cp\u003e4.7.6 Security 102\u003c\/p\u003e \u003cp\u003e4.7.7 WSN Design and Tuning for Reliability 104\u003c\/p\u003e \u003cp\u003e4.7.8 Novel WSN Architectures 104\u003c\/p\u003e \u003cp\u003e4.8 Chapter Summary 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Related Work 110\u003c\/p\u003e \u003cp\u003e5.2 Queueing Network Modeling of Multicore Embedded Architectures 112\u003c\/p\u003e \u003cp\u003e5.2.1 Queueing Network Terminology 112\u003c\/p\u003e \u003cp\u003e5.2.2 Modeling Approach 113\u003c\/p\u003e \u003cp\u003e5.2.3 Assumptions 119\u003c\/p\u003e \u003cp\u003e5.3 Queueing Network Model Validation 120\u003c\/p\u003e \u003cp\u003e5.3.1 Theoretical Validation 120\u003c\/p\u003e \u003cp\u003e5.3.2 Validation with a Multicore Simulator 120\u003c\/p\u003e \u003cp\u003e5.3.3 Speedup 124\u003c\/p\u003e \u003cp\u003e5.4 Queueing Theoretic Model Insights 125\u003c\/p\u003e \u003cp\u003e5.4.1 Model Setup 125\u003c\/p\u003e \u003cp\u003e5.4.2 The Effects of Cache Miss Rates on Performance 129\u003c\/p\u003e \u003cp\u003e5.4.3 The Effects of Workloads on Performance 132\u003c\/p\u003e \u003cp\u003e5.4.4 Performance per Watt and Performance per Unit Area Computations 135\u003c\/p\u003e \u003cp\u003e5.5 Chapter Summary 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III OPTIMIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optimization Approaches in Distributed Embedded Wireless Sensor Networks 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Architecture-Level Optimizations 144\u003c\/p\u003e \u003cp\u003e6.2 Sensor Node Component-Level Optimizations 146\u003c\/p\u003e \u003cp\u003e6.2.1 Sensing Unit 146\u003c\/p\u003e \u003cp\u003e6.2.2 Processing Unit 148\u003c\/p\u003e \u003cp\u003e6.2.3 Transceiver Unit 148\u003c\/p\u003e \u003cp\u003e6.2.4 Storage Unit 148\u003c\/p\u003e \u003cp\u003e6.2.5 Actuator Unit 148\u003c\/p\u003e \u003cp\u003e6.2.6 Location Finding Unit 149\u003c\/p\u003e \u003cp\u003e6.2.7 Power Unit 149\u003c\/p\u003e \u003cp\u003e6.3 Data Link-Level Medium Access Control Optimizations 149\u003c\/p\u003e \u003cp\u003e6.3.1 Load Balancing and Throughput Optimizations 149\u003c\/p\u003e \u003cp\u003e6.3.2 Power\/Energy Optimizations 150\u003c\/p\u003e \u003cp\u003e6.4 Network-Level Data Dissemination and Routing Protocol Optimizations 152\u003c\/p\u003e \u003cp\u003e6.4.1 Query Dissemination Optimizations 152\u003c\/p\u003e \u003cp\u003e6.4.2 Real-Time Constrained Optimizations 154\u003c\/p\u003e \u003cp\u003e6.4.3 Network Topology Optimizations 154\u003c\/p\u003e \u003cp\u003e6.4.4 Resource-Adaptive Optimizations 154\u003c\/p\u003e \u003cp\u003e6.5 Operating System-Level Optimizations 155\u003c\/p\u003e \u003cp\u003e6.5.1 Event-Driven Optimizations 155\u003c\/p\u003e \u003cp\u003e6.5.2 Dynamic Power Management 155\u003c\/p\u003e \u003cp\u003e6.5.3 Fault Tolerance 155\u003c\/p\u003e \u003cp\u003e6.6 Dynamic Optimizations 156\u003c\/p\u003e \u003cp\u003e6.6.1 Dynamic Voltage and Frequency Scaling 156\u003c\/p\u003e \u003cp\u003e6.6.2 Software-Based Dynamic Optimizations 156\u003c\/p\u003e \u003cp\u003e6.6.3 Dynamic Network Reprogramming 157\u003c\/p\u003e \u003cp\u003e6.7 Chapter Summary 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Characteristics of Embedded Systems Applications 163\u003c\/p\u003e \u003cp\u003e7.1.1 Throughput-Intensive 163\u003c\/p\u003e \u003cp\u003e7.1.2 Thermal-Constrained 165\u003c\/p\u003e \u003cp\u003e7.1.3 