Worldwide hyperscale data center markets implement cloud computing with shared resource and the aim, more or less achieved of providing 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. Economies of scale provide savings of between 50% to 100x less cost. These are savings that cannot be ignored by any person responsible or running a business. 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. This module addresses the issue of data center bottlenecks initially by drawing the reader;s attention to an analogy: navigating a sailboat through Woods Hole on Cape cos Massachusetts. The navigation is tricky - potentially dangerous. The bottleneck is potentially dangerous - for a combination of reasons. The current routinely flows through at over 4 knots, and can hit 7 knots. Full current on the nose makes transit slow and awkward. Full current from astern where the current runs slightly cross-channel causes awkward transit at an alarmingly rapid pace. Existing Enterprise Data Center as a Bottleneck: Think Woods Hole Viewed From The Cockpit: The Converging And Diverging Channels Can Look Like A Random Scattering Of Reds And Greens The existing data centers have a lot of entrenched culture and equipment. Mainframes represent 86% of transaction data processing and function generally in a manner separated from web traffic, though they doo handle some web traffic. One issue is, “What to do with the existing mainframes with its separate culture, functioning at 115% of capacity, and utterly impregnable security?” 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. There is an infrastructure layer that functions with simple processor, switch, and transceiver hardware orchestrated by software. There is an application layer that functions in a manner entirely separate from the infrastructure layer. The added benefit of automated application integration at the application layer brings massive savings to the IT budget, replacing manual process for application integration. The mainframe remains separate from this mega data center adventure, staying the course, likely to hold onto the transaction management part o data processing.” 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. Tis study takes a hard look at the alternatives open to business leaders. Everyone should know by now that the enterprise data center is dead. It will no longer exist in three years, that is the time it takes servers to become outdated and need replacement. In that timeframe, enterprises will migrate workload from the core enterprise servers to the large data center that can provide processing at half the cost of current processing. Maybe this forecast is too aggressive, but probably not. The mainframe stays around as detailed in a different WinterGreen Research report. The Hyperscale Data Centers: market size at $86.9.7 million in 2016 is anticipated to be $359.7 billion in 2023. The market has astoundingly rapid growth for a market that really is not yet well defined. The increasing scope of applications across different industries, manufacturing, medical, retail, game, and automotive, all industries really, is expected to drive demand over the forecast period to these unprecedented levels, reaching into the trillion-dollar market arenas soon. The hyperscale data centers are position to manage the explosion in web data, including data from IoT technology that is in the nascent stage with a huge growth potential, and has attracted large investments contributing to the industry growth. 