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Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows
Practical patterns for scaling machine learning from your laptop to a distributed cluster. Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns, you’ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Learning to build distributed systems is hard, especially if they are large scale. It's not that there is a lack of information out there. You can find academic papers, engineering blogs, and even books on the subject. The problem is that the available information is spread out all over the place, and if you were to put it on a spectrum from theory to practice, you would find a lot of material at the two ends but not much in the middle. That is why I decided to write a book that brings together the core theoretical and practical concepts of distributed systems so that you don't have to spend hours connecting the dots. This book will guide you through the fundamentals of large-scale distributed systems, with just enough details and external references to dive deeper. This is the guide I wished existed when I first started out, based on my experience building large distributed systems that scale to millions of requests per second and billions of devices. If you are a developer working on the backend of web or mobile applications (or would like to be!), this book is for you. When building distributed applications, you need to be familiar with the network stack, data consistency models, scalability and reliability patterns, observability best practices, and much more. Although you can build applications without knowing much of that, you will end up spending hours debugging and re-architecting them, learning hard lessons that you could have acquired in a much faster and less painful way. However, if you have several years of experience designing and building highly available and fault-tolerant applications that scale to millions of users, this book might not be for you. As an expert, you are likely looking for depth rather than breadth, and this book focuses more on the latter since it would be impossible to cover the field otherwise. The second edition is a complete rewrite of the previous edition. Every page of the first edition has been reviewed and where appropriate reworked, with new topics covered for the first time.
A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.
Pattern-Oriented Software Architecture (POSA) Volume 4 furnishes significant information about a pattern language for distributed computing. The book walks you through the best practices and introduces you to key areas of building distributed software systems. POSA 4 connects many stand-alone patterns, pattern collections and pattern languages from the existing body of literature found in the POSA series. The panel of experts provides you with a consistent and coherent holistic view on the craft of building distributed systems. · On Patterns and Pattern Languages· On Distributed Systems· On the Pattern Language· Warehouse Management Process Control· Base-line Architecture· Communication Middleware· Warehouse Topology· The Story Behind the Pattern Story· From Mud to Structure· Distribution Infrastructure· Event Demultiplexing and Dispatching· Interface Partitioning· Component Partitioning· Application Control· Concurrency· Synchronization· Object Interaction· Adaptation and Extension· Modal Behavior· Resource Management· Database Access· A Departing Thought
Patterns and Skeletons for Parallel and Distributed Computing is a unique survey of research work in high-level parallel and distributed computing over the past ten years. Comprising contributions from the leading researchers in Europe and the US, it looks at interaction patterns and their role in parallel and distributed processing, and demonstrates for the first time the link between skeletons and design patterns. It focuses on computation and communication structures that are beyond simple message-passing or remote procedure calling, and also on pragmatic approaches that lead to practical design and programming methodologies with their associated compilers and tools. The book is divided into two parts which cover: skeletons-related material such as expressing and composing skeletons, formal transformation, cost modelling and languages, compilers and run-time systems for skeleton-based programming.- design patterns and other related concepts, applied to other areas such as real-time, embedded and distributed systems. It will be an essential reference for researchers undertaking new projects in this area, and will also provide useful background reading for advanced undergraduate and postgraduate courses on parallel or distributed system design.
Designing Distributed Control Systems presents 80 patterns for designing distributed machine control system software architecture (forestry machinery, mining drills, elevators, etc.). These patterns originate from state-of-the-art systems from market-leading companies, have been tried and tested, and will address typical challenges in the domain, such as long lifecycle, distribution, real-time and fault tolerance. Each pattern describes a separate design problem that needs to be solved. Solutions are provided, with consequences and trade-offs. Each solution will enable piecemeal growth of the design. Finding a solution is easy, as the patterns are divided into categories based on the problem field the pattern tackles. The design process is guided by different aspects of quality, such as performance and extendibility, which are included in the pattern descriptions. The book also contains an example software architecture designed by leading industry experts using the patterns in the book. The example system introduces the reader to the problem domain and demonstrates how the patterns can be used in a practical system design process. The example architecture shows how useful a toolbox the patterns provide for both novices and experts, guiding the system design process from its beginning to the finest details. Designing distributed machine control systems with patterns ensures high quality in the final product. High-quality systems will improve revenue and guarantee customer satisfaction. As market need changes, the desire to produce a quality machine is not only a primary concern, there is also a need for easy maintenance, to improve efficiency and productivity, as well as the growing importance of environmental values; these all impact machine design. The software of work machines needs to be designed with these new requirements in mind. Designing Distributed Control Systems presents patterns to help tackle these challenges. With proven methodologies from the expert author team, they show readers how to improve the quality and efficiency of distributed control systems.
Distributed Systems: An Algorithmic Approach, Second Edition provides a balanced and straightforward treatment of the underlying theory and practical applications of distributed computing. As in the previous version, the language is kept as unobscured as possible—clarity is given priority over mathematical formalism. This easily digestible text: Features significant updates that mirror the phenomenal growth of distributed systems Explores new topics related to peer-to-peer and social networks Includes fresh exercises, examples, and case studies Supplying a solid understanding of the key principles of distributed computing and their relationship to real-world applications, Distributed Systems: An Algorithmic Approach, Second Edition makes both an ideal textbook and a handy professional reference.
Middleware is the bridge that connects distributed applications across different physical locations, with different hardware platforms, network technologies, operating systems, and programming languages. This book describes middleware from two different perspectives: from the viewpoint of the systems programmer and from the viewpoint of the applications programmer. It focuses on the use of open source solutions for creating middleware and the tools for developing distributed applications. The design principles presented are universal and apply to all middleware platforms, including CORBA and Web Services. The authors have created an open-source implementation of CORBA, called MICO, which is freely available on the web. MICO is one of the most successful of all open source projects and is widely used by demanding companies and institutions, and has also been adopted by many in the Linux community.* Provides a comprehensive look at the architecture and design of middlewarethe bridge that connects distributed software applications* Includes a complete, commercial-quality open source middleware system written in C++* Describes the theory of the middleware standard CORBA as well as how to implement a design using open source techniques
A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation. This book offers students and researchers a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models. It avoids mathematical argumentation, often a stumbling block for students, teaching algorithmic thought rather than proofs and logic. This approach allows the student to learn a large number of algorithms within a relatively short span of time. Algorithms are explained through brief, informal descriptions, illuminating examples, and practical exercises. The examples and exercises allow readers to understand algorithms intuitively and from different perspectives. Proof sketches, arguing the correctness of an algorithm or explaining the idea behind fundamental results, are also included. An appendix offers pseudocode descriptions of many algorithms. Distributed algorithms are performed by a collection of computers that send messages to each other or by multiple software threads that use the same shared memory. The algorithms presented in the book are for the most part “classics,” selected because they shed light on the algorithmic design of distributed systems or on key issues in distributed computing and concurrent programming. Distributed Algorithms can be used in courses for upper-level undergraduates or graduate students in computer science, or as a reference for researchers in the field.