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Published: Mar, 2017 | Pages:
846 | Publisher: WinterGreen Research
Industry: ICT | Report Format: Electronic (PDF)
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. 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 choices for migration are to regular cloud data centers that remain mired in manual process and lack of automation vs. cloud 2.0 mega data centers that implement automated process inside a building that has scale. The hesitation that companies have had in migrating to the cloud have been concerns about security and protecting the privacy of the corporate data, protecting the crown jewels of the company so to speak. But the security in a shared data center can be as good or even better than security in an enterprise data center. The large independent players profiled in this report have found ways to protect their clients and have very sophisticated systems in place for serving their clients. At this point security concerns are a myth. The much greater risk is that a competitor will be able to cut operating costs by a half or even 500% by moving to cloud data center configurations, providing insurmountable competitive advantage. The commercial data center providers are sophisticated and reliable. The good ones have been around for years, building systems that work in shared environments that are able to protect the integrity of each client’s data. At this point a good independent analyst is the best source for judging what cloud environments best suit a client. This study outlines the inevitability of migrating to cloud. Enterprise data centers are in melt down mode. When technology markets move, they move very quickly and this cloud data center market has been artificially protected by incumbent vendors scaring existing customers about security vulnerabilities, so when the air is let out of the myth, the existing IT culture, it is likely to collapse. As the team at WinterGreen Research wrote the optical transceiver study, interviews revealed a startling observation: “The linear data center is outdated, it has become a bottleneck in the era of the digital economy, the quantity of data has outpaced the ability of the data center to manage and the traditional data center has become a bottleneck. Have you seen what is going on in the mega data centers?” The mega data centers are different from cloud computing and different from the enterprise linear computing data centers, the mega data centers are handling data at the speed of light. This represents a huge change in computing going forward, virtually all the existing data centers are obsolete. This study and the one for CEOs addresses these issues. As we build data centers with the capacity to move data inside at 400 GB per second, more data can be moved around. More analysis can be done, more insight can be gained, more alerts can trigger robotic response. The value of automated process to business has been clear since the inception of computing. Recently, automated process has taken a sudden leap forward. Many companies had been stuck in their enterprise data center spending patterns encompassing manual process. In the enterprise data center the vast majority of IT administrative expenditures are for maintenance rather than for addressing the long-term strategic initiatives. Companies that remained in the manual administrative spending on the data center mode including IBM and Hewlett Packard and most of their customers failed to grow at the same pace as the rapid growth tech companies, Google, Facebook, Amazon, and Microsoft. Business growth depends on technology spending that is intelligent, not on manual labor spending. The manual labor is always slow and error prone, spending on manual process is counterproductive vs automation spending. So many IT processes have been manual, tedious, and error prone that they have held the company back relative to the competition. Mega data centers get rid of that problem. The companies that invested in mega data centers and automated process for the data centers have had astounding growth, while the companies stuck with ordinary data centers are mired in slow growth mode. Topology, technology and design favor building a digital business solutions. Vendors offer colocation-based, programmable networking centers provide data center interconnect fabric. A fabric allows dynamic interconnection between enterprise peers, cloud providers, communications providers and a growing marketplace of service providers. 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. WinterGreen Research is an independent research organization funded by the sale of market research studies all over the world and by the implementation of ROI models that are used to calculate the total cost of ownership of equipment, services, and software. The company has 35 distributors worldwide, including Global Information Info Shop, Market Research.com, Research and Markets, electronics.ca, and Thompson Financial. It conducts its business with integrity. The increasingly global nature of science, technology and engineering is a reflection of the implementation of the globally integrated enterprise. Customers trust wintergreen research to work alongside them to ensure the success of the participation in a particular market segment. WinterGreen Research supports various market segment programs; provides trusted technical services to the marketing departments. It carries out accurate market share and forecast analysis services for a range of commercial and government customers globally. These are all vital market research support solutions requiring trust and integrity. 