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Hyperscale Data Centers: Market Shares, Strategies, and Forecasts, Worldwide, 2017 to 2023

Hyperscale Data Centers: Market Shares, Strategies, and Forecasts, Worldwide, 2017 to 2023

WinterGreen Research announces that it has published a new study Hyperscale Data Centers: Market Shares, Strategy, and Forecasts, Worldwide, 2017 to 2023. The 2017 study has 846 pages, 320 tables and figures. Worldwide hyperscale data center markets implement cloud computing with shared resource and foolproof security systems that protect the integrity of corporate data. Cloud data centers are poised to achieve explosive growth as they replace enterprise web server farms with cloud computing and with cloud 2.0 automated process computing. The implementation of secure large computing capability inside data center buildings provides economies of scale not matched by current state of the art enterprise data center standalone server technology.

Building size cloud 2.0 computer implementations feature simplicity of design achievable only with scale. These data centers implement cloud 2.0 in a move that works better than much of the current cloud computing. The cloud 2.0 data centers have been reduced to two types of components, an ASIC server: single chip servers and a network based on a matching ASIC switch. Data centers are implemented with a software controller for that ASIC server and switch infrastructure.

The major driving factors for Cloud 2.0 mega data center market are cost benefit, growing colocation services, need for data consolidation, and cloud. 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 in any particular data center because there are multiple pathways to every node. In this manner, they automate applications integration for any data in the mega data center.

Cloud 2.0 mega data centers are different from ordinary cloud computing. Mega datacenter networks deliver unprecedented speed at the scale of entire buildings. They are built for modularity. They are constantly upgraded to meet the insatiable bandwidth demands of the latest generation of servers. They are managed for availability.

According to Susan Eustis, principal author of the study, “The mega data centers have stepped in to do the job of automated process in the data center, increasing compute capacity efficiently by simplifying the processing task into two simple component parts that can scale on demand. The added benefit of automated application integration brings massive savings to the IT budget, replacing manual process for application integration.”

The only way to realign enterprise data center cost structures is to automate infrastructure management and orchestration. Mega data centers automate server and connectivity management. Cisco UCS Director illustrates software that automates everything beyond. Cisco UCS automates switching and storage, along with hypervisor, operating system, and virtual machine provisioning.

As IT relies more on virtualization and cloud mega data center computing, the physical infrastructure is flexible and agile enough to support the virtual infrastructure. Comprehensive infrastructure management and orchestration is essential. The enterprise data centers and many cloud infrastructure operations all have similar problems of being mired in administrative expense. This presents a problem for those tasked with running companies.

The Internet has grown by a factor of 100 over the past 10 years. To accommodate that growth, hyperscale data centers have evolved to provide processing at scale, known as cloud computing. Facebook for one, has increased the corporate data center compute capacity by a factor of 1,000. To meet future demands on the Internet over the next 10 years, the company needs to increase capacity by the same amount again. Nobody really knows how to get there.


This is the 690th 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.

HYPERSCALE DATACENTERS EXECUTIVE SUMMARY
Hyperscale Data Center Scale and Automation
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 Defacto Standard Container Management System
Google Shift from Bare Metal To Container Controllers
Cloud 2.0 Mega Data Center Market Driving Forces
Hyperscale 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. HYPERSCALE 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. HYPERSCALE DATACENTERS MARKET SHARES AND FORECASTS
2.1 Hyperscale 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 96
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 Hyperscale Data Center Market Shares