Reliability-Constrained 165\u003c\/p\u003e \u003cp\u003e7.1.4 Real-Time 165\u003c\/p\u003e \u003cp\u003e7.1.5 Parallel and Distributed 165\u003c\/p\u003e \u003cp\u003e7.2 Architectural Approaches 166\u003c\/p\u003e \u003cp\u003e7.2.1 Core Layout 166\u003c\/p\u003e \u003cp\u003e7.2.2 Memory Design 168\u003c\/p\u003e \u003cp\u003e7.2.3 Interconnection Network 170\u003c\/p\u003e \u003cp\u003e7.2.4 Reduction Techniques 172\u003c\/p\u003e \u003cp\u003e7.3 Hardware-Assisted Middleware Approaches 173\u003c\/p\u003e \u003cp\u003e7.3.1 Dynamic Voltage and Frequency Scaling 174\u003c\/p\u003e \u003cp\u003e7.3.2 Advanced Configuration and Power Interface 174\u003c\/p\u003e \u003cp\u003e7.3.3 Gating Techniques 175\u003c\/p\u003e \u003cp\u003e7.3.4 Threading Techniques 176\u003c\/p\u003e \u003cp\u003e7.3.5 Energy Monitoring and Management 177\u003c\/p\u003e \u003cp\u003e7.3.6 Dynamic Thermal Management 178\u003c\/p\u003e \u003cp\u003e7.3.7 Dependable Techniques 179\u003c\/p\u003e \u003cp\u003e7.4 Software Approaches 180\u003c\/p\u003e \u003cp\u003e7.4.1 Data Forwarding 180\u003c\/p\u003e \u003cp\u003e7.4.2 Load Distribution 180\u003c\/p\u003e \u003cp\u003e7.5 High-Performance Energy-Efficient Multicore Processors 182\u003c\/p\u003e \u003cp\u003e7.5.1 ARM11 MPCore 183\u003c\/p\u003e \u003cp\u003e7.5.2 ARM Cortex A-9 MPCore 184\u003c\/p\u003e \u003cp\u003e7.5.3 MPC8572E PowerQUICC III 184\u003c\/p\u003e \u003cp\u003e7.5.4 Tilera TILEPro64 and TILE-Gx 184\u003c\/p\u003e \u003cp\u003e7.5.5 AMD Opteron Processor 185\u003c\/p\u003e \u003cp\u003e7.5.6 Intel Xeon Processor 185\u003c\/p\u003e \u003cp\u003e7.5.7 Intel Sandy Bridge Processor 185\u003c\/p\u003e \u003cp\u003e7.5.8 Graphics Processing Units 186\u003c\/p\u003e \u003cp\u003e7.6 Challenges and Future Research Directions 186\u003c\/p\u003e \u003cp\u003e7.7 Chapter Summary 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Related Work 193\u003c\/p\u003e \u003cp\u003e8.2 MDP-Based Tuning Overview 195\u003c\/p\u003e \u003cp\u003e8.2.1 MDP-Based Tuning Methodology for Embedded Wireless Sensor Networks 195\u003c\/p\u003e \u003cp\u003e8.2.2 MDP Overview with Respect to Embedded Wireless Sensor Networks 197\u003c\/p\u003e \u003cp\u003e8.3 Application-Specific Embedded Sensor Node Tuning Formulation as an MDP 200\u003c\/p\u003e \u003cp\u003e8.3.1 State Space 200\u003c\/p\u003e \u003cp\u003e8.3.2 Decision Epochs and Actions 200\u003c\/p\u003e \u003cp\u003e8.3.3 State Dynamics 201\u003c\/p\u003e \u003cp\u003e8.3.4 Policy and Performance Criterion 201\u003c\/p\u003e \u003cp\u003e8.3.5 Reward Function 202\u003c\/p\u003e \u003cp\u003e8.3.6 Optimality Equation 204\u003c\/p\u003e \u003cp\u003e8.3.7 Policy Iteration Algorithm 205\u003c\/p\u003e \u003cp\u003e8.4 Implementation Guidelines and Complexity 205\u003c\/p\u003e \u003cp\u003e8.4.1 Implementation Guidelines 205\u003c\/p\u003e \u003cp\u003e8.4.2 Computational Complexity 206\u003c\/p\u003e \u003cp\u003e8.4.3 Data Memory Analysis 207\u003c\/p\u003e \u003cp\u003e8.5 Model Extensions 207\u003c\/p\u003e \u003cp\u003e8.6 Numerical Results 210\u003c\/p\u003e \u003cp\u003e8.6.1 Fixed Heuristic Policies for Performance Comparisons 210\u003c\/p\u003e \u003cp\u003e8.6.2 MDP Specifications 210\u003c\/p\u003e \u003cp\u003e8.6.3 Results for a Security\/Defense System Application 213\u003c\/p\u003e \u003cp\u003e8.6.4 Results for a Healthcare Application 216\u003c\/p\u003e \u003cp\u003e8.6.5 Results for an Ambient