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 HYPERSCALE DATACENTERS EXECUTIVE SUMMARY 42 Hyperscale Data Center Scale and Automation 42 Cloud 2.0 Mega Data Center Fabric Implementation 45 Cloud 2.0 Mega Data Center Different from the Hyperscale Cloud 47 Cloud 2.0 Mega Data Center Automatic Rules and Push-Button Actions 49 Making Individual Circuits And Devices Unimportant Is A Primary Aim Of Fabric Architecture 50 Digital Data Expanding Exponentially, Global IP Traffic Passes Zettabyte (1000 Exabytes) Threshold 52 Google Kubernetes Open Source Container Control System 53 Google Kubernetes Defacto Standard Container Management System 53 Google Shift from Bare Metal To Container Controllers 54 Cloud 2.0 Mega Data Center Market Driving Forces 54 Hyperscale Data Center Market Shares 58 Cloud Datacenter, Co-Location, and Social Media Cloud, Revenue Market Shares, Dollars, Worldwide, 2016 59 Cloud 2.0 Mega Data Center Market Forecasts 60 1. HYPERSCALE DATACENTERS: MARKET DESCRIPTION AND MARKET DYNAMICS 62 1.1 Data Center Manager Not Career Track for CEO 62 1.1.1 Colocation Shared Infrastructure 65 1.1.2 Power and Data Center Fault Tolerance 68 1.2 Fiber High Bandwidth Datacenters 71 1.3 100 Gbps Headed For The Data Center 72 1.3.1 100 Gbps Adoption 74 1.4 Scale: Cloud 2.0 Mega Data Center Containers 75 1.4.1 Data Center Architectures Evolving 75 1.4.2 High-Performance Cloud Computing Market Segments 78 1.4.3 Cisco CRS-3 Core Routing Platform 79 1.5 Evolution of Data Center Strategy 79 1.6 Cabling in The Datacenter 82 1.6.1 Datacenter Metrics 86 1.6.1 Digitalization Forcing Data Centers to Evolve 87 1.6.2 A One-Stop Shop 87 1.6.3 Growing With Business 88 2. HYPERSCALE DATACENTERS MARKET SHARES AND FORECASTS 89 2.1 Hyperscale Data Center Scale and Automation 89 2.1.1 Cloud 2.0 Mega Data Center Fabric Implementation 92 2.1.2 Cloud 2.0 Mega Data Center Different from the Hyperscale Cloud 94 2.1.3 Cloud 2.0 Mega Data Center Automatic Rules and Push-Button Actions 95 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 98 2.1.6 Google Kubernetes Open Source Container Control System 99 2.1.7 Google Kubernetes Defacto Standard Container Management System 100 2.1.8 Google Shift from Bare Metal To Container Controllers 101 2.1.9 Cloud 2.0 Mega Data Center Market Driving Forces 101 2.2 Hyperscale Data Center Market Shares 105 2.2.1 Cloud Datacenter, Co-Location, and Social Media Cloud, Revenue Market Shares, Dollars, Worldwide, 2016 106 2.2.2 Cloud 2.0 Mega Datacenter Cap Ex Spending Market Shares Dollars, Worldwide, 2016 108 2.2.3 Amazon Capex for Cloud 2.0 Mega Data Centers 112 2.2.4 Amazon (AWS) Cloud 113 2.2.5 Amazon Datacenter Footprint 113 2.2.6 Cloud 2.0 Mega Data Center Social Media and Search Revenue Market Shares, Dollars, 2016 113 2.2.7 Top Hyperscale Companies 116 2.2.8 Biggest Data Centers 117 2.2.9 Microsoft Azure 126 2.2.10 Microsoft Data Center, Dublin, 550,000 Sf 128 2.2.11 Microsoft Data Center Container Area in Chicago. 129 2.2.12 Microsoft Quincy Data Centers, 470,000 Square Feet 131 2.2.13 . Microsoft San Antonio Data Center, 470,000 SF 132 2.2.14 Microsoft 3rd Data Center in Bexar Could Employ 150 133 2.2.15 Microsoft Builds the Intelligent Cloud Platform 134 2.2.16 Microsoft's datacenter footprint 135 2.2.17 Google Datacenter Footprint 136 2.2.18 Apple Datacenter Footprint 137 2.2.1 Facebook Datacenter Footprint 138 2.2.2 Chef Web-Scale Automation Of Systems Integration In The Cloud 140 2.2.3 Docker Open Platform 140 2.2.4 OpenStack 141 2.2.5 Ragingwire 142 2.2.6 Simplifying Messaging is a Priority for Goldman Sachs Implementing Automation 142 2.2.7 IBM 142 2.3 Cloud 2.0 Mega Data Center Market Forecasts 143 2.3.1 Market Segments: Web Social Media, Web Wireless Apps, Enterprise / Business Transactions, Co-Location, And Broadcast / Communications 145 2.3.2 Cloud 2.0 Mega Data Center Is Changing The Hardware And Data Center Markets 151 2.4 Hyperscale Data Center Storage Market Analysis 152 2.5 Mega-Datacenter: Internet Giants Continue To Increase Capex 154 2.5.1 Apple Datacenter Footprint 154 2.5.2 Google Datacenter Footprint 155 2.5.3 Microsoft Datacenter Footprint 156 2.5.4 Amazon Datacenter Footprint 157 2.5.5 Facebook Datacenter Footprint 158 2.5.6 Service Tiers and Applications 159 2.5.7 M2M industry 160 2.5.8 Cloud 2.0 Mega Data Center Segments 161 2.5.9 Cloud 2.0 Mega Data Center Positioning 161 2.6 Cloud 2.0 Mega Data Center Size 163 2.6.1 Cloud 2.0 Mega Data Centers 164 2.6.2 Public Cloud Infrastructure 165 2.7 Multi-Tenant Data Center Market Shares and Revenue Forecasts 167 2.7.1 Colocation Providers 168 2.7.2 Carrier-Neutral Colocation Providers 169 2.7.3 Wholesale Data Center Providers 170 2.7.4 Largest Data Centers 174 2.8 Cloud 2.0 Mega Data Center 175 2.8.1 Cloud 2.0 Mega Data Center Is Changing The Hardware And Data Center Markets 176 2.8.2 Storage SATA Drives Meet Mega Data Center Requirements 178 2.8.1 Data Center Switching 179 2.8.2 ASIC Switch Vendors 182 2.8.3 Data Center Rack Market 183 2.9 Hyperscale Datacenter Future 186 2.9.1 Public Cloud Services Revenue 194 2.10 Edge Cloud Data Centers 194 2.10.1 Edge Data Center Definition 196 2.11 Data Expanding And Tools Used To Share, Store And Analyze Evolving At Phenomenal Rates 197 2.11.1 Video Traffic 199 2.11.2 Cisco Analysis of Business IP Traffic 199 2.11.3 Increasing Video Definition: By 2020, More Than 40 Percent of Connected Flat- Panel TV Sets Will Be 4K 206 2.11.4 M2M Applications 208 2.11.5 Applications, For Telemedicine And Smart Car Navigation Systems, Require Greater Bandwidth And Lower Latency 210 2.11.6 Explosion of Data Inside Cloud 2.0 Mega Data Center with Multi-Threading 215 2.11.7 Cloud 2.0 Mega Data Center Multi-Threading Automates Systems Integration 215 2.11.8 Fixed Broadband Speeds (in Mbps), 2015-2020 215 2.11.9 Internet Traffic Trends 219 2.11.10 Internet of Things 222 2.11.11 The Rise of the Converged “Digital Enterprise” 223 2.11.12 Enterprise Data Centers Give Way to Commercial Data Centers 223 2.12 Hyperscale Data Center TCO and Pricing: Server vs. Mainframe vs. Cloud vs. Cloud 2.0 224 2.12.1 Labor Accounts For 75% Of The Cost Of An Enterprise Web Server Center 225 2.12.2 Cloud 2.0 Systems And The Mainframe Computing Systems Compared 225 2.12.3 Cloud 2.0 and Mainframe Implements Shared Resource 227 2.12.4 Average Operating Density Of Data Centers 227 2.12.5 Mainframe Capacity vs. Cloud 2.0 228 2.12.6 Enterprise IT Departments TCO / ROI Custom Data Center Cost Analysis 229 2.12.7 Server, Mainframe, Cloud, and Cloud 2.0 Cost Comparisons 232 