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Mega Data Centers: Market Shares, Strategies, and Forecasts, Worldwide, 2017 to 2023
Publication Date Feb 2017
Product Type Report
Pages 418
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WinterGreen Research announces that it has published a new study Mega Data Centers: Market Shares, Strategy, and Forecasts, Worldwide, 2017 to 2023. The 2017 study has 418 pages, 175 tables and figures. Worldwide mega data center markets are poised to achieve significant growth with the Internet of Things (IoT), the wireless data explosion, and increased use of video creating more digital data to be managed. The use of smartphone apps and headsets or glasses that are augmented reality platforms to project digital information as images onto a game image or a work situation create a lot more data to be managed.

The mega data centers are different from cloud computing in general, and different from the existing enterprise linear computing data centers. The mega data centers are handling infrastructure automatically, eliminating manual process for infrastructure, creating a separate application layer where all the work gets done. The operative nomenclature is containers. The operative software is orchestration.

Mega centers are moving data at the speed of light. This represents a huge change in computing going forward, virtually all the existing enterprise data centers are obsolete because moving data at the speed of light demands different infrastructure from moving data using existing cabling that is not fiber. This study addresses these issues. As enterprises and cloud vendors build data centers with the capacity to move data inside the data center at 400 GB per second, more data can be managed, costs will continue to plummet, and efficiency goes up.

The mega data centers are needed to handle all manner of new quantities of digital information. All manner of devices will have electronics to generate digital data turning it into monitored digital information with alerts to permit response to streams of information that demand response, as for example cardiac data going into a cardiac monitor in a hospital intensive care unit. New monitoring situations emerge. The connected home will provide security on every door, window, and room with alerts that can be sent to and accessed from a smart phone. The refrigerators and heaters will be connected and be equipped with rule based logic to detect problems and send relevant info so they can be turned on and off remotely.

In industry, work flow will be automated beyond single process to multi process information management. The sheer scale of the fabric is fundamentally changing how the market leaders monitor and troubleshoot the data center. Components and links behave the same. Baselines and outliers are key to active auditing for problems. Priority-driven alerting and auto-remediation are in place.

Amazon (AWS), Microsoft, Google, and Facebook data centers are in a class by themselves, they have functioning fully automatic, self-healing, networked mega datacenters that operate at fiber optic speeds to create a fabric that can access any node because there are multiple pathways to every compute node. Five of the largest-scale internet firms – Apple, Google, Microsoft, Amazon and Facebook – continue to invest heavily in building out datacenters globally, with capital spending at the companies totaling more than $115 billion over the past 14 quarters. In Q2 2016, capex at the five companies increased 9.7% sequentially and 60.5% over the same quarter two years ago. The pace of capex at large-scale internet firms in general has been increasing over the past several years.

As more people connect and as Facebook creates new products and services, this type of traffic is a small proportion of all the data that needs to be managed. Inside the Facebook data centers machine to machine traffic is several orders of magnitude larger than what goes out to the Internet.

This is the 691st report in a series of primary market research reports that provide forecasts in communications, telecommunications, the Internet, computer, software, telephone equipment, health equipment, and energy. Automated process and significant growth potential are a priority in topic selection. The project leaders take direct responsibility for writing and preparing each report. They have significant experience preparing industry studies. They are supported by a team, each person with specific research tasks and proprietary automated process database analytics. Forecasts are based on primary research and proprietary data bases.

The primary research is conducted by talking to customers, distributors and companies. The survey data is not enough to make accurate assessment of market size, so WinterGreen Research looks at the value of shipments and the average price to achieve market assessments. Our track record in achieving accuracy is unsurpassed in the industry. We are known for being able to develop accurate market shares and projections. This is our specialty.

The analyst process is concentrated on getting good market numbers. This process involves looking at the markets from several different perspectives, including vendor shipments. The interview process is an essential aspect as well. We do have a lot of granular analysis of the different shipments by vendor in the study and addenda prepared after the study was published if that is appropriate.