2.2.1 Cloud Datacenter, Co-Location, and Social Media Cloud, Revenue Market Shares,
Dollars, Worldwide, 2016
2.2.2 Cloud 2.0 Mega Datacenter Cap Ex Spending Market Shares Dollars, Worldwide, 2016
2.2.3 Amazon Capex for Cloud 2.0 Mega Data Centers
2.2.4 Amazon (AWS) Cloud
2.2.5 Amazon Datacenter Footprint
2.2.6 Cloud 2.0 Mega Data Center Social Media and Search Revenue Market Shares, Dollars, 2016
2.2.7 Top Hyperscale Companies
2.2.8 Biggest Data Centers
2.2.9 Microsoft Azure
2.2.10 Microsoft Data Center, Dublin, 550,000 Sf
2.2.11 Microsoft Data Center Container Area in Chicago.
2.2.12 Microsoft Quincy Data Centers, 470,000 Square Feet
2.2.13 . Microsoft San Antonio Data Center, 470,000 SF
2.2.14 Microsoft 3rd Data Center in Bexar Could Employ 150
2.2.15 Microsoft Builds the Intelligent Cloud Platform
2.2.16 Microsoft's datacenter footprint
2.2.17 Google Datacenter Footprint
2.2.18 Apple Datacenter Footprint
2.2.1 Facebook Datacenter Footprint
2.2.2 Chef Web-Scale Automation Of Systems Integration In The Cloud
2.2.3 Docker Open Platform
2.2.4 OpenStack
2.2.5 Ragingwire
2.2.6 Simplifying Messaging is a Priority for Goldman Sachs Implementing Automation
2.2.7 IBM 142
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 Hyperscale Data Center Storage Market Analysis