2.12.8 Server to MIPS Conversion Calculations 237 2.12.9 Mainframe Updates And Cost Efficiencies 239 2.12.10 Cost of Cloud Computing 240 2.12.11 Types of Cloud Computing 242 2.12.12 Software-Defined Infrastructure 243 2.12.13 Scale 244 2.13 Cloud Hyperscale Data Center Regional Market Analysis 249 2.13.1 US Data Center REITs 251 2.13.2 Chicago Data Center Supply Grows 253 2.13.3 Digital Realty Trust Largest Data Center: Cermak, Chicago, 1.1 Million Square Feet 255 2.13.4 US Data Center Activity 258 2.13.5 Cloud Demand Globally 259 2.13.6 Landlords Adapt to Cloud Globally 260 2.13.7 Amazon, Google Detail Next Round of Cloud Data Center Launches 261 2.13.1 Cloud Data Centers Market in Europe 262 2.13.2 Cloud Data Centers Market in Ireland 263 2.13.3 Japanese Data Centers 263 3. HYPERSCALE DATACENTER INFRASTRUCTURE DESCRIPTION 265 3.1 Amazon Cloud 265 3.1.1 Amazon AWS Regions and Availability Zones 266 3.1.2 Amazon Addresses Enterprise Cloud Market, Partnering With VMware 268 3.1.3 AWS Achieves High Availability Through Multiple Availability Zones 270 3.1.4 AWS Improving Continuity Replication Between Regions 270 3.1.5 Amazon (AWS) Meeting Compliance and Data Residency Requirements 271 3.1.6 AWS Step Functions Software 272 3.1.7 Amazon QuickSight Software 273 3.1.8 Amazon North America 275 3.1.9 AWS Server Scale 278 3.1.10 AWS Network Scale 279 3.2 Facebook 288 3.2.1 Dupont Fabros Constructing Second Phase In Acc7 Represents An Expanded Relationship with Facebook 290 3.2.2 Facebook $1B Cloud 2.0 Mega Data Center in Texas 291 3.2.3 Facebook $300 Million Cloud 2.0 Mega Data Center in Iowa 291 3.2.4 Fort Worth Facebook Mega-Data Center 294 3.2.5 Facebook Forest City, N.C. Cloud 2.0 mega data center 296 3.2.6 Data Center Fabric, The Next-Generation Facebook Data Center Network 297 3.2.1 Facebook Altoona Data Center Networking Fabric 298 3.2.2 Facebook Clusters and Limits Of Clusters 303 3.2.3 Facebook Fabric 308 3.2.4 Facebook Network Technology 312 3.2.5 Facebook Fabric Gradual Scalability 314 3.2.6 Facebook Mega Datacenter Physical Infrastructure 315 3.2.7 Facebook Large Fabric Network Automation 317 3.2.8 Facebook Fabric Data Center Transparent Transition 324 3.2.9 Facebook Large-Scale Network 325 3.3 Google Meta Data Centers 331 3.3.1 Google Datacenter Network 332 3.3.2 Google Office Productivity Dynamic Architecture 333 3.3.3 Google Search Engine Dynamic Architecture 336 3.3.4 BigFiles 337 3.3.5 Repository 337 3.3.6 Google Clos Networks 338 3.3.7 Google B4 Datacenter WAN, a SDN 341 3.3.8 Google Programmable Access To Network Stack 343 3.3.9 Google Compute Engine Load Balancing 347 3.3.10 Google Compute Engine (GCE) TCP Stream Performance Improvements 350 3.3.11 Google The Dalles, Oregon Cloud 2.0 Mega Data Center 355 3.3.12 Lenoir, North Carolina 356 3.3.13 Google Hamina, Finland 357 3.3.14 Google Mayes County 359 3.3.15 Google Douglas County 361 3.3.16 Google Cloud 2.0 Mega Data Center St Ghislain, Belgium 364 3.3.17 Google Council Bluffs, Iowa Cloud 2.0 Mega Data Center 365 3.3.18 Google Douglas County Cloud 2.0 Mega Data Center 368 3.3.19 Google $300m Expansion of Existing Metro Atlanta Data Center 369 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 371 3.3.21 Google Cloud 2.0 Mega Data Center in Finland 373 3.3.22 Google Switches Provide Scale-Out: Server And Storage Expansion 376 3.3.23 Google and Microsoft 25G Ethernet Consortium 384 3.3.24 Google Workload Definitions 386 3.3.25 Google Kubernetes Container 389 3.3.26 Google Optical Networking 390 3.3.27 Google Data Center Efficiency Measurements 392 3.3.28 Google Measuring and Improving Energy Use 392 3.3.29 Google Comprehensive Approach to Measuring PUE 393 3.3.30 Q3 2016 PUE Performance 395 3.4 Baidu 400 3.4.1 Baidu Data Center, Shanxi 400 3.4.2 China Mobile Working With Leading Chinese Language Search Engine Baidu And Schneider Electric 402 3.5 China Mobile 403 3.6 Tencent 404 3.6.1 Tencent $1 Billion Midwest China Data Center 404 3.6.2 Tencent Facilitates Cloud 405 3.7 Smart City Data Center Comes Online 406 3.7.1 SNIA China Big Data Project 406 3.8 Alibaba 407 3.9 Yahoo 408 3.10 Microsoft 408 3.10.1 Microsoft .Net Dynamically Defines Reusable Modules 413 3.10.2 Microsoft Combines Managed Modules into Assemblies 414 3.10.3 Microsoft Architecture Dynamic Modular Processing 414 3.10.4 Microsoft Builds Azure Cloud Data Centers in Canada 416 3.10.5 Microsoft Dublin Cloud 2.0 mega data center 417 3.10.6 Microsoft Data Center Largest in U.S. 418 3.10.7 Microsoft Crafts Homegrown Linux For Azure Switches 419 3.10.8 Microsoft Azure Cloud Switch 421 3.10.9 Microsoft Azure CTO Cloud Building 423 3.10.10 Microsoft Cloud 2.0 Mega Data Center Multi-Tenant Containers 424 3.10.11 Microsoft Managed Clustering and Container Management: Docker and Mesos 426 3.10.12 Kubernetes From Google or Mesos 427 3.10.13 Microsoft Second Generation Open Cloud Servers 427 3.10.14 Azure Active Directory 427 3.10.15 Microsoft Azure Stack Platform Brings The Suite Of Azure Services To The Corporate Datacenter 429 3.10.16 Hardware Foundation For Microsoft Azure Stack 437 3.11 Apple 442 3.11.1 Apple Invests €1.7 Billion in European Data Centres 442 3.11.2 Apple Builds $2 Billion Cloud 2.0 mega data center in Arizona 444 3.12 Goldman Sachs 444 3.12.1 Simplifying Messaging is a Priority for Goldman Sachs 445 3.12.2 Goldman Sachs Cloud 2.0 Mega Data Center 446 3.12.3 Goldman Sachs Security for The Financial Services Organization 446 3.12.4 Goldman Sachs: Containers Have Real Promise at The Institution 447 3.12.5 Goldman Sachs Cloud Computing 449 3.13 Fidelity Investments 450 3.13.1 Fidelity Investments Core Unit Building Blocks 450 3.13.2 Fidelity Centercore Design 451 3.13.3 Fidelity Ties Into Modular Momentum Among Financials 451 3.14 QTS Custom Data Centers 452 3.14.1 QTS Multi Tenant Data Center 452 3.14.2 QTS Critical Facilities Management 454 3.15 IBM 454 3.15.1 IBM z Systems in the Cloud 455 3.15.2 IBM Cloud Managed Services on z Systems 458 3.15.3 IBM® Cloud Managed Services® on z Systems 460 3.15.4 Linux-Based Solutions Under IBM z/VM® Shared Infrastructure Support Hybrid Workloads 460 