Forecasts reflect analysis of the market trends in the segment and related segments. Unit and dollar shipments are analyzed through consideration of dollar volume of each market participant in the segment. Installed base analysis and unit analysis is based on interviews and an information search. Market share analysis includes conversations with key customers of products, industry segment leaders, marketing directors, distributors, leading market participants, opinion leaders, and companies seeking to develop measurable market share.

Over 200 in depth interviews are conducted for each report with a broad range of key participants and industry leaders in the market segment. We establish accurate market forecasts based on economic and market conditions as a base. Use input/output ratios, flow charts, and other economic methods to quantify data. Use in-house analysts who meet stringent quality standards.

MEGA DATA CENTERS EXECUTIVE SUMMARY
Mega Data Center Scale and Automation
Mega Data Centers Have Stepped In To Do The Job Of Automated Process
Cloud 2.0 Mega Data Center Fabric Implementation
Cloud 2.0 Mega Data Center Different from the Hyperscale Cloud
Cloud 2.0 Mega Data Center Automatic Rules and Push-Button Actions
Making Individual Circuits And Devices Unimportant Is A Primary Aim Of Fabric Architecture
Digital Data Expanding Exponentially, Global IP Traffic Passes Zettabyte (1000 Exabytes) Threshold
Google Kubernetes Open Source Container Control System
Google Kubernetes a Defacto Standard Container Management System
Google Shift from Bare Metal To Container Controllers
Cloud 2.0 Mega Data Center Market Driving Forces
Mega Data Center Market Shares
Cloud Datacenter, Co-Location, and Social Media Cloud, Revenue Market Shares, Dollars, Worldwide, 2016