2.5 Mega-Datacenter: Internet Giants Continue To Increase Capex
2.5.1 Apple Datacenter Footprint
2.5.2 Google Datacenter Footprint
2.5.3 Microsoft Datacenter Footprint
2.5.4 Amazon Datacenter Footprint
2.5.5 Facebook Datacenter Footprint
2.5.6 Service Tiers and Applications
2.5.7 M2M industry
2.5.8 Cloud 2.0 Mega Data Center Segments
2.5.9 Cloud 2.0 Mega Data Center Positioning
2.6 Cloud 2.0 Mega Data Center Size
2.6.1 Cloud 2.0 Mega Data Centers
2.6.2 Public Cloud Infrastructure
2.7 Multi-Tenant Data Center Market Shares and Revenue Forecasts
2.7.1 Colocation Providers
2.7.2 Carrier-Neutral Colocation Providers
2.7.3 Wholesale Data Center Providers
2.7.4 Largest Data Centers
2.8 Cloud 2.0 Mega Data Center
2.8.1 Cloud 2.0 Mega Data Center Is Changing The Hardware And Data Center Markets
2.8.2 Storage SATA Drives Meet Mega Data Center Requirements
2.8.1 Data Center Switching
2.8.2 ASIC Switch Vendors
2.8.3 Data Center Rack Market
2.9 Hyperscale Datacenter Future
2.9.1 Public Cloud Services Revenue
2.10 Edge Cloud Data Centers
2.10.1 Edge Data Center Definition
2.11 Data Expanding And Tools Used To Share, Store And Analyze Evolving At
Phenomenal Rates
2.11.1 Video Traffic
2.11.2 Cisco Analysis of Business IP Traffic
2.11.3 Increasing Video Definition: By 2020, More Than 40 Percent of Connected Flat-
Panel TV Sets Will Be 4K
2.11.4 M2M Applications
2.11.5 Applications, For Telemedicine And Smart Car Navigation Systems, Require Greater
Bandwidth And Lower Latency
2.11.6 Explosion of Data Inside Cloud 2.0 Mega Data Center with Multi-Threading
2.11.7 Cloud 2.0 Mega Data Center Multi-Threading Automates Systems Integration
2.11.8 Fixed Broadband Speeds (in Mbps), 2015–2020
2.11.9 Internet Traffic Trends
2.11.10 Internet of Things
2.11.11 The Rise of the Converged “Digital Enterprise”
2.11.12 Enterprise Data Centers Give Way to Commercial Data Centers
2.12 Hyperscale Data Center TCO and Pricing: Server vs. Mainframe vs. Cloud vs. Cloud 2.0
2.12.1 Labor Accounts For 75% Of The Cost Of An Enterprise Web Server Center
2.12.2 Cloud 2.0 Systems And The Mainframe Computing Systems Compared
2.12.3 Cloud 2.0 and Mainframe Implements Shared Resource
2.12.4 Average Operating Density Of Data Centers
2.12.5 Mainframe Capacity vs. Cloud 2.0
2.12.6 Enterprise IT Departments TCO / ROI Custom Data Center Cost Analysis
2.12.7 Server, Mainframe, Cloud, and Cloud 2.0 Cost Comparisons
2.12.8 Server to MIPS Conversion Calculations
2.12.9 Mainframe Updates And Cost Efficiencies
2.12.10 Cost of Cloud Computing
2.12.11 Types of Cloud Computing
2.12.12 Software-Defined Infrastructure
2.12.13 Scale 244
2.13 Cloud Hyperscale Data Center Regional Market Analysis
2.13.1 US Data Center REITs
2.13.2 Chicago Data Center Supply Grows
2.13.3 Digital Realty Trust Largest Data Center: Cermak, Chicago, 1.1 Million Square Feet
2.13.4 US Data Center Activity
2.13.5 Cloud Demand Globally
2.13.6 Landlords Adapt to Cloud Globally
2.13.7 Amazon, Google Detail Next Round of Cloud Data Center Launches
2.13.1 Cloud Data Centers Market in Europe
2.13.2 Cloud Data Centers Market in Ireland
2.13.3 Japanese Data Centers
3. HYPERSCALE DATACENTER 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 Baidu 400
3.4.1 Baidu Data Center, Shanxi
3.4.2 China Mobile Working With Leading Chinese Language Search Engine Baidu And Schneider Electric 402
3.5 China Mobile
3.6 Tencent
3.6.1 Tencent $1 Billion Midwest China Data Center
3.6.2 Tencent Facilitates Cloud
3.7 Smart City Data Center Comes Online
3.7.1 SNIA China Big Data Project
3.8 Alibaba
3.9 Yahoo
3.10 Microsoft
3.10.1 Microsoft .Net Dynamically Defines Reusable Modules
3.10.2 Microsoft Combines Managed Modules into Assemblies
3.10.3 Microsoft Architecture Dynamic Modular Processing
3.10.4 Microsoft Builds Azure Cloud Data Centers in Canada
3.10.5 Microsoft Dublin Cloud 2.0 mega data center
3.10.6 Microsoft Data Center Largest in U.S.
3.10.7 Microsoft Crafts Homegrown Linux For Azure Switches
3.10.8 Microsoft Azure Cloud Switch
3.10.9 Microsoft Azure CTO Cloud Building
3.10.10 Microsoft Cloud 2.0 Mega Data Center Multi-Tenant Containers
3.10.11 Microsoft Managed Clustering and Container Management: Docker and Mesos
3.10.12 Kubernetes From Google or Mesos
3.10.13 Microsoft Second Generation Open Cloud Servers
3.10.14 Azure Active Directory
3.10.15 Microsoft Azure Stack Platform Brings The Suite Of Azure Services To The Corporate Datacenter
3.10.16 Hardware Foundation For Microsoft Azure Stack
3.11 Apple
3.11.1 Apple Invests €1.7 Billion in European Data Centres
3.11.2 Apple Builds $2 Billion Cloud 2.0 mega data center in Arizona
3.12 Goldman Sachs
3.12.1 Simplifying Messaging is a Priority for Goldman Sachs
3.12.2 Goldman Sachs Cloud 2.0 Mega Data Center
3.12.3 Goldman Sachs Security for The Financial Services Organization
3.12.4 Goldman Sachs: Containers Have Real Promise at The Institution
3.12.5 Goldman Sachs Cloud Computing
3.13 Fidelity Investments
3.13.1 Fidelity Investments Core Unit Building Blocks
3.13.2 Fidelity Centercore Design
3.13.3 Fidelity Ties Into Modular Momentum Among Financials
3.14 QTS Custom Data Centers
3.14.1 QTS Multi Tenant Data Center
3.14.2 QTS Critical Facilities Management
3.15 IBM
3.15.1 IBM z Systems in the Cloud
3.15.2 IBM Cloud Managed Services on z Systems
3.15.3 IBM® Cloud Managed Services® on z Systems
3.15.4 Linux-Based Solutions Under IBM z/VM® Shared Infrastructure Support Hybrid Workloads
3.15.5 IBM Cloud Managed Services on z Systems
3.15.6 IBM Builds a Cloud 2.0 mega data center in India
3.15.7 IBM Outsourcing
3.15.8 IBM Server SAN Software-Led Infrastructure
3.15.9 IBM Partnership with American Airlines
3.15.10 IBM Cloud Momentum In The Airlines Industry
3.15.11 IBM CICS
3.15.12 IBM z13 Mainframe Hardware Platform
3.15.13 IBM z13 Software Compression Algorithm In Db/2 Cuts The
Number Of Bits Required To Store Data From 80 Bits To Four Bits
3.16 DuPont Fabros Technology
3.16.1 Data Center Market Trends For Wholesale Commissioned MW Trend
3.17 Hewlett Packard
3.17.1 HP Hyperscale Composable Infrastructure
3.17.2 HP Data Center Control Human Tools With Point And Click Interfaces
3.17.3 HP Data Center Hyperscaler Control
3.17.4 Hewlett Packard Project Synergy Composable Infrastructure Initiative
3.18 NTT Raging Wire
3.19 Rackspace
3.19.1 Rackspace Power
3.19.2 Rackspace Network
3.20 Equinix
3.20.1 HK3 Hong Kong - Data Center
3.20.2 Equinox Supports Diverse And Rapidly Growing International Business
Clusters, Rich Industry Ecosystem
3.20.3 Equinox Dublin Metro Facilities
3.20.4 Equinix Brings Online Sixth London Data Center
3.20.5 Equinix Performance Hub
3.21 Twitter 527
3.22 Bank of America
3.23 Wells Fargo
3.24 eBay 530
3.25 Switch SuperNAP
3.25.1 Switch International Expansion
3.25.2 Switch Cloud Connectivity Charges Lowered
3.25.3 Switch SUPERNAP High Density Designs
3.25.4 Red Hat Ansible
3.25.5 Red Hat Ansible Architecture, Agents, And Security
3.25.6 Red Hat Ansible Advanced Features
3.25.7 Red Hat / Ansible
3.26 Cisco 549
4. HYPERSCALE 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 Equinix Expansion of LD6 International Business Exchange Datacenter
