Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.
The material is organized according to the system model—first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.
The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms—to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.
About the author: Nancy A. Lynch is a professor of electrical engineering and computer science at MIT and heads MIT's Theory of Distributed Systems research group. She is the author of numerous research articles about distributed algorithms and impossibility results, and about formal modeling and verification of distributed systems.
A basic knowledge of GNU/Linux, and storage systems, and server components is assumed. If you have no experience of software-defined storage solutions and Ceph, but are eager to learn about them, this is the book for you.What You Will LearnThe limitations of existing systems and why you should use Ceph as a storage solutionFamiliarity with Ceph's architecture, components, and servicesInstant deployment and testing of Ceph within a Vagrant and VirtualBox environmentCeph operations including maintenance, monitoring, and troubleshootingStorage provisioning of Ceph's block, object, and filesystem servicesIntegrate Ceph with OpenStackAdvanced topics including erasure coding, CRUSH maps, and performance tuningBest practices for your Ceph clustersIn Detail
Learning Ceph, Second Edition will give you all the skills you need to plan, deploy, and effectively manage your Ceph cluster. You will begin with the first module, where you will be introduced to Ceph use cases, its architecture, and core projects. In the next module, you will learn to set up a test cluster, using Ceph clusters and hardware selection. After you have learned to use Ceph clusters, the next module will teach you how to monitor cluster health, improve performance, and troubleshoot any issues that arise. In the last module, you will learn to integrate Ceph with other tools such as OpenStack, Glance, Manila, Swift, and Cinder.
By the end of the book you will have learned to use Ceph effectively for your data storage requirements.Style and approach
This step-by-step guide, including use cases and examples, not only helps you to easily use Ceph but also demonstrates how you can use it to solve any of your server or drive storage issues.
In this book we explore IBM System Storage solutions for Business Continuity, within the three segments of Continuous Availability, Rapid Recovery, and Backup and Restore. We position these solutions within the Business Continuity tiers.
We describe, in general, the solutions available in each segment, then present some more detail on many of the products. In each case, the reader is pointed to sources of more information.
Recovery can be performed at the volume or copy pool level. This capability is designed to work specifically with DB2® Version 8 or later. With DFSMShsm fast replication, the backup and recovery of DB2 copy pools can be managed by DFSMShsm. DFSMShsm fast replication provides a quick, easy-to-use backup and recovery solution.
This IBM® Redbooks® publication consists of a technical overview of the DFSMShsm fast replication function in z/OS® V1R12 Data Facility Storage Management Subsystem (DFSMS). It provides you with the information that you need to understand and evaluate the function, with practical implementation hints and tips.
This book is written for storage professionals, database administrators, and system programmers who have experience with the components of DFSMS. It provides sufficient information for you to implement the DFSMShsm fast replication function in your storage environment.
The book is divided into 11 chapters, which cover the following:Overview of transaction processing application and system structureSoftware abstractions found in transaction processing systemsArchitecture of multitier applications and the functions of transactional middleware and database serversQueued transaction processing and its internals, with IBM's Websphere MQ and Oracle's Stream AQ as examplesBusiness process management and its mechanismsDescription of the two-phase locking function, B-tree locking and multigranularity locking used in SQL database systems and nested transaction lockingSystem recovery and its failuresTwo-phase commit protocolComparison between the tradeoffs of replicating servers versus replication resourcesTransactional middleware products and standardsFuture trends, such as cloud computing platforms, composing scalable systems using distributed computing components, the use of flash storage to replace disks and data streams from sensor devices as a source of transaction requests.
The text meets the needs of systems professionals, such as IT application programmers who construct TP applications, application analysts, and product developers. The book will also be invaluable to students and novices in application programming.Complete revision of the classic "non mathematical" transaction processing reference for systems professionals. Updated to focus on the needs of transaction processing via the Internet-- the main focus of business data processing investments, via web application servers, SOA, and important new TP standards. Retains the practical, non-mathematical, but thorough conceptual basis of the first edition.
The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool.
This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling.Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templatesDevelops ideas for creating consistent approaches to high quality data models
Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.
The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projectsOffers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
This book is recommended for IT professionals, including those in consulting, working on systems that will deliver better knowledge management capability. This includes people in these positions: data architects, data analysts, SOA architects, metadata analysts, repository (metadata data warehouse) managers as well as vendors that have a metadata component as part of their systems or tools.First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and managementWritten by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projectsVery practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the wayIncludes sample unstructured metadata for use in self-testing and developing skills