Create, deploy, and manage applications at scale using SRE principles Key FeaturesBuild and run highly available, scalable, and secure softwareExplore abstract SRE in a simplified and streamlined wayEnhance the reliability of cloud environments through SRE enhancementsBook Description Site reliability engineering (SRE) is being touted as the most competent paradigm in establishing and ensuring next-generation high-quality software solutions. This book starts by introducing you to the SRE paradigm and covers the need for highly reliable IT platforms and infrastructures. As you make your way through the next set of chapters, you will learn to develop microservices using Spring Boot and make use of RESTful frameworks. You will also learn about GitHub for deployment, containerization, and Docker containers. Practical Site Reliability Engineering teaches you to set up and sustain containerized cloud environments, and also covers architectural and design patterns and reliability implementation techniques such as reactive programming, and languages such as Ballerina and Rust. In the concluding chapters, you will get well-versed with service mesh solutions such as Istio and Linkerd, and understand service resilience test practices, API gateways, and edge/fog computing. By the end of this book, you will have gained experience on working with SRE concepts and be able to deliver highly reliable apps and services. What you will learnUnderstand how to achieve your SRE goalsGrasp Docker-enabled containerization conceptsLeverage enterprise DevOps capabilities and Microservices architecture (MSA)Get to grips with the service mesh concept and frameworks such as Istio and LinkerdDiscover best practices for performance and resiliencyFollow software reliability prediction approaches and enable patternsUnderstand Kubernetes for container and cloud orchestrationExplore the end-to-end software engineering process for the containerized worldWho this book is for Practical Site Reliability Engineering helps software developers, IT professionals, DevOps engineers, performance specialists, and system engineers understand how the emerging domain of SRE comes handy in automating and accelerating the process of designing, developing, debugging, and deploying highly reliable applications and services.
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.
Numerous advancements are being brought in and incorporated into the cloud domain with the aim of realizing a trove of deeper and decisive automations. Rather than discussing the cloud paradigm in isolation, this fully updated text examines how cloud computing can work collaboratively with other computing models to meet the needs of evolving trends.This multi-dimensional approach encompasses the challenges of fulfilling the storage requirements of big data, the use of the cloud as a remote server for Internet of Things and sensor networks, and an investigation of how cloud computing is interlinked with other established computing phenomenon such as edge computing. New chapters illustrate the distinct ideals of the cloud-native computing, proclaimed as the next-generation cloud computing paradigm. Topics and features: Includes learning objectives, motivating questions, and self-test exercises Introduces the underlying concepts, fundamental features, and key technological foundations of cloud computing Examines how enterprise networking and cloud networking can work together to achieve business goals Reviews the different types of cloud storage available to address the evolution of data and the need for digitization Discusses the challenges and approaches to implementing cloud governance, security, and the hot topic of cloud management Describes the details of cloud migration, the crucial role of monitoring in optimizing the cloud, and the basics of disaster recovery using cloud infrastructure This technically rigorous, yet simple-to-follow textbook is an ideal resource for graduate courses on cloud computing. Professional software developers and cloud architects will also find the work to be an invaluable reference.
800x600 The Concise Introduction to Modern SOA: High-Value Approaches, Innovative Technologies, Proven Use Cases After a decade of innovation in technology and practice, SOA is now a mainstream computing discipline, capable of transforming IT enterprises and optimizing business automation. In Next Generation SOA, top-selling SOA author Thomas Erl and a team of experts present a plain-English tour of SOA, service-orientation, and the key service technologies being used to build sophisticated contemporary service-oriented solutions. The starting point for today's IT professionals, this concise guide distills the increasingly growing and diverse field of service-oriented architecture and the real-world practice of building powerful service-driven systems. Accessible and jargon-free, this book intentionally avoids technical details to provide easy-to-understand, introductory coverage of the following topics: Services, service-orientation, and service-oriented computing: what they are and how they have evolved How SOA and service-orientation change businesses and transform IT culture, priorities, and technology decisions How services are defined and composed to solve a wide spectrum of business problems Deep implications of the service-orientation paradigm--illuminated through an annotation of the classic SOA Manifesto Traditional and contemporary service technologies and architectures How clouds and virtualization support the scalability and reliability of services-based solutions SOA-based industry models, from enterprise service to global trader A detailed case study: how real enterprises bring together contemporary SOA practices, models, and technologies Next Generation SOA will be indispensable to wide audiences of business decision makers and technologists--including architects, developers, managers, executives, strategists, consultants, and researchers. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4
Create, deploy, and manage applications at scale using SRE principles Key FeaturesBuild and run highly available, scalable, and secure softwareExplore abstract SRE in a simplified and streamlined wayEnhance the reliability of cloud environments through SRE enhancementsBook Description Site reliability engineering (SRE) is being touted as the most competent paradigm in establishing and ensuring next-generation high-quality software solutions. This book starts by introducing you to the SRE paradigm and covers the need for highly reliable IT platforms and infrastructures. As you make your way through the next set of chapters, you will learn to develop microservices using Spring Boot and make use of RESTful frameworks. You will also learn about GitHub for deployment, containerization, and Docker containers. Practical Site Reliability Engineering teaches you to set up and sustain containerized cloud environments, and also covers architectural and design patterns and reliability implementation techniques such as reactive programming, and languages such as Ballerina and Rust. In the concluding chapters, you will get well-versed with service mesh solutions such as Istio and Linkerd, and understand service resilience test practices, API gateways, and edge/fog computing. By the end of this book, you will have gained experience on working with SRE concepts and be able to deliver highly reliable apps and services. What you will learnUnderstand how to achieve your SRE goalsGrasp Docker-enabled containerization conceptsLeverage enterprise DevOps capabilities and Microservices architecture (MSA)Get to grips with the service mesh concept and frameworks such as Istio and LinkerdDiscover best practices for performance and resiliencyFollow software reliability prediction approaches and enable patternsUnderstand Kubernetes for container and cloud orchestrationExplore the end-to-end software engineering process for the containerized worldWho this book is for Practical Site Reliability Engineering helps software developers, IT professionals, DevOps engineers, performance specialists, and system engineers understand how the emerging domain of SRE comes handy in automating and accelerating the process of designing, developing, debugging, and deploying highly reliable applications and services.