4.4.1 Equinix and Oracle Collaborate to Bring Oracle Cloud Services to Equinix
Cloud Exchange in Six Global Markets
4.4.2 Oracle Cloud Platform
4.5 Power Management
4.6 M2M Industry
4.7 Equinix Cloud Exchange Interconnection Solution
4.8 System On A Chip (SoCs) for Cloud 2.0 Mega Data Centers
4.8.1 A New Class of Low-Power Server SoCs
4.8.2 Re-Architecting the Network: Software Defined Networks (SDNs)
4.8.3 Synopsys SoCs
4.8.4 Open Network Foundation (ONF) Addresses Need For SoC Lower Power Consumption
4.9 Dynamic Systems
4.9.1 Robust, Enterprise-Quality Fault Tolerance
4.10 Cache / Queue
4.11 Multicast
4.12 Performance Optimization
4.13 Fault Tolerance
4.13.1 Gateways
4.13.2 Promise Of Web Services
4.14 IP Addressing And Directory Management
4.14.1 Dynamic Visual Representations
4.14.2 Application Integration
4.14.3 Point Applications
4.14.4 Fault Tolerance and Redundancy Solutions
4.14.5 Goldman Sachs Open Compute Project
4.15 Robust, Quality Cloud Computing
4.16 Networking Performance
4.17 Data Center Bandwidth Pricing:
5. HYPERSCALE DATACENTERS COMPANY PROFILES
5.1 365 Data Centers
5.2 Amazon
5.2.1 Amazon Business
5.2.2 Amazon Competition
5.2.3 Amazon Description
5.2.4 Amazon Revenue
5.3 Apple
5.3.1 Apple / AuthenTec
5.3.2 Authentec Revenue Recognition – Smart Sensors
5.3.3 Apple 617
5.3.4 Apple Business Strategy
5.3.5 Apple Products
5.3.6 Apple iPhone
5.3.7 Apple Mac Hardware Products
5.3.8 Apple iPod
5.3.9 Apple iTunes®
5.3.10 Apple Mac App Store
5.3.11 Apple iCloud
5.3.12 Apple Software Products and Computer Technologies
5.3.13 Apple Operating System Software iOS
5.3.14 Apple Mac OS X
5.3.15 Apple Third-Largest Mobile Phone Maker
5.3.16 Apple Revenue
5.3.17 Apple Regional Segment Operating Performance
5.3.18 Apple Net Sales
5.3.19 Apple iPhone Shipments
5.3.20 Apple iPad Shipments
5.3.21 Apple Revenue
5.4 Alibaba
5.4.1 Alibaba Cloud Expands Data Centers, Steps Up Challenge to Amazon, Microsoft
5.4.2 Alibaba Cloud Unit has 2.3 Million Customers
5.4.3 Alibaba Seeks To Leverage Applications Integration via Automated Cloud Processes
5.4.4 Alibaba Cloud Middle East
5.4.5 Alibaba Cloud Europe
5.4.6 Alibaba Cloud Australia
5.4.7 Alibaba Cloud Japan
5.5 Baidu 637
5.5.1 Baidu Platform
5.5.2 Baidu Mobile Era Cloud, Mobile Search
5.5.3 Baidu, The Largest Chinese Search Engine
5.5.4 Baidu Self-Designed 10Gb TOR Switch
5.5.5 Baidu ARM on a Large Scale
5.5.6 Baidu Self-Designed SSD
5.5.7 Baidu Customized Rack Servers
5.5.8 Baidu Buys Modular Data Center From Schneider
5.6 Chef 647
5.6.1 Chef Customers
5.6.1 Chef Partner Ecosystem Includes AWS, Dell, and Rackspace
5.6.2 Chef Compliance, Workflow, and Automation Support
5.6.3 Chef Professional DevOps Practice Service Partners
5.6.4 Chef Technology Partners Build World-Class Integrations with Chef
5.6.5 Chef Value Added Resellers
5.7 China Building A Cloud Computing Complex
5.8 China Mobile
5.9 Colocation America Data Center Bandwidth and Measurements
5.10 Colo-D 664
5.10.1 Colo-D Strong Growth Opening Of A Second Cloud 2.0 mega data center In Quebec In 2016
5.11 CoreSIte 666
5.11.1 CoreSite Cloud Data Center Leasing Accelerates
5.11.2 Key Markets Update
5.12 CyrusOne
5.13 Digital Realty
5.14 Docker 671
5.15 DuPont Fabros Technology
5.15.1 DuPont Fabros Technology Customer Analysis
5.15.2 DuPont Fabros Operating Portfolio: Tier 1 Markets
5.16 Edge ConneX
5.16.1 EdgeConneX Hyperscale Cloud Anchor
5.16.2 EdgeConneX Disrupts Incumbent Data Center Providers
5.16.3 EdgeConneX ‘Disruptive Network Positioning
5.16.4 EdgeConneX – US Strategy
5.16.5 Edge Data Center Providers Changing the Internet’s Geography
5.16.6 EdgeConneX Building at the Internet Edge
5.16.7 EdgeConneX Demand Dynamics
5.16.8 EdgeConneX Funding
5.16.9 EdgeConneX Software Supports Speed to Market
5.16.10 EdgeConneX Cloud Strategy
5.16.11 EdgeConneX in Europe
5.16.12 Liberty Global Anchor Tenant in London
5.17 Equinix 699
5.17.1 EQUINIX, INC. Revenues
5.17.2 Equinix Purchase of Digital Realty Trust
5.17.3 Equinix Acquisition of TelecityGroup
5.18 Facebook
5.18.1 Facebook Technology
5.18.2 Facebook Sales and Operations
5.18.3 Facebook Management Discussion
5.18.4 Facebook Revenue
5.18.5 Facebook
5.18.6 Facebook App Draining Smart Phone Batteries
5.18.7 Facebook Messaging Provides Access to User Behavioral Data
5.18.8 Facebook Creating Better Ads
5.18.9 Facebook Next Generation Services
5.18.10 Facebook Platform
5.18.11 Facebook Free Basics
5.18.12 Facebook AI
5.18.13 Facebook Revenue
5.18.14 Facebook Revenue Growth Priorities:
5.18.15 Facebook Average Revenue Per User ARPU
5.18.16 Facebook Geographical Information
5.18.17 Facebook WhatsApp
5.18.18 Facebook WhatsApp Focusing on Growth
5.19 Forsythe 716
5.19.1 Forsythe Data Centers An Adaptable Facility to Meet Clients’ Evolving Needs
5.19.2 Forsythe Data Centers All-Encompassing Network
5.20 Google
5.20.1 Google Revenue
5.20.2 Google
5.20.3 Google Search Technology
5.20.4 Google Recognizes World Is Increasingly Mobile
5.20.5 Google Nest
5.20.6 Google / Nest Protect
5.20.7 Google / Nest Safety History
5.20.8 Google / Nest Learning Thermostat
5.20.9 Google Chromecast
5.21 Hewlett Packard Enterprise
5.22 IBM
5.22.1 IBM Strategy
5.22.2 IBM Cloud Computing
