Market Research Logo

Sea Change Series: Scale in the Mega Data Center

Sea Change Series: Scale in the Mega Data Center

WinterGreen Research announces that it has published a new study Scale in the Mega Data Center: Market Shift to Non-Blocking Network Inside Data Center Building. Next generation mega data center technology is able to leverage scale to implement cloud computing that is better than most of what is out there now.

Scale is a vital part of the technology used to support next generation data centers. The study is targeted to C-level executives that need to move quickly and surely to improve IT. Automation of IT depends on understanding the business market opportunity from an independent perspective. Vendors are smart but they are committed to the technology they are pushing, the Sea Change Series from WinterGreen Research is able to provide a perspective not available anywhere else.

Extreme scale is what brings enough pathways inside a Mega data center to create a non-blocking (CLOS) networked server architecture. Non-blocking network architecture benefits the business because it permits launching thousands of virtual severs on demand at the application layer. In this manner, innovation can be made to happen quickly.

Using a mega data center, DevOps and/or automated processes can request and deploy additional resource without backing the Dell truck up to the data center every week to provide on-demand capacity. Automated deprovisioning handles freeing of surplus resources, while being virtual means not having stacks of surplus hardware to dispose of as underutilized capital assets.

Modern data centers are organized into processing nodes that manage different applications at a layer above infrastructure. Data is stored permanently and operated on in place. These are the two technologies to check for when choosing a data center. These architectural features provide economies of scale that greatly reduce the IT spend while offering better quality IT.


This is the 698th report in a series of primary market research reports that provide senior executive analysis 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.

