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


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

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.

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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.

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Table of Content

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

1.1 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

2.1 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

3.1 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


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