5.22.3 IBM Business Model
5.22.4 IBM Solutions
5.22.5 IBM Blockchain
5.22.6 IBM PureData System for Transaction Analysis
5.22.7 IBM Business Partners
5.22.8 IBM Messaging Extension for Web Application Pattern
5.22.9 IBM MobileFirst
5.22.10 IBM Business Analytics and Optimization Strategy
5.22.11 IBM Growth Market Initiatives
5.22.12 IBM Business Analytics and Optimization
5.22.13 IBM Strategy Addresses Volatility of Information Technology (IT) Systems
5.22.14 IBM Smarter Planet
5.22.15 IBM Business Revenue Segments And Capabilities
5.22.16 IBM Software Capabilities
5.23 Intel
5.17.1 Intel Business
5.23.2 Intel Company Strategy
5.23.3 Intel In The Internet Of Things Market Segment
5.23.4 Intel Competitive Advantages
5.24 I/O
5.25 InterXion
5.25.1 Interxion Revenue
5.26 Mesosphere
5.26.1 Modern App Platform Services
5.26.2 Mesosphere Enterprise DC/OS Hybrid cloud independence
5.26.3 Mesosphere / Open Source Mesos Tool
5.26.4 Modern Enterprise Applications Use Of Open Source Software
5.26.5 Banding Together To Deliver Mesosphere Containers And Stateful DC/OS
5.26.6 Mesosphere DC/OS: Mesos
5.26.7 Heart of DC/OS Mesos
5.26.8 DC/OS Implements Containers
5.26.9 DC/OS Available Services as Packages Included in Universe
5.26.10 Mesosphere Customers
5.27 Microsoft
5.27.1 Microsoft Builds the Intelligent Cloud Platform
5.27.2 Microsoft Targets Personal Computing
5.27.3 Microsoft Reportable Segments
5.27.4 Skype and Microsoft
5.27.5 Microsoft / Skype / GroupMe Free Group Messaging
5.27.6 Microsoft SOA
5.27.7 Microsoft .Net Open Source
5.27.8 Microsoft Competition
5.27.9 Microsoft Revenue
5.28 US National Security Agency
5.29 NEC
5.29.1 NEC Revenue
5.29.2 NEC Leading Company In Small Cell Solutions
5.29.3 NEC Business Outline
5.30 NTT / RagingWire
5.30.1 RagingWire Data Centers
5.30.2 RagingWire Best Practice Approach to Data Center Colocation
5.30.3 RagingWire Joined NTT
5.31 OpenStack Cloud Controller
5.31.1 OpenStack Has Created Its Own APIs
5.31.2 How Many Openstack Clouds Have Been Deployed
5.31.3 OpenStack Functions
5.31.4 OpenStack Regional Market
5.31.5 OpenStack Collaboration with Industry
5.31.6 OpenStack Cloud Service Context
5.31.7 OpenStack Industry Presence
5.31.8 Storage Innovations Drive OpenStack
5.32 Puppet
5.32.1 Puppet: Standard platform.
5.32.2 Puppet Customers
5.32.3 Puppet Open Source Software
5.32.4 Puppet Orchestrator
5.32.5 Puppet Project Blueshift
5.33 QTS
5.33.1 QTS History
5.33.2 QTS Chicago Data Center
5.34 Qualcom
5.34.1 Qualcomm
5.34.2 Qualcomm Business
5.34.3 QMC Offers Comprehensive Chipset Solutions
5.34.4 Qualcomm Government Technologies
5.34.5 Qualcomm Internet Services
5.34.6 Qualcomm Ventures
5.34.7 Qualcomm Revenue
5.34.8 Qualcomm / WiPower
5.35 Rackspace
5.36 Red Hat / Ansible
5.37 Switch
5.37.1 Switch SUPERNAP CORE Cooperative: $3 Trillion Independent Purchasing And Collaboration Ecosystem
5.37.2 Switch SUPERNAP Edge
5.38 Tango
5.39 Tencent
5.39.1 TenCent Revenue
5.39.2 Tencent Revenues
5.39.3 Tencent Holdings Has a Partnership With Glu
5.39.4 Tencent WeChat
5.40 Twitter
5.40.1 Twitter Revenue
5.40.2 Twitter Creation And Sharing Ideas And Information
5.40.3 Bringing Tweets To People
5.17 Yahoo
5.17.1 Yahoo Revenue
5.17.2 Yahoo Mavens Revenue
5.17.3 Yahoo Tumblr
5.17.4 Yahoo Tumblr Sponsored Posts
5.17.5 Yahoo Tumblr Sponsored Day
5.17.6 Yahoo Tumblr Use Case
5.17.7 Yahoo Display Revenue
5.17.8 Yahoo Display Metrics
5.17.9 Yahoo / Microsoft
5.17.10 Yahoo / Google
5.17.11 Yahoo / Tumblr
WINTERGREEN RESEARCH,
WinterGreen Research Research Methodology
List Of Figures and Tables
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. Global Digital Information Created and Shared 2005-2015
Figure 9. 100 Gbps Adoption
Figure 10. Data Center Technology Shifting
Figure 11. Data Center Technology Shift
Figure 12. IT Cloud Evolution
Figure 13. Facebook Networking Infrastructure Fabric
Figure 14. Datacenter Metrics
Figure 15. Cloud 2.0 Mega Data Center Market Driving Forces
Figure 16. Cloud Datacenter, Co-Location, and Social Media Revenue
Market Shares, Dollars, Worldwide, 2016, Image
Figure 17. Cloud Datacenter, Co-Location, and Social Media Revenue
Market Shares, Dollars, Worldwide, 2016
Figure 18. Cloud 2.0 Mega Datacenter Cap Ex Spending Market Shares Dollars,
Worldwide, 2016
Figure 19. Large Internet Company Cap Ex Market Shares, Dollars,
Worldwide, 2013 to 2016
Figure 20. Cloud 2.0 Mega Data Center Cap Ex Market Shares, Dollars,
Worldwide, 2013 to 2016
Figure 21. Cloud 2.0 Mega Data Center Cap Ex Market Shares, Dollars, Worldwide, 2016
Figure 22. Cloud 2.0 Mega Data Center Social Media and Search Revenue
Market Shares, Dollars, 2016, Image
Figure 23. Cloud 2.0 Mega Data Center Social Media and Search Revenue
Market Shares, Dollars, 2016
Figure 24. Big Eight Hyperscaler Cloud Providers
Figure 25. Supernap, Las Vegas, 407,000 sf
Figure 26. DuPONT FABROS CH1, ELK GROVE VILLAGE, Ill. 485,000 SF
Figure 27. 538,000SF: i/o Data Centers and Microsoft Phoenix One, Phoenix, Ariz.
Figure 28. Phoenix, Arizona i/o Data Center Design Innovations
Figure 29. Next Generation Data Europe, Wales 750,000 SF
Figure 30. NAP Of The Americas, Miami, 750,000 SF
Figure 31. QTS Metro Data Center, Atlanta, 990,000 SF