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Companies Profiled Market Leaders • Facebook • Amazon (AWS) • Microsoft • Google Market Participants • 365 Data Centers • Amazon • Apple • Alibaba • Baidu • Chef • China Building A Cloud Computing Complex • China Mobile • Colocation America Data Center Bandwidth and Measurements • Colo-D • CoreSIte • CyrusOne • Digital Realty • Docker • DuPont Fabros Technology • Edge ConneX • Equinix • Facebook • Forsythe • Google • Hewlett Packard Enterprise • IBM • Intel • I/O • InterXion • Mesosphere • Microsoft • US National Security Agency • NEC • NTT / RagingWire • OpenStack Cloud Controller • Puppet • QTS • Qualcom • Rackspace • Red Hat / Ansible • Switch • Tango • Tencent • Twitter • Yahoo Key Topics • Hyperscale Data Center • Scale • Automation • Cloud Computing • Cloud 2.0 • Automatic Rules • Push-Button Actions • Cloud Application Integration • Container Control System • Open Source Container • Bare Metal To Container Controllers • Kubernetes Defacto Standard • Container Management System • Global IP Traffic • Mega Data Center • Google Kubernetes Defacto Standard Container • Digital Data Expanding Exponentially • Colocation Shared Infrastructure • Power and Data Center Fault Tolerance • 100 Gbps Adoption • Data Center Architectures • High-Performance Cloud Computing • Core Routing Platform • Datacenter Metrics • Mega Data Center Fabric Implementation • Digital Data • Open Source Container Control System • Defacto Standard Container Management System • Co-Location, and Social Media Cloud • Biggest Data Centers • Cloud 2.0 • Intelligent Cloud Segment
Table of Contents SEA CHANGE SERIES: CLOUD 2.0, MEGA DATA CENTERS Executive Summary 3 BOTTLENECKS: NAVIGATING WOODS HOLE IS TRICKY - POTENTIALLY DANGEROUS 5 Viewed From The Cockpit: The Converging And Diverging Channels Can Look Like A Random Scattering Of Reds And Greens 6 Using the Red and Green Boys to Navigate 7 Nine-Foot Bay Of Fundy Tide 10 Video and Data Streams Create Bottlenecks: 11 Demand for New Types of Cloud 11 The Right Type of Cloud: Mega Data Centers, Cloud 2.0 12 Mega Data Center Scale and Automation 22 Only Way To Realign Data Center Cost Structure Is To Automate Infrastructure Management And Orchestration 23 Entire Warehouse Building As A Single System 24 Half a Trillion Dollars 25 Two Tier Architecture to Achieve Simplicity 26 Bandwidth and Data Storage Demands Create Need For Application Integration 27 Cultural Shift 28 Line of Business Loses Control Of Hardware Servers 29 Cultural Change Needed to Move to Cloud 31 Adjusting to Rapid Change 32 Amazon Web Services (AWS) Fully Automatic, Self-Healing, Networked Mega Systems Inside A Building. 33 Data Center Design Innovation 34 Shift To An All-Digital Business Environment 35 System Operates As A Whole, At Fiber Optic Speeds, To Create A Fabric 35 Mega Data Center Market Description and Market Dynamics 36 Advantages of Mega Data Center Cloud 2.0: Multi-Threading 37 Cloud 2.0 Mega Data Center Multi-Threading Automates Systems Integration 38 Advantages of Mega Data Center Cloud 2.0: Scale 39 Infrastructure Scale 41 Intense Tide Of Data Causing Bottlenecks 42 Application Integration Bare Metal vs. Container Controllers 43 Workload Schedulers, Cluster Managers, And Container Controllers Work Together 44 Google Kubernetes Container 45 Google Shift from Bare Metal To Mega Data Center Container Controllers 46 Mesosphere / Open Source Mesos Tool 46 Mega Data Center TCO and Pricing: Server vs. Mainframe vs. Cloud vs. Cloud 2.0 47 Labor Accounts For 75% Of The Cost Of An Enterprise Web Server Center 48 Cloud 2.0 Systems And The Mainframe Computing Systems Compared 49 Cloud 2.0 Mega Data Center Lower Operations Cost 50 Cloud 2.0 mega Data Center Is Changing the Hardware And Data Center Markets 51 Scale Needed to Make Mega Data Center Containers Work Automatically 52 Multipathing 53 Cloud 2.0 Mega Data Centers Simple