3.15.5 IBM Cloud Managed Services on z Systems 467 3.15.6 IBM Builds a Cloud 2.0 mega data center in India 468 3.15.7 IBM Outsourcing 470 3.15.8 IBM Server SAN Software-Led Infrastructure 470 3.15.9 IBM Partnership with American Airlines 472 3.15.10 IBM Cloud Momentum In The Airlines Industry 474 3.15.11 IBM CICS 474 3.15.12 IBM z13 Mainframe Hardware Platform 475 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 482 3.16 DuPont Fabros Technology 482 3.16.1 Data Center Market Trends For Wholesale Commissioned MW Trend 484 3.17 Hewlett Packard 492 3.17.1 HP Hyperscale Composable Infrastructure 492 3.17.2 HP Data Center Control Human Tools With Point And Click Interfaces 493 3.17.3 HP Data Center Hyperscaler Control 493 3.17.4 Hewlett Packard Project Synergy Composable Infrastructure Initiative 495 3.18 NTT Raging Wire 498 3.19 Rackspace 507 3.19.1 Rackspace Power 509 3.19.2 Rackspace Network 509 3.20 Equinix 510 3.20.1 HK3 Hong Kong - Data Center 512 3.20.2 Equinox Supports Diverse And Rapidly Growing International Business Clusters, Rich Industry Ecosystem 514 3.20.3 Equinox Dublin Metro Facilities 515 3.20.4 Equinix Brings Online Sixth London Data Center 518 3.20.5 Equinix Performance Hub 519 3.21 Twitter 527 3.22 Bank of America 528 3.23 Wells Fargo 529 3.24 eBay 530 3.25 Switch SuperNAP 532 3.25.1 Switch International Expansion 538 3.25.2 Switch Cloud Connectivity Charges Lowered 540 3.25.3 Switch SUPERNAP High Density Designs 542 3.25.4 Red Hat Ansible 544 3.25.5 Red Hat Ansible Architecture, Agents, And Security 544 3.25.6 Red Hat Ansible Advanced Features 545 3.25.7 Red Hat / Ansible 548 3.26 Cisco 549 4. HYPERSCALE DATACENTERS RESEARCH AND TECHNOLOGY 552 4.1 Enterprise IT Control Centers 552 4.2 Open Compute Project (OCP), 554 4.2.1 Microsoft Investment in Open Compute 556 4.2.2 Microsoft Leverages Open Compute Project to Bring Benefit to Enterprise Customers 557 4.3 Open Source Foundation 557 4.3.1 OSPF Neighbor Relationship Over Layer 3 MPLS VPN 558 4.4 Equinix Expansion of LD6 International Business Exchange Datacenter 561 4.4.1 Equinix and Oracle Collaborate to Bring Oracle Cloud Services to Equinix Cloud Exchange in Six Global Markets 561 4.4.2 Oracle Cloud Platform 562 4.5 Power Management 563 4.6 M2M Industry 564 4.7 Equinix Cloud Exchange Interconnection Solution 565 4.8 System On A Chip (SoCs) for Cloud 2.0 Mega Data Centers 566 4.8.1 A New Class of Low-Power Server SoCs 567 4.8.2 Re-Architecting the Network: Software Defined Networks (SDNs) 568 4.8.3 Synopsys SoCs 570 4.8.4 Open Network Foundation (ONF) Addresses Need For SoC Lower Power Consumption 571 4.9 Dynamic Systems 579 4.9.1 Robust, Enterprise-Quality Fault Tolerance 579 4.10 Cache / Queue 581 4.11 Multicast 583 4.12 Performance Optimization 584 4.13 Fault Tolerance 585 4.13.1 Gateways 586 4.13.2 Promise Of Web Services 586 4.14 IP Addressing And Directory Management 587 4.14.1 Dynamic Visual Representations 589 4.14.2 Application Integration 590 4.14.3 Point Applications 591 4.14.4 Fault Tolerance and Redundancy Solutions 592 4.14.5 Goldman Sachs Open Compute Project 593 4.15 Robust, Quality Cloud Computing 594 4.16 Networking Performance 601 4.17 Data Center Bandwidth Pricing: 602 5. HYPERSCALE DATACENTERS COMPANY PROFILES 605 5.1 365 Data Centers 605 5.2 Amazon 605 5.2.1 Amazon Business 606 5.2.2 Amazon Competition 606 5.2.3 Amazon Description 608 5.2.4 Amazon Revenue 612 5.3 Apple 613 5.3.1 Apple / AuthenTec 614 5.3.2 Authentec Revenue Recognition - Smart Sensors 615 5.3.3 Apple 617 5.3.4 Apple Business Strategy 618 5.3.5 Apple Products 618 5.3.6 Apple iPhone 619 5.3.7 Apple Mac Hardware Products 619 5.3.8 Apple iPod 620 5.3.9 Apple iTunes® 621 5.3.10 Apple Mac App Store 621 5.3.11 Apple iCloud 622 5.3.12 Apple Software Products and Computer Technologies 622 5.3.13 Apple Operating System Software iOS 622 5.3.14 Apple Mac OS X 623 5.3.15 Apple Third-Largest Mobile Phone Maker 623 5.3.16 Apple Revenue 623 5.3.17 Apple Regional Segment Operating Performance 625 5.3.18 Apple Net Sales 627 5.3.19 Apple iPhone Shipments 628 5.3.20 Apple iPad Shipments 629 5.3.21 Apple Revenue 630 5.4 Alibaba 632 5.4.1 Alibaba Cloud Expands Data Centers, Steps Up Challenge to Amazon, Microsoft 632 5.4.2 Alibaba Cloud Unit has 2.3 Million Customers 633 5.4.3 Alibaba Seeks To Leverage Applications Integration via Automated Cloud Processes 634 5.4.4 Alibaba Cloud Middle East 636 5.4.5 Alibaba Cloud Europe 636 5.4.6 Alibaba Cloud Australia 637 5.4.7 Alibaba Cloud Japan 637 5.5 Baidu 637 5.5.1 Baidu Platform 638 5.5.2 Baidu Mobile Era Cloud, Mobile Search 640 5.5.3 Baidu, The Largest Chinese Search Engine 641 5.5.4 Baidu Self-Designed 10Gb TOR Switch 641 5.5.5 Baidu ARM on a Large Scale 641 5.5.6 Baidu Self-Designed SSD 642 5.5.7 Baidu Customized Rack Servers 643 5.5.8 Baidu Buys Modular Data Center From Schneider 646 5.6 Chef 647 5.6.1 Chef Customers 653 5.6.1 Chef Partner Ecosystem Includes AWS, Dell, and Rackspace 653 5.6.2 Chef Compliance, Workflow, and Automation Support 654 5.6.3 Chef Professional DevOps Practice Service Partners 656 5.6.4 Chef Technology Partners Build World-Class Integrations with Chef 657 5.6.5 Chef Value Added Resellers 658 5.7 China Building A Cloud Computing Complex 660 5.8 China Mobile 660 5.9 Colocation America Data Center Bandwidth and Measurements 663 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 664 5.11 CoreSIte 666 5.11.1 CoreSite Cloud Data Center Leasing Accelerates 666 5.11.2 Key Markets Update 667 5.12 CyrusOne 668 5.13 Digital Realty 668 5.14 Docker 671 5.15 DuPont Fabros Technology 674 5.15.1 DuPont Fabros Technology Customer Analysis 674 5.15.2 DuPont Fabros Operating Portfolio: Tier 1 Markets 683 5.16 Edge ConneX 686 5.16.1 EdgeConneX Hyperscale Cloud Anchor 686 5.16.2 EdgeConneX Disrupts Incumbent Data Center Providers 688 5.16.3 EdgeConneX ‘Disruptive Network Positioning 689 5.16.4 EdgeConneX - US Strategy 690 5.16.5 Edge Data Center Providers Changing the Internet’s Geography 691 5.16.6 EdgeConneX Building at the Internet Edge 693 5.16.7 EdgeConneX Demand Dynamics 693 5.16.8 EdgeConneX Funding 695 5.16.9 EdgeConneX