Figure 32. 350 East Cermak, Chicago, 1.1 Million Square Feet
Figure 33. Data Center Multiple-Facility Campuses Feature Half Million SF
Figure 34. Microsoft Data Center, Dublin, 550,000 Sf
Figure 35. Container Area In The Microsoft Data Center In Chicago
Figure 36. An aerial view of the Microsoft data center in Quincy, Washington
Figure 37. . Microsoft San Antonio Data Centers, 470,000 SF
Figure 38. Microsoft 3rd Data Center in Bexar Could Employ 150
Figure 39. Cloud 2.0 Mega Datacenter Market Forecast, Dollars, Worldwide, 2017-2023
Figure 40. Cloud 2.0 Mega Datacenter Market Shares Dollars, Forecast,
Worldwide, 2017-2023,
Figure 41. Cloud 2.0 Mega Datacenter Market Shares Percent, Forecast,
Worldwide, 2017-2023
Figure 42. Hyperscale Data Centers
Figure 43. Market Driving Forces for Cloud 2.0 Mega Data Centers
Figure 44. Market Challenges of Cloud 2.0 Mega Data Centers
Figure 45. Data Center Size Definition
Figure 46. Data Center Density Definitions
Figure 47. Carrier-Neutral Colocation Vendors
Figure 48. DuPont Fabros Technology 450,000 square feet, ACC7 in Ashburn,
VA Large Data Center
Figure 49. Ten Largest Data Centers
Figure 50. Cloud 2.0 mega data center Open Compute Project OCP ASIC Switch Vendors
Figure 51. Global Data Center Rack Market Key Vendors
Figure 52. Global Data Center Rack Market Company Participants
Figure 53. Key Components And Topology Of A Mega Datacenter
Figure 54. Datacenter Topology without Single Managed Entities
Figure 55. Key Challenges Enterprise IT Datacenters:
Figure 56. Software Defined Datacenter
Figure 57. Cloud Automation Vendors
Figure 58. Cisco VNI Forecast Overview
Figure 59. The Cisco VNI Forecast—Historical Internet Context
Figure 60. Global Devices and Connections Growth
Figure 61. Average Number of Devices and Connections per Capita
Figure 62. Global IP Traffic by Devices
Figure 63. Global Internet Traffic by Device Type
Figure 64. Global 4K Video Traffic
Figure 65. Global IPv6-Capable Devices and Connections Forecast 2015–2020
Figure 66. Projected Global Fixed and Mobile IPv6 Traffic Forecast 2015–2020
Figure 67. Global M2M Connection Growth
Figure 68. Global M2M Connection Growth by Industries
Figure 69. Global M2M Traffic Growth: Exabytes per Month
Figure 70. Global Residential Services Adoption and Growth
Figure 71. Global IP Traffic by Application Category
Figure 72. Mobile Video Growing Fastest; Online Video and Digital TV Grow Similarly
Figure 73. Global Cord Cutting Generates Double the Traffic
Figure 74. Fixed Broadband Speeds (in Mbps), 2015–2020
Figure 75. Future of Wi-Fi as Wired Complement
Figure 76. Global IP Traffic, Wired and Wireless*
Figure 77. Global Internet Traffic, Wired and Wireless
Figure 78. Cisco VNI Forecasts 194 EB per Month of IP Traffic by 2020
Figure 79. Cisco Forecast of Global Devices and Connections Growth
Figure 80. Hardware Cost Comparison – Mainframe vs. Distributed
Figure 81. Server and Mainframe Hardware Costs
Figure 82. 2016 Compute Cost Metrics
Figure 83. 2016 Compute Hardware Cost Metrics
Figure 84. Server Transactions Per Watt From Greenway Collaborative
Figure 85. Server to MIPS Conversion Calculations
Figure 86. Working Out The Hardware Cost Differential Between
Mainframe And Distributed Systems
Figure 87. Server And Mainframe Hardware Costs
Figure 88. Benefits of Cloud Computing
Figure 89. Intel Xeon Processor, 36 Cores, x2Thread Count = 72,
Spin 5000 virtual Machines
Figure 90. Cloud Services Market Shares, Dollars, 2016
Figure 91. Cloud Services Market Shares Dollars, Worldwide, 2016
Figure 92. Cloud Services Market Shares Percent, Worldwide, 2016
Figure 93. Cloud Services, Companies with Measurable Market Shares,
Dollars and Percent, Worldwide, 2016
Figure 94. Cloud 2.0 Mega Data Center Regional Market Segments, Dollars, 2016, Image
Figure 95. Cloud 2.0 Mega Data Center Regional Market Segments, Dollars, 2016
Figure 96. Data Center Supply in Selected US Regions
Figure 97. Chicago Data Center Market
Figure 98. Digital Realty Trust Lakeside Technology Center
Figure 99. Digital Realty Trust Lakeside Technology Center Hallway Gothic architecture
Figure 100. Digital Realty Trust Lakeside Technology Center Industrial-Strength
Power And Fiber Infrastructure
Figure 101. Map of Google’s Cloud Data Centers
Figure 102. Amazon Zones and Regions
Figure 103. Amazon AWS Global Cloud Infrastructure
Figure 104. Amazon (AWS) Support for Global IT Presence
Figure 105. AWS E Tool Functions
Figure 106. AWS E Tool Supported Sources
Figure 107. Amazon North America Map
Figure 108. Amazon North America List of Locations
Figure 109. Example of AWS Region
Figure 110. Example of AWS Availability Zone
Figure 111. Example of AWS Data Center
Figure 112. AWS Network Latency and Variability
Figure 113. Amazon (AWS) Regional Data Center
Figure 114. A Map of Amazon Web Service Global Infrastructure
Figure 115. Rows of Servers Inside an Amazon (AWS) Data Center
Figure 116. Facebook DuPont Fabros Technology Ashburn, VA Data Center
Figure 117. Facebook Altoona Iowa Cloud 2.0 Mega Data Center
Figure 118. Facebook Cloud 2.0 mega data center in Altoona, Iowa Construction Criteria
Figure 119. Facebook Fifth Data Center Fort Worth Complex.