Cloud Enterprise Architecture examines enterprise architecture (EA) in the context of the surging popularity of Cloud computing. It explains the different kinds of desired transformations the architectural blocks of EA undergo in light of this strategically significant convergence. Chapters cover each of the contributing architectures of EA-business, information, application, integration, security, and technology-illustrating the current and impending implications of the Cloud on each. Discussing the implications of the Cloud paradigm on EA, the book details the perceptible and positive changes that will affect EA design, governance, strategy, management, and sustenance. The author ties these topics together with chapters on Cloud integration and composition architecture. He also examines the Enterprise Cloud, Federated Clouds, and the vision to establish the InterCloud. Laying out a comprehensive strategy for planning and executing Cloud-inspired transformations, the book: Explains how the Cloud changes and affects enterprise architecture design, governance, strategy, management, and sustenance Presents helpful information on next-generation Cloud computing Describes additional architectural types such as enterprise-scale integration, security, management, and governance architectures This book is an ideal resource for enterprise architects, Cloud evangelists and enthusiasts, and Cloud application and service architects. Cloud center administrators, Cloud business executives, managers, and analysts will also find the book helpful and inspirational while formulating appropriate mechanisms and schemes for sound modernization and migration of traditional applications to Cloud infrastructures and platforms.
Explore the cloud-native paradigm for event-driven and service-oriented applications In Cloud-Native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications, a team of distinguished professionals delivers a comprehensive and insightful treatment of cloud-native computing technologies and tools. With a particular emphasis on the Kubernetes platform, as well as service mesh and API gateway solutions, the book demonstrates the need for reliability assurance in any distributed environment. The authors explain the application engineering and legacy modernization aspects of the technology at length, along with agile programming models. Descriptions of MSA and EDA as tools for accelerating software design and development accompany discussions of how cloud DevOps tools empower continuous integration, delivery, and deployment. Cloud-Native Computing also introduces proven edge devices and clouds used to construct microservices-centric and real-time edge applications. Finally, readers will benefit from: Thorough introductions to the demystification of digital transformation Comprehensive explorations of distributed computing in the digital era, as well as reflections on the history and technological development of cloud computing Practical discussions of cloud-native computing and microservices architecture, as well as event-driven architecture and serverless computing In-depth examinations of the Akka framework as a tool for concurrent and distributed applications development Perfect for graduate and postgraduate students in a variety of IT- and cloud-related specialties, Cloud-Native Computing also belongs in the libraries of IT professionals and business leaders engaged or interested in the application of cloud technologies to various business operations.
This reader-friendly textbook presents a comprehensive overview of the essential aspects of cloud computing, from the origin of the field to the latest developments. Rather than merely discussing the cloud paradigm in isolation, the text also examines how cloud computing can work collaboratively with other computing models to meet the needs of evolving computing trends. This multi-dimensional approach encompasses the challenges of fulfilling the storage requirements of big data, the use of the cloud as a remote server for Internet of Things and sensor networks, and an investigation of how cloud computing is interlinked with edge, fog and mist computing, among other illuminating perspectives. Topics and features: includes learning objectives, motivating questions, and self-test exercises in every chapter; presents an introduction to the underlying concepts, fundamental features, and key technological foundations of cloud computing; examines how enterprise networking and cloud networking can work together to achieve business goals; reviews the different types of cloud storage available to address the evolution of data and the need for digitization; discusses the challenges and approaches to implementing cloud security, and the hot topic of cloud management; highlights the value of cloud brokerage capabilities, and explains the importance of cloud orchestration in multi-cloud environments; describes the details of cloud migration, the crucial role of monitoring in optimizing the cloud, and the basics of disaster recovery using cloud infrastructure. This technically rigorous yet simple-to-follow textbook is an ideal resource for graduate courses on cloud computing. Professional software developers and cloud architects will also find the work to be an invaluable reference.
Numerous advancements are being brought in and incorporated into the cloud domain with the aim of realizing a trove of deeper and decisive automations. Rather than discussing the cloud paradigm in isolation, this fully updated text examines how cloud computing can work collaboratively with other computing models to meet the needs of evolving trends.This multi-dimensional approach encompasses the challenges of fulfilling the storage requirements of big data, the use of the cloud as a remote server for Internet of Things and sensor networks, and an investigation of how cloud computing is interlinked with other established computing phenomenon such as edge computing. New chapters illustrate the distinct ideals of the cloud-native computing, proclaimed as the next-generation cloud computing paradigm. Topics and features: Includes learning objectives, motivating questions, and self-test exercises Introduces the underlying concepts, fundamental features, and key technological foundations of cloud computing Examines how enterprise networking and cloud networking can work together to achieve business goals Reviews the different types of cloud storage available to address the evolution of data and the need for digitization Discusses the challenges and approaches to implementing cloud governance, security, and the hot topic of cloud management Describes the details of cloud migration, the crucial role of monitoring in optimizing the cloud, and the basics of disaster recovery using cloud infrastructure This technically rigorous, yet simple-to-follow textbook is an ideal resource for graduate courses on cloud computing. Professional software developers and cloud architects will also find the work to be an invaluable reference.
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.