Cloud 2.0 Mega Data Center Market Forecasts
1. MEGA DATACENTERS: MARKET DESCRIPTION AND MARKET DYNAMICS
1.1 Data Center Manager Not Career Track for CEO
1.1.1 Colocation Shared Infrastructure
1.1.2 Power and Data Center Fault Tolerance
1.2 Fiber High Bandwidth Datacenters
1.3 100 Gbps Headed For The Data Center
1.3.1 100 Gbps Adoption
1.4 Scale: Cloud 2.0 Mega Data Center Containers
1.4.1 Data Center Architectures Evolving
1.4.2 High-Performance Cloud Computing Market Segments
1.4.3 Cisco CRS-3 Core Routing Platform
1.5 Evolution of Data Center Strategy
1.6 Cabling in The Datacenter
1.6.1 Datacenter Metrics
1.6.1 Digitalization Forcing Data Centers to Evolve
1.6.2 A One-Stop Shop
1.6.3 Growing With Business
2. MEGA DATA CENTERS MARKET SHARES AND FORECASTS
2.1 Mega Data Center Scale and Automation
2.1.1 Cloud 2.0 Mega Data Center Fabric Implementation
2.1.2 Cloud 2.0 Mega Data Center Different from the Hyperscale Cloud
2.1.3 Cloud 2.0 Mega Data Center Automatic Rules and Push-Button Actions
2.1.4 Making Individual Circuits And Devices Unimportant Is A Primary Aim Of Fabric Architecture
2.1.5 Digital Data Expanding Exponentially, Global IP Traffic Passes
Zettabyte (1000 Exabytes) Threshold
2.1.6 Google Kubernetes Open Source Container Control System
2.1.7 Google Kubernetes Defacto Standard Container Management System
2.1.8 Google Shift from Bare Metal To Container Controllers
2.1.9 Cloud 2.0 Mega Data Center Market Driving Forces
2.2 Mega Data Center Market Shares
2.2.1 Cloud 2.0 Mega Datacenter Cap Ex Spending Market Shares Dollars, Worldwide, 2016
2.2.2 Amazon Capex for Cloud 2.0 Mega Data Centers
2.2.3 Amazon (AWS) Cloud
2.2.4 Amazon Datacenter Footprint
2.2.5 Cloud 2.0 Mega Data Center Social Media and Search Revenue Market Shares, Dollars, 2016
2.2.6 Microsoft Azure
2.2.7 Microsoft Data Center, Dublin, 550,000 Sf
2.2.8 Microsoft Data Center Container Area in Chicago.
2.2.9 Microsoft Quincy Data Centers, 470,000 Square Feet
2.2.10 Microsoft San Antonio Data Center, 470,000 SF
2.2.11 Microsoft 3rd Data Center in Bexar Could Employ 150
2.2.12 Microsoft Builds the Intelligent Cloud Platform
2.2.13 Microsoft's Datacenter Footprint
2.2.14 Microsoft Datacenter Footprint
2.2.15 Google Datacenter Footprint
2.2.16 Google Datacenter Footprint
2.2.17 Facebook Datacenter Footprint
2.2.18 Facebook Datacenter Footprint
2.3 Cloud 2.0 Mega Data Center Market Forecasts
2.3.1 Market Segments: Web Social Media, Web Wireless Apps, Enterprise / Business
Transactions, Co-Location, And Broadcast / Communications
2.3.2 Cloud 2.0 Mega Data Center Is Changing The Hardware And Data Center Markets
2.4 Mega-Datacenter: Internet Giants Continue To Increase Capex
2.4.1 Amazon Datacenter Footprint
2.4.2 Service Tiers and Applications
2.4.3 Cloud 2.0 Mega Data Center Segments
2.4.4 Mega Data Center Positioning