Conditions Monitoring Application 220\u003c\/p\u003e \u003cp\u003e8.6.6 Sensitivity Analysis 222\u003c\/p\u003e \u003cp\u003e8.6.7 Number of Iterations for Convergence 223\u003c\/p\u003e \u003cp\u003e8.7 Chapter Summary 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Related Work 227\u003c\/p\u003e \u003cp\u003e9.2 Dynamic Optimization Methodology 228\u003c\/p\u003e \u003cp\u003e9.2.1 Methodology Overview 228\u003c\/p\u003e \u003cp\u003e9.2.2 State Space 229\u003c\/p\u003e \u003cp\u003e9.2.3 Objective Function 229\u003c\/p\u003e \u003cp\u003e9.2.4 Online Optimization Algorithms 230\u003c\/p\u003e \u003cp\u003e9.3 Experimental Results 233\u003c\/p\u003e \u003cp\u003e9.3.1 Experimental Setup 233\u003c\/p\u003e \u003cp\u003e9.3.2 Results 235\u003c\/p\u003e \u003cp\u003e9.4 Chapter Summary 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Related Work 243\u003c\/p\u003e \u003cp\u003e10.2 Dynamic Optimization Methodology 244\u003c\/p\u003e \u003cp\u003e10.2.1 Overview 244\u003c\/p\u003e \u003cp\u003e10.2.2 State Space 246\u003c\/p\u003e \u003cp\u003e10.2.3 Optimization Objection Function 246\u003c\/p\u003e \u003cp\u003e10.3 Algorithms for Dynamic Optimization Methodology 248\u003c\/p\u003e \u003cp\u003e10.3.1 Initial Tunable Parameter Value Settings and Exploration Order 248\u003c\/p\u003e \u003cp\u003e10.3.2 Parameter Arrangement 249\u003c\/p\u003e \u003cp\u003e10.3.3 Online Optimization Algorithm 251\u003c\/p\u003e \u003cp\u003e10.3.4 Computational Complexity 252\u003c\/p\u003e \u003cp\u003e10.4 Experimental Results 252\u003c\/p\u003e \u003cp\u003e10.4.1 Experimental Setup 253\u003c\/p\u003e \u003cp\u003e10.4.2 Results 255\u003c\/p\u003e \u003cp\u003e10.5 Chapter Summary 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Related Work 271\u003c\/p\u003e \u003cp\u003e11.2 Multicore Architectures and Benchmarks 272\u003c\/p\u003e \u003cp\u003e11.2.1 Multicore Architectures 272\u003c\/p\u003e \u003cp\u003e11.2.2 Benchmark Applications and Kernels 273\u003c\/p\u003e \u003cp\u003e11.3 Parallel Computing Device Metrics 275\u003c\/p\u003e \u003cp\u003e11.4 Results 277\u003c\/p\u003e \u003cp\u003e11.4.1 Quantitative Comparison of SMPs and TMAs 277\u003c\/p\u003e \u003cp\u003e11.4.2 Benchmark-Driven Results for SMPs 278\u003c\/p\u003e \u003cp\u003e11.4.3 Benchmark-Driven Results for TMAs 280\u003c\/p\u003e \u003cp\u003e11.4.4 Comparison of SMPs and TMAs 282\u003c\/p\u003e \u003cp\u003e11.5 Chapter Summary 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Related Work 290\u003c\/p\u003e \u003cp\u003e12.1.1 Performance Analysis and Optimization 290\u003c\/p\u003e \u003cp\u003e12.1.2 Parallelized MM Algorithms 290\u003c\/p\u003e \u003cp\u003e12.1.3 Cache Blocking 291\u003c\/p\u003e \u003cp\u003e12.1.4 Tiled Manycore Architectures 292\u003c\/p\u003e \u003cp\u003e12.2 Tiled Manycore Architecture (TMA) Overview 293\u003c\/p\u003e \u003cp\u003e12.2.1 Intel’s TeraFLOPS Research Chip 294\u003c\/p\u003e \u003cp\u003e12.2.2 IBM’s Cyclops-64 (C64) 296\u003c\/p\u003e \u003cp\u003e12.2.3 Tilera’s TILEPro64 297\u003c\/p\u003e \u003cp\u003e12.2.4 Tilera’s TILE64 300\u003c\/p\u003e \u003cp\u003e12.3 Parallel Computing Metrics and Matrix Multiplication (MM) Case Study 301\u003c\/p\u003e \u003cp\u003e12.3.1 Parallel Computing Metrics for TMAs 301\u003c\/p\u003e \u003cp\u003e12.3.2 Matrix Multiplication (MM) Case Study 302\u003c\/p\u003e \u003cp\u003e12.4 Matrix Multiplication Algorithms’ Code