Software Supports Speed to Market 695 5.16.10 EdgeConneX Cloud Strategy 696 5.16.11 EdgeConneX in Europe 696 5.16.12 Liberty Global Anchor Tenant in London 698 5.17 Equinix 699 5.17.1 EQUINIX, INC. Revenues 700 5.17.2 Equinix Purchase of Digital Realty Trust 701 5.17.3 Equinix Acquisition of TelecityGroup 702 5.18 Facebook 702 5.18.1 Facebook Technology 703 5.18.2 Facebook Sales and Operations 703 5.18.3 Facebook Management Discussion 703 5.18.4 Facebook Revenue 705 5.18.5 Facebook 706 5.18.6 Facebook App Draining Smart Phone Batteries 707 5.18.7 Facebook Messaging Provides Access to User Behavioral Data 707 5.18.8 Facebook Creating Better Ads 708 5.18.9 Facebook Next Generation Services 708 5.18.10 Facebook Platform 709 5.18.11 Facebook Free Basics 710 5.18.12 Facebook AI 710 5.18.13 Facebook Revenue 711 5.18.14 Facebook Revenue Growth Priorities: 712 5.18.15 Facebook Average Revenue Per User ARPU 713 5.18.16 Facebook Geographical Information 714 5.18.17 Facebook WhatsApp 714 5.18.18 Facebook WhatsApp Focusing on Growth 715 5.19 Forsythe 716 5.19.1 Forsythe Data Centers An Adaptable Facility to Meet Clients’ Evolving Needs 717 5.19.2 Forsythe Data Centers All-Encompassing Network 717 5.20 Google 719 5.20.1 Google Revenue 719 5.20.2 Google 720 5.20.3 Google Search Technology 721 5.20.4 Google Recognizes World Is Increasingly Mobile 721 5.20.5 Google Nest 722 5.20.6 Google / Nest Protect 723 5.20.7 Google / Nest Safety History 724 5.20.8 Google / Nest Learning Thermostat 725 5.20.9 Google Chromecast 726 5.21 Hewlett Packard Enterprise 727 5.22 IBM 729 5.22.1 IBM Strategy 729 5.22.2 IBM Cloud Computing 731 5.22.3 IBM Business Model 731 5.22.4 IBM Solutions 732 5.22.5 IBM Blockchain 732 5.22.6 IBM PureData System for Transaction Analysis 734 5.22.7 IBM Business Partners 735 5.22.8 IBM Messaging Extension for Web Application Pattern 736 5.22.9 IBM MobileFirst 736 5.22.10 IBM Business Analytics and Optimization Strategy 737 5.22.11 IBM Growth Market Initiatives 737 5.22.12 IBM Business Analytics and Optimization 738 5.22.13 IBM Strategy Addresses Volatility of Information Technology (IT) Systems 739 5.22.14 IBM Smarter Planet 740 5.22.15 IBM Business Revenue Segments And Capabilities 741 5.22.16 IBM Software Capabilities 745 5.23 Intel 752 5.17.1 Intel Business 754 5.23.2 Intel Company Strategy 755 5.23.3 Intel In The Internet Of Things Market Segment 757 5.23.4 Intel Competitive Advantages 757 5.24 I/O 759 5.25 InterXion 759 5.25.1 Interxion Revenue 760 5.26 Mesosphere 760 5.26.1 Modern App Platform Services 761 5.26.2 Mesosphere Enterprise DC/OS Hybrid cloud independence 762 5.26.3 Mesosphere / Open Source Mesos Tool 763 5.26.4 Modern Enterprise Applications Use Of Open Source Software 764 5.26.5 Banding Together To Deliver Mesosphere Containers And Stateful DC/OS 765 5.26.6 Mesosphere DC/OS: Mesos 765 5.26.7 Heart of DC/OS Mesos 768 5.26.8 DC/OS Implements Containers 768 5.26.9 DC/OS Available Services as Packages Included in Universe 770 5.26.10 Mesosphere Customers 770 5.27 Microsoft 771 5.27.1 Microsoft Builds the Intelligent Cloud Platform 772 5.27.2 Microsoft Targets Personal Computing 774 5.27.3 Microsoft Reportable Segments 774 5.27.4 Skype and Microsoft 778 5.27.5 Microsoft / Skype / GroupMe Free Group Messaging 779 5.27.6 Microsoft SOA 780 5.27.7 Microsoft .Net Open Source 782 5.27.8 Microsoft Competition 783 5.27.9 Microsoft Revenue 784 5.28 US National Security Agency 784 5.29 NEC 784 5.29.1 NEC Revenue 785 5.29.2 NEC Leading Company In Small Cell Solutions 786 5.29.3 NEC Business Outline 788 5.30 NTT / RagingWire 789 5.30.1 RagingWire Data Centers 791 5.30.2 RagingWire Best Practice Approach to Data Center Colocation 792 5.30.3 RagingWire Joined NTT 793 5.31 OpenStack Cloud Controller 794 5.31.1 OpenStack Has Created Its Own APIs 795 5.31.2 How Many Openstack Clouds Have Been Deployed 797 5.31.3 OpenStack Functions 797 5.31.4 OpenStack Regional Market 798 5.31.5 OpenStack Collaboration with Industry 798 5.31.6 OpenStack Cloud Service Context 800 5.31.7 OpenStack Industry Presence 800 5.31.8 Storage Innovations Drive OpenStack 802 5.32 Puppet 804 5.32.1 Puppet: Standard platform. 804 5.32.2 Puppet Customers 805 5.32.3 Puppet Open Source Software 807 5.32.4 Puppet Orchestrator 807 5.32.5 Puppet Project Blueshift 808 5.33 QTS 808 5.33.1 QTS History 809 5.33.2 QTS Chicago Data Center 810 5.34 Qualcom 811 5.34.1 Qualcomm 812 5.34.2 Qualcomm Business 812 5.34.3 QMC Offers Comprehensive Chipset Solutions 813 5.34.4 Qualcomm Government Technologies 814 5.34.5 Qualcomm Internet Services 815 5.34.6 Qualcomm Ventures 816 5.34.7 Qualcomm Revenue 818 5.34.8 Qualcomm / WiPower 819 5.35 Rackspace 820 5.36 Red Hat / Ansible 821 5.37 Switch 822 5.37.1 Switch SUPERNAP CORE Cooperative: $3 Trillion Independent Purchasing And Collaboration Ecosystem 823 5.37.2 Switch SUPERNAP Edge 823 5.38 Tango 824 5.39 Tencent 825 5.39.1 TenCent Revenue 826 5.39.2 Tencent Revenues 828 5.39.3 Tencent Holdings Has a Partnership With Glu 828 5.39.4 Tencent WeChat 829 5.40 Twitter 830 5.40.1 Twitter Revenue 830 5.40.2 Twitter Creation And Sharing Ideas And Information 830 5.40.3 Bringing Tweets To People 831 5.17 Yahoo 831 5.17.1 Yahoo Revenue 832 5.17.2 Yahoo Mavens Revenue 833 5.17.3 Yahoo Tumblr 835 5.17.4 Yahoo Tumblr Sponsored Posts 836 5.17.5 Yahoo Tumblr Sponsored Day 836 5.17.6 Yahoo Tumblr Use Case 836 5.17.7 Yahoo Display Revenue 837 5.17.8 Yahoo Display Metrics 837 5.17.9 Yahoo / Microsoft 838 5.17.10 Yahoo / Google 838 5.17.11 Yahoo / Tumblr 839 WINTERGREEN RESEARCH, 841 WinterGreen Research Research Methodology 842
