The Datacenter as a Computer: Designing Warehouse-Scale Machines, Third Edition

Morgan & Claypool Publishers
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This book describes warehouse-scale computers (WSCs), the computing platforms that power cloud computing and all the great web services we use every day. It discusses how these new systems treat the datacenter itself as one massive computer designed at warehouse scale, with hardware and software working in concert to deliver good levels of internet service performance. The book details the architecture of WSCs and covers the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. Each chapter contains multiple real-world examples, including detailed case studies and previously unpublished details of the infrastructure used to power Google's online services. Targeted at the architects and programmers of today's WSCs, this book provides a great foundation for those looking to innovate in this fascinating and important area, but the material will also be broadly interesting to those who just want to understand the infrastructure powering the internet.

The third edition reflects four years of advancements since the previous edition and nearly doubles the number of pictures and figures. New topics range from additional workloads like video streaming, machine learning, and public cloud to specialized silicon accelerators, storage and network building blocks, and a revised discussion of data center power and cooling, and uptime. Further discussions of emerging trends and opportunities ensure that this revised edition will remain an essential resource for educators and professionals working on the next generation of WSCs.

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Additional Information

Publisher
Morgan & Claypool Publishers
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Published on
Oct 29, 2018
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Pages
189
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ISBN
9781681734347
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Best For
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Language
English
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Genres
Computers / Computer Engineering
Computers / Computer Science
Computers / Databases / General
Computers / Systems Architecture / General
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Content Protection
This content is DRM protected.
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Over 50 recipes on the core features of Apache Mesos and running big data frameworks in MesosAbout This BookLearn to install and configure Mesos to suit the needs of your organizationFollow step-by-step instructions to deploy application frameworks on top of Mesos, saving you many hours of research and trial and errorUse this practical guide packed with powerful recipes to implement Mesos and easily integrate it with other application frameworksWho This Book Is For

This book is for system administrators, engineers, and big data programmers. Basic experience with big data technologies such as Hadoop or Spark would be useful but is not essential. A working knowledge of Apache Mesos is expected.

What You Will LearnSet up Mesos on different operating systemsUse the Marathon and Chronos frameworks to manage multiple applicationsWork with Mesos and DockerIntegrate Mesos with Spark and other big data frameworksUse networking features in Mesos for effective communication between containersConfigure Mesos for high availability using ZookeeperSecure your Mesos clusters with SASL and Authorization ACLsSolve everyday problems and discover the best practicesIn Detail

Apache Mesos is open source cluster sharing and management software. Deploying and managing scalable applications in large-scale clustered environments can be difficult, but Apache Mesos makes it easier with efficient resource isolation and sharing across application frameworks.

The goal of this book is to guide you through the practical implementation of the Mesos core along with a number of Mesos supported frameworks. You will begin by installing Mesos and then learn how to configure clusters and maintain them. You will also see how to deploy a cluster in a production environment with high availability using Zookeeper.

Next, you will get to grips with using Mesos, Marathon, and Docker to build and deploy a PaaS. You will see how to schedule jobs with Chronos. We'll demonstrate how to integrate Mesos with big data frameworks such as Spark, Hadoop, and Storm. Practical solutions backed with clear examples will also show you how to deploy elastic big data jobs.

You will find out how to deploy a scalable continuous integration and delivery system on Mesos with Jenkins. Finally, you will configure and deploy a highly scalable distributed search engine with ElasticSearch.

Throughout the course of this book, you will get to know tips and tricks along with best practices to follow when working with Mesos.

Style and approach

This step-by-step guide is packed with powerful recipes on using Apache Mesos and shows its integration with containers and big data frameworks.

Multithreaded architectures now appear across the entire range of computing devices, from the highest-performing general purpose devices to low-end embedded processors. Multithreading enables a processor core to more effectively utilize its computational resources, as a stall in one thread need not cause execution resources to be idle. This enables the computer architect to maximize performance within area constraints, power constraints, or energy constraints. However, the architectural options for the processor designer or architect looking to implement multithreading are quite extensive and varied, as evidenced not only by the research literature but also by the variety of commercial implementations. This book introduces the basic concepts of multithreading, describes a number of models of multithreading, and then develops the three classic models (coarse-grain, fine-grain, and simultaneous multithreading) in greater detail. It describes a wide variety of architectural and software design tradeoffs, as well as opportunities specific to multithreading architectures. Finally, it details a number of important commercial and academic hardware implementations of multithreading. Table of Contents: Introduction / Multithreaded Execution Models / Coarse-Grain Multithreading / Fine-Grain Multithreading / Simultaneous Multithreading / Managing Contention / New Opportunities for Multithreaded Processors / Experimentation and Metrics / Implementations of Multithreaded Processors / Conclusion
Today, computer-system optimization, at both the hardware and software levels, must consider the details of the memory system in its analysis; failing to do so yields systems that are increasingly inefficient as those systems become more complex. This lecture seeks to introduce the reader to the most important details of the memory system; it targets both computer scientists and computer engineers in industry and in academia. Roughly speaking, computer scientists are the users of the memory system and computer engineers are the designers of the memory system. Both can benefit tremendously from a basic understanding of how the memory system really works: the computer scientist will be better equipped to create algorithms that perform well and the computer engineer will be better equipped to design systems that approach the optimal, given the resource limitations. Currently, there is consensus among architecture researchers that the memory system is "the bottleneck," and this consensus has held for over a decade. Somewhat inexplicably, most of the research in the field is still directed toward improving the CPU to better tolerate a slow memory system, as opposed to addressing the weaknesses of the memory system directly. This lecture should get the bulk of the computer science and computer engineering population up the steep part of the learning curve. Not every CS/CE researcher/developer needs to do work in the memory system, but, just as a carpenter can do his job more efficiently if he knows a little of architecture, and an architect can do his job more efficiently if he knows a little of carpentry, giving the CS/CE worlds better intuition about the memory system should help them build better systems, both software and hardware. Table of Contents: Primers / It Must Be Modeled Accurately / ...\ and It Will Change Soon
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