2.4.5 Cloud 2.0 Mega Data Centers
2.5 Hyperscale Datacenter Future
2.6 Data Expanding And Tools Used To Share, Store, And Analyze Evolving At
Phenomenal Rates
2.6.1 Video Traffic
2.6.2 Cisco Analysis of Business IP Traffic
2.6.3 Increasing Video Definition: By 2020, More Than 40 Percent of Connected Flat-Panel
TV Sets Will Be 4K 142
2.6.4 M2M Applications
2.6.5 Applications, For Telemedicine And Smart Car Navigation Systems, Require Greater
Bandwidth And Lower Latency
2.6.6 Explosion of Data Inside Cloud 2.0 Mega Data Center with Multi-Threading
2.6.7 Cloud 2.0 Mega Data Center Multi-Threading Automates Systems Integration
2.6.8 Fixed Broadband Speeds (in Mbps), 2015–2020
2.6.9 Internet Traffic Trends
2.6.10 Internet of Things
2.6.11 The Rise of the Converged “Digital Enterprise”
2.6.12 Enterprise Data Centers Give Way to Commercial Data Centers
2.6.13 Types of Cloud Computing
2.7 Cloud Mega Data Center Regional Market Analysis
2.7.1 Amazon, Google Detail Next Round of Cloud Data Center Launches
2.7.1 Cloud Data Centers Market in Europe
2.7.2 Cloud Data Centers Market in Ireland
2.7.3 Japanese Data Centers
3. MEGA DATA CENTER INFRASTRUCTURE DESCRIPTION
3.1 Amazon Cloud
3.1.1 Amazon AWS Regions and Availability Zones
3.1.2 Amazon Addresses Enterprise Cloud Market, Partnering With VMware
3.1.3 AWS Achieves High Availability Through Multiple Availability Zones
3.1.4 AWS Improving Continuity Replication Between Regions
3.1.5 Amazon (AWS) Meeting Compliance and Data Residency Requirements
3.1.6 AWS Step Functions Software
3.1.7 Amazon QuickSight Software
3.1.8 Amazon North America
3.1.9 AWS Server Scale
3.1.10 AWS Network Scale
3.2 Facebook
3.2.1 Dupont Fabros Constructing Second Phase In Acc7 Represents An Expanded
Relationship with Facebook
3.2.2 Facebook $1B Cloud 2.0 Mega Data Center in Texas
3.2.3 Facebook $300 Million Cloud 2.0 Mega Data Center in Iowa
3.2.4 Fort Worth Facebook Mega-Data Center
3.2.5 Facebook Forest City, N.C. Cloud 2.0 mega data center
3.2.6 Data Center Fabric, The Next-Generation Facebook Data Center Network
3.2.1 Facebook Altoona Data Center Networking Fabric
3.2.2 Facebook Clusters and Limits Of Clusters
3.2.3 Facebook Fabric
3.2.4 Facebook Network Technology
3.2.5 Facebook Fabric Gradual Scalability
3.2.6 Facebook Mega Datacenter Physical Infrastructure
3.2.7 Facebook Large Fabric Network Automation
3.2.8 Facebook Fabric Data Center Transparent Transition
3.2.9 Facebook Large-Scale Network
3.3 Google Meta Data Centers
3.3.1 Google Datacenter Network
3.3.2 Google Office Productivity Dynamic Architecture
3.3.3 Google Search Engine Dynamic Architecture
3.3.4 BigFiles
3.3.5 Repository
3.3.6 Google Clos Networks
3.3.7 Google B4 Datacenter WAN, a SDN
3.3.8 Google Programmable Access To Network Stack
3.3.9 Google Compute Engine Load Balancing
3.3.10 Google Compute Engine (GCE) TCP Stream Performance Improvements
