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The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

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As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and so As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and software resources in these facilities must work in concert to efficiently deliver good levels of Internet service performance, something that can only be achieved by a holistic approach to their design and deployment. In other words, we must treat the datacenter itself as one massive warehouse-scale computer (WSe. We describe the architecture of WSCs, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. We hope it will be useful to architects and programmers of today's WSCs, as well as those of future many-core platforms which may one day implement the equivalent of today's WSCs on a single board. Table of Contents: Introduction / Workloads and Software Infrastructure / Hardware Building Blocks / Datacenter Basics / Energy and Power Efficiency / Modeling Costs / Dealing with Failures and Repairs / Closing Remarks


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As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and so As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and software resources in these facilities must work in concert to efficiently deliver good levels of Internet service performance, something that can only be achieved by a holistic approach to their design and deployment. In other words, we must treat the datacenter itself as one massive warehouse-scale computer (WSe. We describe the architecture of WSCs, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. We hope it will be useful to architects and programmers of today's WSCs, as well as those of future many-core platforms which may one day implement the equivalent of today's WSCs on a single board. Table of Contents: Introduction / Workloads and Software Infrastructure / Hardware Building Blocks / Datacenter Basics / Energy and Power Efficiency / Modeling Costs / Dealing with Failures and Repairs / Closing Remarks

57 review for The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

  1. 5 out of 5

    David Chou

    An awesome book explaining not only the physical side of the datacenter, but also the requirements of the software infrastructure that needs to be built on top for developers to efficiently leverage the datacenter resources. This book is a must read for any infrastructure engineer.

  2. 4 out of 5

    heidi

    I took my time to finish and truly understand the contents of this book because I was reading it for work. Our purpose was slightly different, which was to build a computational simulation server, much smaller than warehouse scale machines obviously. However I found this book very comprehensive, and perfect for beginners in parallel computing. It covers issues I didn't think of in the beginning of our project, such as heat generation and power supply (these are more relevant to hardware and I on I took my time to finish and truly understand the contents of this book because I was reading it for work. Our purpose was slightly different, which was to build a computational simulation server, much smaller than warehouse scale machines obviously. However I found this book very comprehensive, and perfect for beginners in parallel computing. It covers issues I didn't think of in the beginning of our project, such as heat generation and power supply (these are more relevant to hardware and I only know a bit of numerical computing). There are helpful "management" contents as well such as how to calculate ROI and Capex vs Opex. I totally recommend this book for any engineer who suddenly finds herself having to build a tiny "cluster" at work and have to start with googling shit up… That said, because this book is for beginners you'll need to start digging through the references listed at the back and read more from there.

  3. 5 out of 5

    Ralph N

    Good for beginners of parallel computing.

  4. 4 out of 5

    Trung

    Feels like a report paper (short and dense with statistics) than a book. There are a lot of insights into how Google operates, but it is a bit too short.

  5. 4 out of 5

    Wong Wen Hao

  6. 4 out of 5

    Kevin Ferret

  7. 4 out of 5

    Fred Dinkler

  8. 5 out of 5

    Gareth Davis

  9. 4 out of 5

    Christian Witts

  10. 4 out of 5

    AG

  11. 4 out of 5

    Miguel Angel

  12. 4 out of 5

    Dexter

  13. 5 out of 5

    Kevin

  14. 4 out of 5

    Marc Donner

  15. 5 out of 5

    D. Ramakrishnan

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    Jeff

  17. 4 out of 5

    Subhajit Das

  18. 4 out of 5

    Chelsea

  19. 5 out of 5

    Christopher Maxwell

  20. 5 out of 5

    Roman Leventov

  21. 4 out of 5

    Amin Khoshnood

  22. 5 out of 5

    Nazmul Ahmed Noyon

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    Jakob Thomsen

  24. 4 out of 5

    Tine Hutchison

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    Pyang

  26. 4 out of 5

    Sebastien

  27. 5 out of 5

    wszhou

  28. 5 out of 5

    Federico Fregosi

  29. 5 out of 5

    Kevin Li

  30. 4 out of 5

    zlu

  31. 5 out of 5

    Aaron Wolfson

  32. 4 out of 5

    Adam Schepis

  33. 5 out of 5

    Chris

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    Mike Fisher

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    James Waldrop

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    Sean Keery

  37. 4 out of 5

    David Yeo

  38. 5 out of 5

    Rui Natário

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    Nuno Guerreiro

  40. 4 out of 5

    Victor

  41. 4 out of 5

    Tim Cowlishaw

  42. 5 out of 5

    Dan Winkler

  43. 5 out of 5

    Andrew

  44. 4 out of 5

    Mr. Tom

  45. 4 out of 5

    MICHAEL RINGGAARD

  46. 4 out of 5

    Bianca Gibson

  47. 5 out of 5

    Chris

  48. 5 out of 5

    Mike Murray

  49. 5 out of 5

    S.yadhunandan

  50. 5 out of 5

    Brad

  51. 4 out of 5

    Anand Purohit

  52. 4 out of 5

    Jeongho Park

  53. 4 out of 5

    John

  54. 5 out of 5

    Jake Schmidt

  55. 4 out of 5

    Keegan

  56. 5 out of 5

    Michael

  57. 4 out of 5

    Marcin Kania

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