List of Figures Figure 1. Cloud 2.0 Mega Data Center Market Driving Forces 56 Figure 2. Cloud Datacenter, Co-Location, and Social Media Revenue Market Shares, Dollars, Worldwide, 2016, Image 59 Figure 3. Cloud 2.0 Mega Datacenter Market Forecast, Dollars, Worldwide, 2017-2023 61 Figure 4. RagingWire Colocation N+1 Shared Infrastructure 65 Figure 5. RagingWire Colocation N+1 Dedicated Infrastructure 67 Figure 6. RagingWire Data Center Maintenance on N+1 Dedicated System Reduces Fault Tolerance to N 69 Figure 7. RagingWire Data Center Stays Fault Tolerant During Maintenance with 2N+2 System 70 Figure 8. Global Digital Information Created and Shared 2005-2015 73 Figure 9. 100 Gbps Adoption 74 Figure 10. Data Center Technology Shifting 76 Figure 11. Data Center Technology Shift 77 Figure 12. IT Cloud Evolution 82 Figure 13. Facebook Networking Infrastructure Fabric 84 Figure 14. Datacenter Metrics 86 Figure 15. Cloud 2.0 Mega Data Center Market Driving Forces 103 Figure 16. Cloud Datacenter, Co-Location, and Social Media Revenue Market Shares, Dollars, Worldwide, 2016, Image 106 Figure 17. Cloud Datacenter, Co-Location, and Social Media Revenue Market Shares, Dollars, Worldwide, 2016 107 Figure 18. Cloud 2.0 Mega Datacenter Cap Ex Spending Market Shares Dollars, Worldwide, 2016 108 Figure 19. Large Internet Company Cap Ex Market Shares, Dollars, Worldwide, 2013 to 2016 110 Figure 20. Cloud 2.0 Mega Data Center Cap Ex Market Shares, Dollars, Worldwide, 2013 to 2016 111 Figure 21. Cloud 2.0 Mega Data Center Cap Ex Market Shares, Dollars, Worldwide, 2016 112 Figure 22. Cloud 2.0 Mega Data Center Social Media and Search Revenue Market Shares, Dollars, 2016, Image 114 Figure 23. Cloud 2.0 Mega Data Center Social Media and Search Revenue Market Shares, Dollars, 2016 115 Figure 24. Big Eight Hyperscaler Cloud Providers 116 Figure 25. Supernap, Las Vegas, 407,000 sf 117 Figure 26. DuPONT FABROS CH1, ELK GROVE VILLAGE, Ill. 485,000 SF 118 Figure 27. 538,000SF: i/o Data Centers and Microsoft Phoenix One, Phoenix, Ariz. 119 Figure 28. Phoenix, Arizona i/o Data Center Design Innovations 120 Figure 29. Next Generation Data Europe, Wales 750,000 SF 121 Figure 30. NAP Of The Americas, Miami, 750,000 SF 122 Figure 31. QTS Metro Data Center, Atlanta, 990,000 SF 123 Figure 32. 350 East Cermak, Chicago, 1.1 Million Square Feet 124 Figure 33. Data Center Multiple-Facility Campuses Feature Half Million SF 126 Figure 34. Microsoft Data Center, Dublin, 550,000 Sf 128 Figure 35. Container Area In The Microsoft Data Center In Chicago 129 Figure 36. An aerial view of the Microsoft data center in Quincy, Washington 131 Figure 37. . Microsoft San Antonio Data Centers, 470,000 SF 132 Figure 38. Microsoft 3rd Data Center in Bexar Could Employ 150 133 Figure 39. Cloud 2.0 Mega Datacenter Market Forecast, Dollars, Worldwide, 2017-2023 144 Figure 40. Cloud 2.0 Mega Datacenter Market Shares Dollars, Forecast, Worldwide, 2017-2023, 145 Figure 41. Cloud 2.0 Mega Datacenter Market Shares Percent, Forecast, Worldwide, 2017-2023 146 Figure 42. Hyperscale Data Centers 147 Figure 43. Market Driving Forces for Cloud 2.0 Mega Data Centers 148 Figure 44. Market Challenges of Cloud 2.0 Mega Data Centers 149 Figure 45. Data Center Size Definition 163 Figure 46. Data Center Density Definitions 164 Figure 47. Carrier-Neutral Colocation Vendors 170 Figure 48. DuPont Fabros Technology 450,000 square feet, ACC7 in Ashburn, VA Large Data Center 173 Figure 49. Ten Largest Data Centers 175 Figure 50. Cloud 2.0 mega data center Open Compute Project OCP ASIC Switch Vendors 183 Figure 51. Global Data Center Rack Market Key Vendors 184 Figure 52. Global Data Center Rack Market Company Participants 185 Figure 53. Key Components And Topology Of A Mega Datacenter 188 Figure 54. Datacenter Topology without Single Managed Entities 189 Figure 55. Key Challenges Enterprise IT Datacenters: 190 Figure 56. Software Defined Datacenter 192 Figure 57. Cloud Automation Vendors 193 Figure 58. Cisco VNI Forecast Overview 200 Figure 59. The Cisco VNI Forecast—Historical Internet Context 201 Figure 60. Global Devices and Connections Growth 202 Figure 61. Average Number of Devices and Connections per Capita 203 Figure 62. Global IP Traffic by Devices 204 Figure 63. Global Internet Traffic by Device Type 205 Figure 64. Global 4K Video Traffic 206 Figure 65. Global IPv6-Capable Devices and Connections Forecast 2015-2020 207 Figure 66. Projected Global Fixed and Mobile IPv6 Traffic Forecast 2015-2020 208 Figure 67. Global M2M Connection Growth 209 Figure 68. Global M2M Connection Growth by Industries 210 Figure 69. Global M2M Traffic Growth: Exabytes per Month 211 Figure 70. Global Residential Services Adoption and Growth 212 Figure 71. Global IP Traffic by Application Category 213 Figure 72. Mobile Video Growing Fastest; Online Video and Digital TV Grow Similarly 214 Figure 73. Global Cord Cutting Generates Double the Traffic 214 Figure 74. Fixed Broadband Speeds (in Mbps), 2015-2020 216 Figure 75. Future of Wi-Fi as Wired Complement 217 Figure 76. Global IP Traffic, Wired and Wireless* 218 Figure 77. Global Internet Traffic, Wired and Wireless 219 Figure 78. Cisco VNI Forecasts 194 EB per Month of IP Traffic by 2020 221 Figure 79. Cisco Forecast of Global Devices and Connections Growth 222 Figure 80. Hardware Cost Comparison - Mainframe vs. Distributed 231 Figure 81. Server and Mainframe Hardware Costs 232 Figure 82. 2016 Compute Cost Metrics 233 Figure 83. 2016 Compute Hardware Cost Metrics 235 Figure 84. Server Transactions Per Watt From Greenway Collaborative 236 Figure 85. Server to MIPS Conversion Calculations 237 Figure 86. Working Out The Hardware Cost Differential Between Mainframe And Distributed Systems 238 Figure 87. Server And Mainframe Hardware Costs 239 Figure 88. Benefits of Cloud Computing 241 Figure 89. Intel Xeon Processor, 36 Cores, x2Thread Count = 72, Spin 5000 virtual Machines 243 Figure 90. Cloud Services Market Shares, Dollars, 2016 244 Figure 91. Cloud Services Market Shares Dollars, Worldwide, 2016 246 Figure 92. Cloud Services Market Shares Percent, Worldwide, 2016 247 Figure 93. Cloud Services, Companies with Measurable Market Shares, Dollars and Percent, Worldwide, 2016 248 Figure 94. Cloud 2.0 Mega Data Center Regional Market Segments, Dollars, 2016, Image 249 Figure 95. Cloud 2.0 Mega Data Center Regional Market Segments, Dollars, 2016 250 Figure 96. Data Center Supply in Selected US Regions 252 Figure 97. Chicago Data Center Market 254 Figure 98. Digital Realty Trust Lakeside Technology Center 255 Figure 99. Digital Realty Trust Lakeside Technology Center Hallway Gothic architecture 257 