3.3.11 Google The Dalles, Oregon Cloud 2.0 Mega Data Center
3.3.12 Lenoir, North Carolina
3.3.13 Google Hamina, Finland
3.3.14 Google Mayes County
3.3.15 Google Douglas County
3.3.16 Google Cloud 2.0 Mega Data Center St Ghislain, Belgium
3.3.17 Google Council Bluffs, Iowa Cloud 2.0 Mega Data Center
3.3.18 Google Douglas County Cloud 2.0 Mega Data Center
3.3.19 Google $300m Expansion of Existing Metro Atlanta Data Center
3.3.20 Google B4 SDN Initiative Benefits: Not Need To Be A Network Engineer To Control A
Network; Can Do It At An Application Level
3.3.21 Google Cloud 2.0 Mega Data Center in Finland
3.3.22 Google Switches Provide Scale-Out: Server And Storage Expansion
3.3.23 Google and Microsoft 25G Ethernet Consortium
3.3.24 Google Workload Definitions
3.3.25 Google Kubernetes Container
3.3.26 Google Optical Networking
3.3.27 Google Data Center Efficiency Measurements
3.3.28 Google Measuring and Improving Energy Use
3.3.29 Google Comprehensive Approach to Measuring PUE
3.3.30 Q3 2016 PUE Performance
3.4 Microsoft
3.4.1 Microsoft .Net Dynamically Defines Reusable Modules
3.4.2 Microsoft Combines Managed Modules into Assemblies
3.4.3 Microsoft Architecture Dynamic Modular Processing
3.4.4 Microsoft Builds Azure Cloud Data Centers in Canada
3.4.5 Microsoft Dublin Cloud 2.0 mega data center
3.4.6 Microsoft Data Center Largest in U.S.
3.4.7 Microsoft Crafts Homegrown Linux For Azure Switches
3.4.8 Microsoft Azure Cloud Switch
3.4.9 Microsoft Azure CTO Cloud Building
3.4.10 Microsoft Cloud 2.0 Mega Data Center Multi-Tenant Containers
3.4.11 Microsoft Managed Clustering and Container Management: Docker and Mesos
3.4.12 Kubernetes From Google or Mesos
3.4.13 Microsoft Second Generation Open Cloud Servers
3.4.14 Azure Active Directory
3.4.15 Microsoft Azure Stack Platform Brings The Suite Of Azure Services To The Corporate Datacenter
3.4.16 Hardware Foundation For Microsoft Azure Stack
4. MEGA DATACENTERS RESEARCH AND TECHNOLOGY
4.1 Enterprise IT Control Centers
4.2 Open Compute Project (OCP),
4.2.1 Microsoft Investment in Open Compute
4.2.2 Microsoft Leverages Open Compute Project to Bring Benefit to Enterprise Customers
4.3 Open Source Foundation
4.3.1 OSPF Neighbor Relationship Over Layer 3 MPLS VPN
4.4 Dynamic Systems
4.4.1 Robust, Enterprise-Quality Fault Tolerance
4.5 Cache / Queue
4.6 Multicast
4.7 Performance Optimization
4.8 Fault Tolerance
4.8.1 Gateways
4.8.2 Promise Of Web Services
4.9 IP Addressing And Directory Management
4.9.1 Dynamic Visual Representations
4.9.2 Application Integration
4.9.3 Point Applications
4.9.4 Fault Tolerance and Redundancy Solutions
4.9.5 Goldman Sachs Open Compute Project
4.10 Robust, Quality Cloud Computing
4.11 Networking Performance
5. MEGA DATACENTERS COMPANY PROFILES
5.1 Amazon
5.1.1 Amazon Business
5.1.2 Amazon Competition