Figure 120. Facebook Altoona Positioning Of Global Infrastructure
Figure 121. Facebook Back-End Service Tiers And Applications Account for
Machine-To-Machine Traffic Growth
Figure 122. Facebook Back-End Service Tiers And Applications Functions
Figure 123. Facebook Cluster-Focused Architecture Limitations
Figure 124. Facebook Clusters Fail to Solve a Networking Limitations
Figure 125. Facebook Sample Pod: Unit of Network
Figure 126. Facebook Data Center Fabric Network Topology
Figure 127. Facebook Network Technology
Figure 128. Facebook Schematic Fabric-Optimized Datacenter Physical Topology
Figure 129. Facebook Automation of Cloud 2.0 mega data center Process
Figure 130. Facebook Creating a Modular Cloud 2.0 mega data center Solution
Figure 131. Facebook Cloud 2.0 mega data center Fabric High-Level Settings Components
Figure 132. Facebook Cloud 2.0 mega data center Fabric Unattended Mode
Figure 133. Facebook Data Center Auto Discovery Functions
Figure 134. Facebook Automated Process Rapid Deployment Architecture
Figure 135. Facebook Fabric Automated Process Rapid Deployment Architecture
Figure 136. Facebook Fabric Rapid Deployment
Figure 137. Facebook Cloud 2.0 mega data center High Speed Network
Implementation Aspects
Figure 138. Facebook Cloud 2.0 mega data center High Speed Network
Implementation Aspects
Figure 139. Google St. Ghislain, Belgium, Europe Data Center
Figure 140. Google Dynamic Architecture
Figure 141. Google Clos Multistage Switching Network
Figure 142. Google Key Principles Used In Designing Datacenter Networks
Figure 143. Google Andromeda Cloud Architecture Throughput Benefits
Figure 144. Google Andromeda Software Defined Networking (SDN)-Based
Substrate Functions 344
Figure 145. Google Andromeda Cloud High-Level Architecture
Figure 146. Google Andromeda Performance Factors Of The Underlying Network
Figure 147. Google Compute Engine Load Balanced Requests Architecture
Figure 148. Google Compute Engine Load Balancing
Figure 149. Google Cloud Platform TCP Andromeda Throughput Advantages
Figure 150. Google Meta Data Center Locations
Figure 151. Google Meta Data Center Locations Map
Figure 152. Google Dalles Data Center Cooling Pipes
Figure 153. Google Hamina, Finland Data Center
Figure 154. Google Lenoir Data Center North Carolina, US
Figure 155. Google Data Center in Pryor, Oklahoma
Figure 156. Google Douglas County, Georgia Data Center Facility
Figure 157. Google Berkeley County, South Carolina, Data Center
Figure 158. Google Council Bluffs Iowa Cloud 2.0 Mega Data Center
Figure 159. Google Council Bluffs Iowa Cloud 2.0 Mega Data Center Campus
Network Room
Figure 160. Google Douglas County Cloud 2.0 Mega Data Center
Figure 161. Google Team of Technical Experts Develop And Lead Execution Of’
Global Data Center Sustainability Strategy
Figure 162. Google Datacenter Manager Responsibilities
Figure 163. Google Meta Data Center
Figure 164. Google Server Warehouse in Former Paper Mill
Figure 165. Google Data Center in Hamina, Finland
Figure 166. Google Traffic Generated by Data Center Servers
Figure 167. Google Cloud 2.0 mega data center Multipathing: Implementing
Lots And Lots Of Paths Between Each Source And Destination
Figure 168. Google Cloud 2.0 mega data center Multipathing: Routing Destinations
Figure 169. Google Builds Own Network Switches And Software
Figure 170. Google Clos Topology Network Capacity Scalability
Figure 171. Google Jupiter Network Delivers 1.3 Pb/Sec Of Aggregate Bisection
Bandwidth Across A Datacenter
Figure 172. Jupiter Superblock Collection of Jupiter Switches Running SDN
Stack Based On Openflow Protocol:
Figure 173. Google Modernized Switch, Server, Storage And Network Speeds
Figure 174. Google Container Controller Positioning
Figure 175. Google Data Center Efficiency Measurements
Figure 176. Google Data Center PUE Measurement Boundaries
Figure 177. Google Continuous PUE Improvement with Quarterly Variatiion, 2008 to 2017
Figure 178. Cumulative Corporate Renewable Energy Purchasing in the
United States, Europe, and Mexico, November 2016
Figure 179. Images for Microsoft Dublin Cloud 2.0 Mega Data Center
Figure 180. Microsoft Azure Data Center
Figure 181. Microsoft Dublin Cloud 2.0 mega data center
Figure 182. Microsoft .Net Dynamic Definition of Reusable Modules
Figure 183. Microsoft .NET Compiling Source Code into Managed Assemblies
Figure 184. Microsoft Architecture Dynamic Modular Processing
Figure 185. Microsoft-Azure-Stack-Block-Diagram
Figure 186. Microsoft-Azure-Platform Stack-Services
Figure 187. Figure 175. Microsoft-Cloud Virtual Machine -Platform Stack-Services
Figure 188. Microsoft-Azure-Core Management-Services
Figure 189. Microsoft Data Centers
Figure 190. QTS Multi Tenant Data Center Outsourcing Benefits
Figure 191. IBM Cloud Managed Services for z Systems
Figure 192. IBM Cloud Managed Services for z Systems Functions
Figure 193. IBM Cloud Managed Services Features
Figure 194. IBM Cloud Managed Services on z Systems Benefits
Figure 195. Linux-Based Solutions Under IBM z/VM® Shared Infrastructure
Support Hybrid Workloads
Figure 196. IBM® Cloud Managed Services® on z Systems Features
Figure 197. IBM® Cloud Managed Services® on z Systems Functions
Figure 198. IBM and CA Security Management Products For Mainframes
Figure 199. IBM Mainframe Regulatory Compliance Support
Figure 200. IBM Cloud Managed Services on z Systems
Figure 201. IBM Cloud Managed Services on z Systems—Linux
Figure 202. IBM i American Airlines Cloud Partnership Functions
Figure 203. IBM Model z13 Computer Features
Figure 204. Pipeline Has An Instruction Queue
Figure 205. IBM Model z13 Computer Configuration
Figure 206. DuPont Fabros ACC Data Center Technology
Figure 207. Data Center Market MW Availability
Figure 208. Wholesale Data Center MW Availability Definition
Figure 209. Wholesale Data Center Consolidated MW Trends
Figure 210. Wholesale Data Center MW Commissioned Growth
Figure 211. Retail Data Center Colocation Churn is Increassing
Figure 212. DuPont Fabros Technology Portfolio Competition
Figure 213. Data Center Key Metrics