Figure 100. Digital Realty Trust Lakeside Technology Center Industrial-Strength Power And Fiber Infrastructure 258 Figure 101. Map of Google’s Cloud Data Centers 262 Figure 102. Amazon Zones and Regions 266 Figure 103. Amazon AWS Global Cloud Infrastructure 269 Figure 104. Amazon (AWS) Support for Global IT Presence 271 Figure 105. AWS E Tool Functions 273 Figure 106. AWS E Tool Supported Sources 274 Figure 107. Amazon North America Map 275 Figure 108. Amazon North America List of Locations 276 Figure 109. Example of AWS Region 281 Figure 110. Example of AWS Availability Zone 282 Figure 111. Example of AWS Data Center 283 Figure 112. AWS Network Latency and Variability 284 Figure 113. Amazon (AWS) Regional Data Center 285 Figure 114. A Map of Amazon Web Service Global Infrastructure 286 Figure 115. Rows of Servers Inside an Amazon (AWS) Data Center 287 Figure 116. Facebook DuPont Fabros Technology Ashburn, VA Data Center 289 Figure 117. Facebook Altoona Iowa Cloud 2.0 Mega Data Center 292 Figure 118. Facebook Cloud 2.0 mega data center in Altoona, Iowa Construction Criteria 293 Figure 119. Facebook Fifth Data Center Fort Worth Complex. 296 Figure 120. Facebook Altoona Positioning Of Global Infrastructure 298 Figure 121. Facebook Back-End Service Tiers And Applications Account for Machine-To-Machine Traffic Growth 301 Figure 122. Facebook Back-End Service Tiers And Applications Functions 302 Figure 123. Facebook Cluster-Focused Architecture Limitations 304 Figure 124. Facebook Clusters Fail to Solve a Networking Limitations 306 Figure 125. Facebook Sample Pod: Unit of Network 309 Figure 126. Facebook Data Center Fabric Network Topology 311 Figure 127. Facebook Network Technology 313 Figure 128. Facebook Schematic Fabric-Optimized Datacenter Physical Topology 316 Figure 129. Facebook Automation of Cloud 2.0 mega data center Process 319 Figure 130. Facebook Creating a Modular Cloud 2.0 mega data center Solution 320 Figure 131. Facebook Cloud 2.0 mega data center Fabric High-Level Settings Components 321 Figure 132. Facebook Cloud 2.0 mega data center Fabric Unattended Mode 322 Figure 133. Facebook Data Center Auto Discovery Functions 323 Figure 134. Facebook Automated Process Rapid Deployment Architecture 326 Figure 135. Facebook Fabric Automated Process Rapid Deployment Architecture 327 Figure 136. Facebook Fabric Rapid Deployment 328 Figure 137. Facebook Cloud 2.0 mega data center High Speed Network Implementation Aspects 329 Figure 138. Facebook Cloud 2.0 mega data center High Speed Network Implementation Aspects 330 Figure 139. Google St. Ghislain, Belgium, Europe Data Center 332 Figure 140. Google Dynamic Architecture 334 Figure 141. Google Clos Multistage Switching Network 339 Figure 142. Google Key Principles Used In Designing Datacenter Networks 340 Figure 143. Google Andromeda Cloud Architecture Throughput Benefits 342 Figure 144. Google Andromeda Software Defined Networking (SDN)-Based Substrate Functions 344 Figure 145. Google Andromeda Cloud High-Level Architecture 344 Figure 146. Google Andromeda Performance Factors Of The Underlying Network 346 Figure 147. Google Compute Engine Load Balanced Requests Architecture 348 Figure 148. Google Compute Engine Load Balancing 349 Figure 149. Google Cloud Platform TCP Andromeda Throughput Advantages 351 Figure 150. Google Meta Data Center Locations 353 Figure 151. Google Meta Data Center Locations Map 354 Figure 152. Google Dalles Data Center Cooling Pipes 356 Figure 153. Google Hamina, Finland Data Center 357 Figure 154. Google Lenoir Data Center North Carolina, US 358 Figure 155. Google Data Center in Pryor, Oklahoma 360 Figure 156. Google Douglas County, Georgia Data Center Facility 361 Figure 157. Google Berkeley County, South Carolina, Data Center 363 Figure 158. Google Council Bluffs Iowa Cloud 2.0 Mega Data Center 366 Figure 159. Google Council Bluffs Iowa Cloud 2.0 Mega Data Center Campus Network Room 367 Figure 160. Google Douglas County Cloud 2.0 Mega Data Center 368 Figure 161. Google Team of Technical Experts Develop And Lead Execution Of’ Global Data Center Sustainability Strategy 369 Figure 162. Google Datacenter Manager Responsibilities 370 Figure 163. Google Meta Data Center 372 Figure 164. Google Server Warehouse in Former Paper Mill 373 Figure 165. Google Data Center in Hamina, Finland 375 Figure 166. Google Traffic Generated by Data Center Servers 376 Figure 167. Google Cloud 2.0 mega data center Multipathing: Implementing Lots And Lots Of Paths Between Each Source And Destination 378 Figure 168. Google Cloud 2.0 mega data center Multipathing: Routing Destinations 379 Figure 169. Google Builds Own Network Switches And Software 379 Figure 170. Google Clos Topology Network Capacity Scalability 380 Figure 171. Google Jupiter Network Delivers 1.3 Pb/Sec Of Aggregate Bisection Bandwidth Across A Datacenter 382 Figure 172. Jupiter Superblock Collection of Jupiter Switches Running SDN Stack Based On Openflow Protocol: 383 Figure 173. Google Modernized Switch, Server, Storage And Network Speeds 384 Figure 174. Google Container Controller Positioning 388 Figure 175. Google Data Center Efficiency Measurements 392 Figure 176. Google Data Center PUE Measurement Boundaries 394 Figure 177. Google Continuous PUE Improvement with Quarterly Variatiion, 2008 to 2017 395 Figure 178. Cumulative Corporate Renewable Energy Purchasing in the United States, Europe, and Mexico, November 2016 397 Figure 179. Images for Microsoft Dublin Cloud 2.0 Mega Data Center 409 Figure 180. Microsoft Azure Data Center 410 Figure 181. Microsoft Dublin Cloud 2.0 mega data center 412 Figure 182. Microsoft .Net Dynamic Definition of Reusable Modules 413 Figure 183. Microsoft .NET Compiling Source Code into Managed Assemblies 415 Figure 184. Microsoft Architecture Dynamic Modular Processing 416 Figure 185. Microsoft-Azure-Stack-Block-Diagram 431 Figure 186. Microsoft-Azure-Platform Stack-Services 433 Figure 187. Figure 175. Microsoft-Cloud Virtual Machine -Platform Stack-Services 435 Figure 188. Microsoft-Azure-Core Management-Services 435 Figure 189. Microsoft Data Centers 441 Figure 190. QTS