5.1.3 Amazon Description
5.1.4 Amazon Revenue
5.2 Facebook
5.2.1 Facebook Technology
5.2.2 Facebook Sales and Operations
5.2.3 Facebook Management Discussion
5.2.4 Facebook Revenue
5.2.5 Facebook
5.2.6 Facebook App Draining Smart Phone Batteries
5.2.7 Facebook Messaging Provides Access to User Behavioral Data
5.2.8 Facebook Creating Better Ads
5.2.9 Facebook Next Generation Services
5.2.10 Facebook Platform
5.2.11 Facebook Free Basics
5.2.12 Facebook AI
5.2.13 Facebook Revenue
5.2.14 Facebook Revenue Growth Priorities:
5.2.15 Facebook Average Revenue Per User ARPU
5.2.16 Facebook Geographical Information
5.2.17 Facebook WhatsApp
5.2.18 Facebook WhatsApp Focusing on Growth
5.3 Google
5.3.1 Google Revenue
5.3.2 Google
5.3.3 Google Search Technology
5.3.4 Google Recognizes World Is Increasingly Mobile
5.3.5 Google Nest
5.3.6 Google / Nest Protect
5.3.7 Google / Nest Safety History
5.3.8 Google / Nest Learning Thermostat
5.3.9 Google Chromecast
5.4 Microsoft
5.4.1 Microsoft Builds the Intelligent Cloud Platform
5.4.2 Microsoft Targets Personal Computing
5.4.3 Microsoft Reportable Segments
5.4.4 Skype and Microsoft
5.4.5 Microsoft / Skype / GroupMe Free Group Messaging
5.4.6 Microsoft SOA
5.4.7 Microsoft .Net Open Source
5.4.8 Microsoft Competition
5.4.9 Microsoft Revenue
WINTERGREEN RESEARCH,
WinterGreen Research Research Methodology
Figure 1. Cloud 2.0 Mega Data Center Market Driving Forces
Figure 2. Cloud Datacenter, Co-Location, and Social Media Revenue Market Shares, Dollars, Worldwide, 2016, Image
Figure 3. Cloud 2.0 Mega Datacenter Market Forecast, Dollars, Worldwide, 2017-2023
Figure 4. RagingWire Colocation N+1 Shared Infrastructure
Figure 5. RagingWire Colocation N+1 Dedicated Infrastructure
Figure 6. RagingWire Data Center Maintenance on N+1 Dedicated System
Reduces Fault Tolerance to N
Figure 7. RagingWire Data Center Stays Fault Tolerant During Maintenance with 2N+2 System
Figure 8. 100 Gbps Adoption
Figure 9. Data Center Technology Shifting
Figure 10. Data Center Technology Shift
Figure 11. IT Cloud Evolution
Figure 12. Facebook Networking Infrastructure Fabric
Figure 13. Datacenter Metrics
Figure 14. Cloud 2.0 Mega Data Center Market Driving Forces
Figure 15. Cloud 2.0 Mega Datacenter Cap Ex Spending Market Shares
Dollars, Worldwide, 2016
Figure 16. Large Internet Company Cap Ex Market Shares, Dollars,
Worldwide, 2013 to 2016
Figure 17. Cloud 2.0 Mega Data Center Cap Ex Market Shares, Dollars,
Worldwide, 2013 to 2016
Figure 18. Cloud 2.0 Mega Data Center Cap Ex Market Shares, Dollars, Worldwide, 2016
Figure 19. Cloud 2.0 Mega Data Center Social Media and Search Revenue
Market Shares, Dollars, 2016, Image
Figure 20. Cloud 2.0 Mega Data Center Social Media and Search Revenue
Market Shares, Dollars, 2016
Figure 21. 538,000SF: i/o Data Centers and Microsoft Phoenix One, Phoenix, Ariz.