Figure 214. Hewlett Packard Composable Data Center Infrastructure
Figure 215. Hewlett Packard Project Synergy Composable Infrastructure Initiative
Figure 216. NTT RagingWire Data Centers Image
Figure 217. NTT Mission Critical IT Systems Features
Figure 218. NTT RagingWire Highly Customizable Colocation Solutions Features
Figure 219. NTT Ragingwire Data Centers Facilities Location, Power, and
Cooling Features
Figure 220. NTT Ragingwire Data Centers Facilities Security Functions
Figure 221. RagingWire Wholesale Data Center Campuses:
Figure 222. NTT RagingWire Ashburn Va2 Data Center
Figure 223. Rackspace Hosting Provider Functions
Figure 224. Equinix LD6 data center in Slough, England
Figure 225. IBX Data Center Locations
Figure 226. Equinix Dublin Metro Data Centers
Figure 227. Equinox Dublin Metro Data Center
Figure 228. Equinox Dublin Data Center Server Racks
Figure 229. Equinix Asia Pacific Data Centers
Figure 230. Equinix IBX Data Center Features
Figure 231. Equinix IBX Data Center Functions:
Figure 232. Equinix Connections and Interconnections
Figure 233. Equinix Data Center Image
Figure 234. Equinix Data Center Features
Figure 235. eBay Cloud 2.0 mega data center
Figure 236. Switch SuperNAP Synoptek Advanced Data Center
Figure 237. Switch Synoptek Data Center Advantages
Figure 238. Switch Synoptek Hosting Facilities Advantages
Figure 239. SuperNAP SSAE16 Type II Certified Facility Features
Figure 240. Switch Synoptek Power Advantages
Figure 241. Switch SuperNap Facilities Aspects:
Figure 242. Switch SUPERNAP Data Center High Density Racks
Figure 243. Switch SUPERNAP High Density Data Center
Figure 244. Red hat Ansible Playbook Language Advanced Features
Figure 245. Cisco UCS Director Delivers Comprehensive Infrastructure
Management and Orchestration
Figure 246. Multiple Pathways Open To Processing Nodes In The Cloud 2.0
Mega Data Center Functions
Figure 247. Layer 3 MPLS VPN Backbone
Figure 248. OSPF Network Types
Figure 249. Cloud 2.0 Mega Data Centers Are Demanding Significant
Amounts Of Power And Network Management
Figure 250. Reducing Power With Micro Server SoCs
Figure 251. Software Stack, Standard Platforms, Simplified Network
Architecture, Reduces Network Management Costs
Figure 252. Data center SoC architecture
Figure 253. Simplifying the Data Center Network with SDN
Figure 254. Simplifying the Data Center Network
Figure 255. Data Center Network SDN Functions
Figure 256. Synopsys DesignWare IP Portfolio Features
Figure 257. Figure 168. Synopsys DesignWare IP Portfolio Modules
Figure 258. Data Center SoC Architecture Incorporating Synopsys
DesignWare IP Attributes
Figure 259. Automatic Detection And Recovery From Network And System Failure
Figure 260. High Performance And Real-Time Message Throughput
Figure 261. Fault Tolerance Features
Figure 262. Functions Of An IP Addressing Device
Figure 263. Benefits Of an IP Addressing Device
Figure 264. Dynamic Visual Representation System Uses
Figure 265. Application Integration Health Care Functions
Figure 266. Application Integration Industry Functions
Figure 267. CERNE Cloud Architecture
Figure 268. Cern Cloud and Dev
Figure 269. CERN Use Cases
Figure 270. Cern Hardware Spectrum
Figure 271. Open Stack at Cern
Figure 272. Cern Open Space Containeers on Clouds
Figure 273. 365 Data Centers Products & Services
Figure 274. Amazon Principal Competitive Factors In The Online Retail Business
Figure 275. Amazon Improving Customer Experience Functions
Figure 276. Amazon Ways To Achieve Efficiency In Technology For Operations
Figure 277. Alibaba Applications Integration Automated Cloud Processes
Figure 278. Baidu Search and Information
Figure 279. Baidu Range Of Energy Saving Methods And Technologies
Figure 280. Key Benefits of AWS OpsWorks for Chef Automate
Figure 281. Chef Automate Builds On Widely Adopted Open-Source Projects:
Figure 282. Chef Automate Solution for Automating the Technology Stack
Figure 283. Chef Automate Block Diagram
Figure 284. Chef Professional DevOps Practice Service Partners
Figure 285. Technology Partners Build World Class Integrations With Chef To
Accelerate And Compliment Their Platforms With Automation
Figure 286. Chef Value Added Resellers (VARs)
Figure 287. Chef Open Source And Commercial Automation Platforms
Figure 288. Digital Realty Trust Metropolitan Area Percentage of September 30, 2016
Total Annualized Rent 670
Figure 289. Docker Challenges and Solutions
Figure 290. DuPont Fabros Wholesale Data Center Characteristics
Figure 291. DFT Development Plan
Figure 292. DuPont Fabros Triple-Net Leases
Figure 293. DuPont Fabros Technology Data Center Locations
Figure 294. DuPont Fabros Key Strategic Initiatives
Figure 295. DuPont Fabros Base Rent Trends
Figure 296. DuPont Fabros Key Operating Metrics – Leasing / Renewals
Figure 297. DuPont Fabros Occupancy Trends
Figure 298. DuPont Fabros Technology Operating Portfolio: Tier 1 Markets
Figure 299. DuPont Fabros Annual Revenue
Figure 300. EdgeConneX Data Centers Positioned at the Edge
Figure 301. EdgeConneX Edge Data Centers North America and Europe
Figure 302. Equinix Global Regional Segment Revenue, Three Months 2016
Figure 303. Equinix Global Co Location Data Centers
Figure 304. Google / Nest Learning Thermostat
Figure 305. IBM PureSystems Target Industries
Figure 306. Mesosphere Target Applications
Figure 307. Cloud-Native Building Blocks Change Delivery Of Apps
Figure 308. Mesosphere DC/OS: Mesos Features:
Figure 309. Microsoft Productivity and Business Processes Segment
Figure 310. Microsoft Intelligent Cloud Segment
Figure 311. Microsoft / Skype / GroupMe Free Group Messaging
Figure 312. Microsoft Service Orientated Architecture SOA Functions
Figure 313. Ragingwire Wholesale Data Center Campuses:
Figure 314. OpenStack Cloud at CERN
Figure 315. Open Stack Deployments
Figure 316. QTS Chicago Data Center
Figure 317. QTS Chicago Data Center Inside Raised Floor
Figure 318. Rackspace London
Figure 319. Rackspace Global Infrastructure
Figure 320. Red Hat Ansible Tower 3 Job Run Metrics

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