Repetitive Systems 53 Simplifying The Process Of Handling Load Balanced Requests 54 Google Servers Are Linked Logically, Each With Their Own Switch 55 Internet Apps Trillion Dollar Markets 56 Clos Simplicity 57 Clos-Based Topologies Increase Network Capacity 59 Mega Data Centers Embrace Open Source: Scale Is Everything 60 Open Cloud Server 61 Mainframe Provides Security 62 IBM Mainframe Handles Transactions, Business Analytics, and Mobile Apps 63 IBM Excels in Mastering Large Size Of Data To Be Managed 64 Transaction Based Mainframe 65 Microsoft Market Presence 66 Observers See Enterprise Data Center Moving to Cloud 67 Public Cloud Adoption 68 Microsoft Positioned To Become A Hyperscaler, Open Sourcing Hardware 69 Google Shift from Bare Metal To Container Controllers 70 Rapid Cloud Adoption: Google Says No Bare Metal 71 IBM Uses Bare Metal Servers: Mainframe Not Dead 72 VMware Photon Controller: Open Source Container Infrastructure Platform 73 Why Mega-Datacenters? 74 Data Center Switching 75 Software-Defined Networks Represent the Future 76 Broadcom 40 Gigabit Ethernet Optical Transceiver 78 40G, 100GBPS Transceivers Evolving Place in Mega Data Center: 79 NeoPhotonics 400 Gbps CFP8 PAM4 80 Applications: Equinix and Oracle 81 Oracle Cloud Platform 82 Reason Companies Move to Cloud 2.0 Mega Data Center 83 System On A Chip (SoAc) 84 Optical Transceiver Vendors Have Noticed That Mega Data Centers Are at the Center of Modern Processing 86 Fiber High Bandwidth Datacenters 87 400 Gbps Headed For The Data Center 87 100 Gbps Adoption 89 Optical Transceiver Vendors Have Noticed That Mega Data Centers Are at the Center of Modern Processing 89 Digital Workloads Increasing 90 Optical Transceiver High Growth as Shift to Cloud Occurs 91 Google Disruptive Technology: Base Orchestration Enhancements 92 Digital Realty Trust Lakeside Technology in Chicago: 1.1 Million Square Foot Data Center 93 Cisco Cloud Index: Cloud Replaces Data Centers 94 NTT Has Dominant Market Position 95 Enterprise Networking Rapid Transition 96 Public Cloud Adoption 97 Cisco CRS-3 Core Routing Platform 98 Evolution of Data Center Strategy 99 Systems Integration 101 AWS, Amazon Cloud Services Facebook, Google, and Microsoft: AWS leads in Mega Data Center Infrastructure 102 Conclusion 103 Cloud 2.0 Mega Data Center Evolution 103 APPENDIX A 104 Growth of Quantity of Data 104 Data Expanding And Tools Used To Share, Store And Analyze Evolving At Phenomenal Rates 104 Video Traffic 105 Cisco Analysis of Business IP Traffic 105 Increasing Video Definition: By 2020, More Than 40 Percent of Connected Flat- Panel TV Sets Will Be 4K 113 M2M Applications 115 Applications, For Telemedicine And Smart Car Navigation Systems, Require Greater Bandwidth And Lower Latency 117 Explosion of Data Inside Cloud 2.0 Mega Data Center with Multi Threading 122 Cloud 2.0 Mega Data Center Multi-Threading Automates Systems Integration 122 Fixed Broadband Speeds (in Mbps), 2015-2020 123 Internet Traffic Trends 127 Siemens Predicts IoT Growth 130 Appendix B: Things People Already Know About Cloud Computing 132 WINTERGREEN RESEARCH, 133 WinterGreen Research Methodology 134
List of Figures Enterprise Data Center as a Bottleneck: Think Woods Hole Figure 1. Existing Enterprise Data Center as a Bottleneck: Think Woods Hole 5 Figure 2. AWS Data Center Image 6 Figure 3. Achieving a Scalable Architecture from Simple Units 7 Figure 4. Facebook Sample Pod: Unit of Network 8 Figure 5. Facebook Data Center Fabric Network Topology 9 Figure 6. Cloud 2.0 Mega Data Center 11 Figure 7. Cloud 2.0 Mega Data Centers Support 1.5 Billion Facebook Users Worldwide. 