Figure 22. Phoenix, Arizona i/o Data Center Design Innovations
Figure 23. Microsoft Data Center, Dublin, 550,000 Sf
Figure 24. Container Area In The Microsoft Data Center In Chicago
Figure 25. An aerial view of the Microsoft data center in Quincy, Washington
Figure 26. . Microsoft San Antonio Data Centers, 470,000 SF
Figure 27. Microsoft 3rd Data Center in Bexar Could Employ 150
Figure 28. Cloud 2.0 Mega Datacenter Market Forecast, Dollars, Worldwide, 2017-2023
Figure 29. Cloud 2.0 Mega Datacenter Market Shares Dollar, Forecast,
Worldwide, 2017-2023
Figure 30. Cloud 2.0 Mega Datacenter Market Shares Percent, Forecast,
Worldwide, 2017-2023
Figure 31. Market Driving Forces for Cloud 2.0 Mega Data Centers
Figure 32. Market Challenges of Cloud 2.0 Mega Data Centers
Figure 33. Key Components And Topology Of A Mega Datacenter
Figure 34. Datacenter Topology without Single Managed Entities
Figure 35. Key Challenges Enterprise IT Datacenters:
Figure 36. Software Defined Datacenter
Figure 37. Cisco VNI Forecast Overview
Figure 38. The Cisco VNI Forecast—Historical Internet Context
Figure 39. Global Devices and Connections Growth
Figure 40. Average Number of Devices and Connections per Capita
Figure 41. Global IP Traffic by Devices
Figure 42. Global Internet Traffic by Device Type
Figure 43. Global 4K Video Traffic
Figure 44. Global IPv6-Capable Devices and Connections Forecast 2015–2020
Figure 45. Projected Global Fixed and Mobile IPv6 Traffic Forecast 2015–2020
Figure 46. Global M2M Connection Growth
Figure 47. Global M2M Connection Growth by Industries
Figure 48. Global M2M Traffic Growth: Exabytes per Month
Figure 49. Global Residential Services Adoption and Growth
Figure 50. Global IP Traffic by Application Category
Figure 51. Mobile Video Growing Fastest; Online Video and Digital TV Grow Similarly
Figure 52. Global Cord Cutting Generates Double the Traffic
Figure 53. Fixed Broadband Speeds (in Mbps), 2015–2020
Figure 54. Future of Wi-Fi as Wired Complement
Figure 55. Global IP Traffic, Wired and Wireless*
Figure 56. Global Internet Traffic, Wired and Wireless
Figure 57. Cisco VNI Forecasts 194 EB per Month of IP Traffic by 2020
Figure 58. Cisco Forecast of Global Devices and Connections Growth
Figure 59. Cloud 2.0 Mega Data Center Regional Market Segments, Dollars, 2016, Image
Figure 60. Cloud 2.0 Mega Data Center Regional Market Segments, Dollars, 2016
Figure 61. Map of Google’s Cloud Data Centers
Figure 62. Amazon Zones and Regions
Figure 63. Amazon AWS Global Cloud Infrastructure
Figure 64. Amazon (AWS) Support for Global IT Presence
Figure 65. AWS E Tool Functions
Figure 66. AWS E Tool Supported Sources
Figure 67. Amazon North America Map
Figure 68. Amazon North America List of Locations
Figure 69. Example of AWS Region
Figure 70. Example of AWS Availability Zone
Figure 71. Example of AWS Data Center
Figure 72. AWS Network Latency and Variability
Figure 73. Amazon (AWS) Regional Data Center
Figure 74. A Map of Amazon Web Service Global Infrastructure
Figure 75. Rows of Servers Inside an Amazon (AWS) Data Center
Figure 76. Facebook DuPont Fabros Technology Ashburn, VA Data Center
Figure 77. Facebook Altoona Iowa Cloud 2.0 Mega Data Center
Figure 78. Facebook Cloud 2.0 mega data center in Altoona, Iowa Construction Criteria
Figure 79. Facebook Fifth Data Center Fort Worth Complex.