SEA CHANGE SERIES: SCALE IN THE MEGA DATA CENTER
Sea Change Series: Scale in the Mega Data Center, Amazon, Google, Microsoft, Facebook
Aim to Realign IT Cost Structure
Scale Matters 4
Table of Contents
Facebook Mega Datacenter Physical Infrastructure
Facebook Automation of Mega Data Center Process
Facebook Altoona Data Center Networking Fabric
Facebook Altoona Cloud Mega Data Center
Facebook Altoona Data Center Innovative Networking Fabric Depends on Scale
Facebook Fabric Operates Inside the Data Center
Facebook Fabric
Exchange Of Data Between Servers Represents A Complex Automation Of Process
Applications Customized For Each User
Machine-To-Machine Management of Traffic Growth
Facebook Data Center Fabric Network Topology
Building-Wide Connectivity
Highly Modular Design Allows Users To Quickly Scale Capacity In Any Dimension
Back-End Service Tiers And Applications
Scaling Up As a Basic Function Of The Mega Data Center Network
Facebook Fabric Next-Generation Data Center Network Design: Pod Unit of Network
Mega Data Center Server Pods
Facebook Sample Pod: Unit of Network
Non-Blocking Network Architecture
Data Center Auto Discovery
Facebook Large-Scale Network
Rapid Deployment Architecture
Facebook Expedites Provisioning And Changes
Google Douglas County Mega Data Center
Google Data Center Efficiency Measurements
Google Programmable Access To Network Stack
Google Software Defined Networking (SDN)-Supports Scale and Automation
Google Compute Engine Load Balancing
Google Compute Engine Load Balanced Requests Architecture
Google Compute Engine Load Balancing Scaling
Google Switches Provide Scale-Out: Server And Storage Expansion
Google Uses Switches and Routers Deployed in Fabrics
Google Mega Data Center Multipathing
Google Mega Data Center Multipathing: Routing Destinations
Google Clos Topology Network Capacity Scalability
Google Aggregation Switches Are Lashed Together Through a Set Of Non-Blocking Spine Switches
Google Network Called Jupiter
Microsoft Cloud Data Center Multi-Tenant Containers
Microsoft Azure Running Docker Containers
Microsoft Data Center, Dublin, 550,000 Sf
Microsoft Builds Intelligent Cloud Platform
Microsoft Crafts Homegrown Linux For Azure Switches
Microsoft Azure Has Scale
Microsoft Azure Stack Hardware Foundation
Microsoft Azure Stack Key Systems Partners: Cisco Systems, Lenovo, Fujitsu, and NEC
Microsoft Gradual Transformation From A Platform Cloud To A Broader Offering Leveraging Economies of Scale
Microsoft Contributing to Open Systems
Microsoft Mega Data Center Supply Chain
Microsoft Leverages Open Compute Project to Bring Benefit to Enterprise Customers
Microsoft Assists Open Compute to Close The Loop On The Hardware Side
Microsoft Project Olympus Modular And Flexible
Microsoft Azure
Microsoft Azure Active Directory Has Synchronization
Microsoft Azure Has Scale
Mega Data Center Different from the Hyperscale Cloud
Mega Data Center Scaling
Mega Data Center Automatic Rules and Push-Button Actions
Amazon Capex for Cloud 2.0 Mega Data Centers
AWS Server Scale
Amazon North America
Innovation a Core Effort for Amazon
Amazon Offers the Richest Services Set
AWS Server Scale
On AWS, Customers Architect Their Applications
AWS Scale to Address Network Bottleneck
Networking A Concern for AWS Solved by Scale
AWS Regions and Network Scale
AWS Datacenter Bandwidth
Amazon (AWS) Regional Data Center
Map of Amazon Web Service Global Infrastructure
Rows of Servers Inside an Amazon (AWS) Data Center
Amazon Capex for Mega Data Centers
Amazon Addresses Enterprise Cloud Market, Partnering With VMware
Making Individual Circuits And Devices Unimportant Is A Primary Aim Of Fabric Architecture
Google Clos Network Architecture Topology Allows the Building a Non-Blocking Network Using Small Switches
You Have To Hit A Certain Scale Before Clos Networks Work
Clos Network
Digital Data Expanding Exponentially, Global IP Traffic Passes Zettabyte (1000 Exabytes) Threshold
Summary: Economies of Scale
WINTERGREEN RESEARCH,
WinterGreen Research Methodology
List of Figures
Enterprise Data Center as a Bottleneck: Scale Supports Non Blocking Network Inside
Building and More Efficient Processing
Figure 1. Slow Growth Companies Do Not Have Data Center Scale
Figure 2. Mega Data Center Fabric Implementation
Figure 3. Facebook Schematic Fabric-Optimized Datacenter Physical Topology
Figure 4. Facebook Automation of Mega Data Center Process
Figure 5. Facebook Altoona Positioning Of Global Infrastructure
Figure 6. FaceBook Equal Performance Paths Between Servers
Figure 7. FaceBook Data Center Fabric Depends on Scale
Figure 8. Facebook Fabric Operates Inside the Data Center, Fabric Is The Whole Data Center
Figure 9. Fabric Switches and Top of Rack Switches, Facebook Took a Disaggregated Approach
Figure 10. Exchange Of Data Between Servers Represents A Complex Automation Of Process20
Figure 11. Samsung Galaxy J3
Figure 12. Facebook Back-End Service Tiers And Applications Account for Machine-To-Machine Traffic Growth
Figure 1. Facebook Data Center Fabric Network Topology
Figure 13. Implementing building-wide connectivity
Figure 14. Modular Design Allows Users To Quickly Scale Capacity In Any Dimension
Figure 15. Facebook Back-End Service Tiers And Applications Functions
Figure 16. Using Fabric to Scale Capacity
Figure 17. Facebook Fabric: Pod Unit of Network
Figure 18. Server Pods Permit An Architecture Able To Implement Uniform High-Performance Connectivity
Figure 19. Non-Blocking Network Architecture
Figure 20. Facebook Automation of Cloud 2.0 Mega Data Center Process
Figure 21. Facebook Creating a Modular Cloud 2.0 mega data center Solution
Figure 22. Facebook Cloud 2.0 mega data center Fabric High-Level Settings Components
Figure 23. Facebook Mega Data Center Fabric Unattended Mode
Figure 24. Facebook Data Center Auto Discovery Functions
Figure 25. Facebook Automated Process Rapid Deployment Architecture
Figure 26. Google Douglas County Cloud 2.0 Mega Data Center
Figure 27. Google Data Center Efficiency Measurements
Figure 28. Google Andromeda Cloud High-Level Architecture
Figure 29. Google Andromeda Software Defined Networking (SDN)-Based Substrate Functions43
Figure 30. Google Compute Engine Load Balancing Functions
Figure 31. Google Compute Engine Load Balanced Requests Architecture
Figure 32. Google Compute Engine Load Balancing Scaling
Figure 33. Google Traffic Generated by Data Center Servers
Figure 34. Google Mega Data Center Multipathing: Implementing Lots And Lots Of Paths Between Each Source And
Destination
Figure 35. Google Mega Data Center Multipathing: Routing Destinations
Figure 36. Google Builds Own Network Switches And Software
Figure 37. Google Clos Topology Network Capacity Scalability
Figure 38. Schematic fabric-optimized Facebook datacenter physical topology
Figure 39. Google Jupiter Network Delivers 1.3 Pb/Sec Of Aggregate Bisection Bandwidth Across A Datacenter
Figure 40. Microsoft Azure Cloud Software Stack Hyper-V hypervisor
Figure 41. Microsoft Azure Running Docker Containers
Figure 42. Microsoft Data Center, Dublin, 550,000 Sf
Figure 43. Microsoft-Azure-Stack-Block-Diagram
Figure 44. Microsoft Azure Stack Architecture
Figure 45. Microsoft Data Centers
Figure 46. Microsoft Open Hardware Design: Project Olympus
Figure 47. Microsoft Open Compute Closes That Loop On The Hardware Side
Figure 48. Microsoft Olympus Product
Figure 49. Microsoft Azure Has Scale
Figure 50. Mega Data Center Cloud vs. Hyperscale Cloud
Figure 51. Amazon Web Services
Figure 52. Amazon North America Map
Figure 53. Amazon North America List of Locations
Figure 54. Woods Hole Bottleneck: Google Addresses Network Bottleneck with Scale
Figure 55. Example of AWS Region
Figure 56. Example of AWS Availability Zone
Figure 57. Example of AWS Data Center
Figure 58. AWS Network Latency and Variability
Figure 59. Amazon (AWS) Regional Data Center
Figure 60. A Map of Amazon Web Service Global Infrastructure
Figure 61. Rows of Servers Inside an Amazon (AWS) Data Center
Figure 62. Clos Network
Figure 63. Data Center Technology Shifting
Figure 64. Data Center Technology Shift

Download our eBook: How to Succeed Using Market Research

Learn how to effectively navigate the market research process to help guide your organization on the journey to success.

Download eBook

Share this report