12 Figure 8. Facebook DuPont Fabros Technology Ashburn, VA Data Center 24 Figure 9. SOA Foundation Business, Infrastructure, and Data Information Architecture 27 Figure 10. AWS Market Leader In Cloud Computing 32 Figure 11. 538,000SF: i/o Data Centers and Microsoft Phoenix One, Phoenix, Ariz. 34 Figure 12. Phoenix, Arizona i/o Data Center Design Innovations 34 Figure 13. Key Challenges Enterprise IT Datacenters: 36 Figure 14. Multi-threading Manages Pathways From One Node To Another Node 37 Figure 15. Cloud Types of System Implementation 38 Figure 16. Google Mega Data Center Scale 39 Figure 17. Key Advantage of Cloud 2.0 Mega IT Datacenters: 40 Figure 18. NTT RagingWire Ashburn Va2 Data Center 41 Figure 19. AWS Region Diagram 42 Figure 20. Google Shift from Bare Metal To Container Controllers Advantages 45 Figure 21. Cloud 2.0 Mega Data Center Advantages 51 Figure 22. Images for Google Container Cloud 3.0 Mega Data Centers 52 Figure 23. Facebook Fifth Data Center Fort Worth Complex. 53 Figure 24. Google Compute Engine Load Balanced Requests Architecture 56 Figure 25. Google Extends App Indexing 57 Figure 26. Google Clos Multistage Switching Network 58 Figure 27. The size of the basic switch element has an impact on the total number of switching nodes requi Google Clos Multistage Switching Network 59 Figure 28. Mainframe Security 62 Figure 29. IBM Mainframe System z/OS 63 Figure 30. z13 Server Benefits 64 Figure 31. Aspects of Cloud 65 Figure 32. Observers See Enterprise Data Center Moving to Cloud 67 Figure 33. Broadcom 40 Gigabit Ethernet Optical Transceiver 78 Figure 34. 40G, 100GBPS Transceiver Target Markets 79 Figure 35. NeoPhotonics 400G CFP8 PAM4 80 Figure 36. Neophotonics 400 Gbps CFP8 PAM4 Features 80 Figure 37. Equinix LD6 data center in Slough, England 81 Figure 38. Cloud 2.0 Mega Data Centers Are Demanding Significant Amounts Of Power And Network Management 85 Figure 39. Flow of Digital Data Creating Bottlenecks In Enterprise Data Center 90 Figure 40. Google Base Orchestration Enhancement Functions 92 Figure 41. Digital Realty Trust Lakeside Technology Center Industrial- Strength Power And Fiber Infrastructure 93 Figure 42. NTT RagingWire Data Centers Image 95 Figure 43. Google Andromeda Cloud High-Level Architecture 99 Figure 44. Amazon AWS Global Cloud Infrastructure 102 Figure 45. Cisco VNI Forecast Overview 106 Figure 46. The Cisco VNI Forecast—Historical Internet Context 107 Figure 47. Global Devices and Connections Growth 108 Figure 48. Average Number of Devices and Connections per Capita 110 Figure 49. Global IP Traffic by Devices 110 Figure 50. Global Internet Traffic by Device Type 111 Figure 51. Global 4K Video Traffic 113 Figure 52. Global IPv6-Capable Devices and Connections Forecast 2015–2020 114 Figure 53. Projected Global Fixed and Mobile IPv6 Traffic Forecast 2015–2020 115 Figure 54. Global M2M Connection Growth 116 Figure 55. Global M2M Connection Growth by Industries 117 Figure 56. Global M2M Traffic Growth: Exabytes per Month 118 Figure 57. Global Residential Services Adoption and Growth 119 Figure 58. Global IP Traffic by Application Category 120 Figure 59. Mobile Video Growing Fastest; Online Video and Digital TV Grow Similarly 121 Figure 60. Global Cord Cutting Generates Double the Traffic 121 Figure 61. Fixed Broadband Speeds (in Mbps), 2015–2020 123 Figure 62. Future of Wi-Fi as Wired Complement 124 Figure 63. Global IP Traffic, Wired and Wireless 125 Figure 64. Global Internet Traffic, Wired and Wireless 126 Figure 65. Cisco VNI Forecasts 194 EB per Month of IP Traffic by 2020 129 Figure 66. Cisco Forecast of Global Devices and Connections Growth 130 Figure 67. Siemens Perspective of Billions of Things, Trillions of Dollars 131 Figure 68. Benefits of Cloud Computing 132
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