Figure 80. Facebook Altoona Positioning Of Global Infrastructure
Figure 81. Facebook Back-End Service Tiers And Applications Account for Machine-To-
Machine Traffic Growth
Figure 82. Facebook Back-End Service Tiers And Applications Functions
Figure 83. Facebook Cluster-Focused Architecture Limitations
Figure 84. Facebook Clusters Fail to Solve a Networking Limitations
Figure 85. Facebook Sample Pod: Unit of Network
Figure 86. Facebook Data Center Fabric Network Topology
Figure 87. Facebook Network Technology
Figure 88. Facebook Schematic Fabric-Optimized Datacenter Physical Topology
Figure 89. Facebook Automation of Cloud 2.0 mega data center Process
Figure 90. Facebook Creating a Modular Cloud 2.0 mega data center Solution
Figure 91. Facebook Cloud 2.0 mega data center Fabric High-Level Settings Components
Figure 92. Facebook Cloud 2.0 mega data center Fabric Unattended Mode
Figure 93. Facebook Data Center Auto Discovery Functions
Figure 94. Facebook Automated Process Rapid Deployment Architecture
Figure 95. Facebook Fabric Automated Process Rapid Deployment Architecture
Figure 96. Facebook Fabric Rapid Deployment
Figure 97. Facebook Cloud 2.0 mega data center High Speed
Network Implementation Aspects
Figure 98. Facebook Cloud 2.0 mega data center High Speed
Network Implementation Aspects
Figure 99. Google St. Ghislain, Belgium, Europe Data Center
Figure 100. Google Dynamic Architecture
Figure 101. Google Clos Multistage Switching Network
Figure 102. Google Key Principles Used In Designing Datacenter Networks
Figure 103. Google Andromeda Cloud Architecture Throughput Benefits
Figure 104. Google Andromeda Software Defined Networking (SDN)-
Based Substrate Functions
Figure 105. Google Andromeda Cloud High-Level Architecture
Figure 106. Google Andromeda Performance Factors Of The Underlying Network
Figure 107. Google Compute Engine Load Balanced Requests Architecture
Figure 108. Google Compute Engine Load Balancing
Figure 109. Google Cloud Platform TCP Andromeda Throughput Advantages
Figure 110. Google Meta Data Center Locations
Figure 111. Google Meta Data Center Locations Map
Figure 112. Google Dalles Data Center Cooling Pipes
Figure 113. Google Hamina, Finland Data Center
Figure 114. Google Lenoir Data Center North Carolina, US
Figure 115. Google Data Center in Pryor, Oklahoma
Figure 116. Google Douglas County, Georgia Data Center Facility
Figure 117. Google Berkeley County, South Carolina, Data Center
Figure 118. Google Council Bluffs Iowa Cloud 2.0 Mega Data Center
Figure 119. Google Council Bluffs Iowa Cloud 2.0 Mega Data Center Campus
Network Room
Figure 120. Google Douglas County Cloud 2.0 Mega Data Center
Figure 121. Google Team of Technical Experts Develop And Lead Execution Of’
Global Data Center Sustainability Strategy
Figure 122. Google Datacenter Manager Responsibilities
Figure 123. Google Meta Data Center
Figure 124. Google Server Warehouse in Former Paper Mill
Figure 125. Google Data Center in Hamina, Finland
Figure 126. Google Traffic Generated by Data Center Servers
Figure 127. Google Cloud 2.0 mega data center Multipathing: Implementing Lots And
Lots Of Paths Between Each Source And Destination
Figure 128. Google Cloud 2.0 mega data center Multipathing: Routing Destinations
Figure 129. Google Builds Own Network Switches And Software
Figure 130. Google Clos Topology Network Capacity Scalability
Figure 131. Google Jupiter Network Delivers 1.3 Pb/Sec Of Aggregate Bisection
Bandwidth Across A Datacenter
Figure 132. Jupiter Superblock Collection of Jupiter Switches Running SDN Stack
Based On Openflow Protocol:
Figure 133. Google Modernized Switch, Server, Storage And Network Speeds
Figure 134. Google Container Controller Positioning
Figure 135. Google Data Center Efficiency Measurements
Figure 136. Google Data Center PUE Measurement Boundaries
Figure 137. Google Continuous PUE Improvement with Quarterly Variatiion, 2008 to 2017
Figure 138. Cumulative Corporate Renewable Energy Purchasing in the United States,
Europe, and Mexico, November 2016
Figure 139. Images for Microsoft Dublin Cloud 2.0 Mega Data Center
Figure 140. Microsoft Azure Data Center
Figure 141. Microsoft Dublin Cloud 2.0 mega data center
Figure 142. Microsoft .Net Dynamic Definition of Reusable Modules
Figure 143. Microsoft .NET Compiling Source Code into Managed Assemblies
Figure 144. Microsoft Architecture Dynamic Modular Processing
Figure 145. Microsoft-Azure-Stack-Block-Diagram
Figure 146. Microsoft-Azure-Platform Stack-Services
Figure 147. Figure 175. Microsoft-Cloud Virtual Machine -Platform Stack-Services
Figure 148. Microsoft-Azure-Core Management-Services
Figure 149. Microsoft Data Centers
Figure 150. Multiple Pathways Open To Processing Nodes In The Cloud 2.0 Mega
Data Center Functions
Figure 151. Layer 3 MPLS VPN Backbone
Figure 152. OSPF Network Types
Figure 153. Automatic Detection And Recovery From Network And System Failure
Figure 154. High Performance And Real-Time Message Throughput
Figure 155. Fault Tolerance Features
Figure 156. Functions Of An IP Addressing Device
Figure 157. Benefits Of an IP Addressing Device
Figure 158. Dynamic Visual Representation System Uses
Figure 159. Application Integration Health Care Functions
Figure 160. Application Integration Industry Functions
Figure 161. CERNE Cloud Architecture
Figure 162. Cern Cloud and Dev
Figure 163. CERN Use Cases
Figure 164. Cern Hardware Spectrum
Figure 165. Cern Operations Containers
Figure 166. Open Stack at Cern
Figure 167. Cern Open Space Containeers on Clouds
Figure 168. Amazon Principal Competitive Factors In The Online Retail Business
Figure 169. Amazon Improving Customer Experience Functions
Figure 170. Amazon Ways To Achieve Efficiency In Technology For Operations
Figure 171. Google / Nest Learning Thermostat
Figure 172. Microsoft Productivity and Business Processes Segment
Figure 173. Microsoft Intelligent Cloud Segment
Figure 174. Microsoft / Skype / GroupMe Free Group Messaging
Figure 175. Microsoft Service Orientated Architecture SOA Functions

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