Snippets for Tilera’s TILEPro64 303\u003c\/p\u003e \u003cp\u003e12.4.1 Serial Non-blocked Matrix Multiplication Algorithm 303\u003c\/p\u003e \u003cp\u003e12.4.2 Serial Blocked Matrix Multiplication Algorithm 304\u003c\/p\u003e \u003cp\u003e12.4.3 Parallel Blocked Matrix Multiplication Algorithm 307\u003c\/p\u003e \u003cp\u003e12.4.4 Parallel Blocked Cannon’s Algorithm for Matrix Multiplication 309\u003c\/p\u003e \u003cp\u003e12.5 Performance Optimization on a Manycore Architecture 314\u003c\/p\u003e \u003cp\u003e12.5.1 Performance Optimization on a Single Tile 314\u003c\/p\u003e \u003cp\u003e12.5.2 Parallel Performance Optimizations 315\u003c\/p\u003e \u003cp\u003e12.5.3 Compiler-Based Optimizations 319\u003c\/p\u003e \u003cp\u003e12.6 Results 323\u003c\/p\u003e \u003cp\u003e12.6.1 Data Allocation, Data Decomposition, Data Layout, and Communication 324\u003c\/p\u003e \u003cp\u003e12.6.2 Performance Optimizations on a Single Tile 327\u003c\/p\u003e \u003cp\u003e12.6.3 Parallel Performance Optimizations 332\u003c\/p\u003e \u003cp\u003e12.7 Chapter Summary 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Conclusions 343\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 349\u003c\/p\u003e \u003cp\u003eIndex 369\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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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 \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e1. 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. 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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.\u003cbr\u003e\u003cbr\u003e 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. 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This second edition of Mastering Ceph takes you a step closer to becoming an expert on Ceph.\u003c\/p\u003e\u003cp\u003eYou’ll get started by understanding the design goals and planning steps that should be undertaken to ensure successful deployments. In the next sections, you’ll be guided through setting up and deploying the Ceph cluster with the help of orchestration tools. This will allow you to witness Ceph’s scalability, erasure coding (data protective) mechanism, and automated data backup features on multiple servers. You’ll then discover more about the key areas of Ceph including BlueStore, erasure coding and cache tiering with the help of examples. Next, you’ll also learn some of the ways to export Ceph into non-native environments and understand some of the pitfalls that you may encounter. The book features a section on tuning that will take you through the process of optimizing both Ceph and its supporting infrastructure. You’ll also learn to develop applications, which use Librados and distributed computations with shared object classes. Toward the concluding chapters, you’ll learn to troubleshoot issues and handle various scenarios where Ceph is not likely to recover on its own.\u003c\/p\u003e\u003cp\u003eBy the end of this book, you’ll be able to master storage management with Ceph and generate solutions for managing your infrastructure.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003ePlan, design and deploy a Ceph cluster\u003c\/li\u003e\n\u003cli\u003eGet well-versed with different features and storage methods\u003c\/li\u003e\n\u003cli\u003eCarry out regular maintenance and daily operations with ease\u003c\/li\u003e\n\u003cli\u003eTune Ceph for improved  ROI and performance\u003c\/li\u003e\n\u003cli\u003eRecover Ceph from a range of issues\u003c\/li\u003e\n\u003cli\u003eUpgrade  clusters to BlueStore\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you are a storage professional, system administrator, or cloud engineer looking for guidance on building powerful storage solutions for your cloud and on-premise infrastructure, this book is for you.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003ePlanning for ceph\u003c\/li\u003e\n\u003cli\u003eDeploying Ceph With Containers \u003c\/li\u003e\n\u003cli\u003eBluestore\u003c\/li\u003e\n\u003cli\u003eCeph and Non-Native Protocols\u003c\/li\u003e\n\u003cli\u003eRADOS Pools and Client Access\u003c\/li\u003e\n\u003cli\u003eDeveloping with Librados\u003c\/li\u003e\n\u003cli\u003eDistributed Computation with Ceph RADOS Classes\u003c\/li\u003e\n\u003cli\u003eMonitoring Ceph\u003c\/li\u003e\n\u003cli\u003eTuning Ceph\u003c\/li\u003e\n\u003cli\u003eTiering with Ceph\u003c\/li\u003e\n\u003cli\u003eTroubleshooting\u003c\/li\u003e\n\u003cli\u003eDisaster Recovery\u003c\/li\u003e\n\u003cli\u003eAssessments\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":52085547565399,"sku":"9781789610703","price":42.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789610703.jpg?v=1762209930"},{"product_id":"practical-cybersecurity-architecture-a-guide-to-creating-and-implementing-robust-designs-for-cybersecurity-architects-9781837637164","title":"Practical Cybersecurity Architecture: A guide to creating and implementing robust designs for cybersecurity architects","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003ePlan, design, and build resilient security architectures to secure your organization's hybrid networks, cloud-based workflows, services, and applications  Key Features  Understand the role of the architect in successfully creating complex security structures Learn methodologies for creating architecture documentation, engaging stakeholders, and implementing designs Understand how to refine and improve architecture methodologies to meet business challenges Purchase of the print or Kindle book includes a free PDF eBook  Book DescriptionCybersecurity architecture is the discipline of systematically ensuring that an organization is resilient against cybersecurity threats. 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As you progress, you'll also learn about well-known frameworks for building robust designs and strategies that you can adopt to create your own designs. By the end of this book, you’ll have the foundational skills required to build infrastructure, cloud, AI, and application solutions for today and well into the future with robust security components for your organization.What you will learn  Create your own architectures and analyze different models Understand strategies for creating architectures for environments and applications Discover approaches to documentation using repeatable approaches and tools Discover different communication techniques for designs, goals, and requirements Focus on implementation strategies for designs that help reduce risk Apply architectural discipline to your organization using best practices  Who this book is forThis book is for new as well as seasoned cybersecurity architects looking to explore and polish their cybersecurity architecture skills. 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