Multi Tenant Data Center Outsourcing Benefits 453 Figure 191. IBM Cloud Managed Services for z Systems 455 Figure 192. IBM Cloud Managed Services for z Systems Functions 456 Figure 193. IBM Cloud Managed Services Features 458 Figure 194. IBM Cloud Managed Services on z Systems Benefits 459 Figure 195. Linux-Based Solutions Under IBM z/VM® Shared Infrastructure Support Hybrid Workloads 461 Figure 196. IBM® Cloud Managed Services® on z Systems Features 463 Figure 197. IBM® Cloud Managed Services® on z Systems Functions 464 Figure 198. IBM and CA Security Management Products For Mainframes 465 Figure 199. IBM Mainframe Regulatory Compliance Support 466 Figure 200. IBM Cloud Managed Services on z Systems 467 Figure 201. IBM Cloud Managed Services on z Systems—Linux 468 Figure 202. IBM i American Airlines Cloud Partnership Functions 473 Figure 203. IBM Model z13 Computer Features 478 Figure 204. Pipeline Has An Instruction Queue 479 Figure 205. IBM Model z13 Computer Configuration 480 Figure 206. DuPont Fabros ACC Data Center Technology 483 Figure 207. Data Center Market MW Availability 485 Figure 208. Wholesale Data Center MW Availability Definition 486 Figure 209. Wholesale Data Center Consolidated MW Trends 487 Figure 210. Wholesale Data Center MW Commissioned Growth 488 Figure 211. Retail Data Center Colocation Churn is Increassing 489 Figure 212. DuPont Fabros Technology Portfolio Competition 490 Figure 213. Data Center Key Metrics 491 Figure 214. Hewlett Packard Composable Data Center Infrastructure 494 Figure 215. Hewlett Packard Project Synergy Composable Infrastructure Initiative 496 Figure 216. NTT RagingWire Data Centers Image 499 Figure 217. NTT Mission Critical IT Systems Features 500 Figure 218. NTT RagingWire Highly Customizable Colocation Solutions Features 501 Figure 219. NTT Ragingwire Data Centers Facilities Location, Power, and Cooling Features 502 Figure 220. NTT Ragingwire Data Centers Facilities Security Functions 503 Figure 221. RagingWire Wholesale Data Center Campuses: 504 Figure 222. NTT RagingWire Ashburn Va2 Data Center 506 Figure 223. Rackspace Hosting Provider Functions 508 Figure 224. Equinix LD6 data center in Slough, England 510 Figure 225. IBX Data Center Locations 511 Figure 226. Equinix Dublin Metro Data Centers 513 Figure 227. Equinox Dublin Metro Data Center 516 Figure 228. Equinox Dublin Data Center Server Racks 517 Figure 229. Equinix Asia Pacific Data Centers 521 Figure 230. Equinix IBX Data Center Features 522 Figure 231. Equinix IBX Data Center Functions: 523 Figure 232. Equinix Connections and Interconnections 524 Figure 233. Equinix Data Center Image 525 Figure 234. Equinix Data Center Features 526 Figure 235. eBay Cloud 2.0 mega data center 530 Figure 236. Switch SuperNAP Synoptek Advanced Data Center 532 Figure 237. Switch Synoptek Data Center Advantages 533 Figure 238. Switch Synoptek Hosting Facilities Advantages 533 Figure 239. SuperNAP SSAE16 Type II Certified Facility Features 534 Figure 240. Switch Synoptek Power Advantages 535 Figure 241. Switch SuperNap Facilities Aspects: 536 Figure 242. Switch SUPERNAP Data Center High Density Racks 541 Figure 243. Switch SUPERNAP High Density Data Center 543 Figure 244. Red hat Ansible Playbook Language Advanced Features 546 Figure 245. Cisco UCS Director Delivers Comprehensive Infrastructure Management and Orchestration 551 Figure 246. Multiple Pathways Open To Processing Nodes In The Cloud 2.0 Mega Data Center Functions 553 Figure 247. Layer 3 MPLS VPN Backbone 559 Figure 248. OSPF Network Types 560 Figure 249. Cloud 2.0 Mega Data Centers Are Demanding Significant Amounts Of Power And Network Management 567 Figure 250. Reducing Power With Micro Server SoCs 568 Figure 251. Software Stack, Standard Platforms, Simplified Network Architecture, Reduces Network Management Costs 569 Figure 252. Data center SoC architecture 570 Figure 253. Simplifying the Data Center Network with SDN 572 Figure 254. Simplifying the Data Center Network 573 Figure 255. Data Center Network SDN Functions 573 Figure 256. Synopsys DesignWare IP Portfolio Features 576 Figure 257. Figure 168. Synopsys DesignWare IP Portfolio Modules 577 Figure 258. Data Center SoC Architecture Incorporating Synopsys DesignWare IP Attributes 578 Figure 259. Automatic Detection And Recovery From Network And System Failure 580 Figure 260. High Performance And Real-Time Message Throughput 584 Figure 261. Fault Tolerance Features 585 Figure 262. Functions Of An IP Addressing Device 587 Figure 263. Benefits Of an IP Addressing Device 588 Figure 264. Dynamic Visual Representation System Uses 589 Figure 265. Application Integration Health Care Functions 590 Figure 266. Application Integration Industry Functions 591 Figure 267. CERNE Cloud Architecture 595 Figure 268. Cern Cloud and Dev 596 Figure 269. CERN Use Cases 597 Figure 270. Cern Hardware Spectrum 598 Figure 271. Open Stack at Cern 600 Figure 272. Cern Open Space Containeers on Clouds 600 Figure 273. 365 Data Centers Products & Services 605 Figure 274. Amazon Principal Competitive Factors In The Online Retail Business 607 Figure 275. Amazon Improving Customer Experience Functions 609 Figure 276. Amazon Ways To Achieve Efficiency In Technology For Operations 611 Figure 277. Alibaba Applications Integration Automated Cloud Processes 635 Figure 278. Baidu Search and Information 639 Figure 279. Baidu Range Of Energy Saving Methods And Technologies 645 Figure 280. Key Benefits of AWS OpsWorks for Chef Automate 648 Figure 281. Chef Automate Builds On Widely Adopted Open-Source Projects: 650 Figure 282. Chef Automate Solution for Automating the Technology Stack 651 Figure 283. Chef Automate Block Diagram 655 Figure 284. Chef Professional DevOps Practice Service Partners 656 Figure 285. Technology Partners Build World Class Integrations With Chef To Accelerate And Compliment Their Platforms With Automation 657 Figure 286. Chef Value Added Resellers (VARs) 658 Figure 287. Chef Open Source And Commercial Automation Platforms 659 Figure 288. Digital Realty Trust Metropolitan Area Percentage of September 30, 2016 Total Annualized Rent 670 Figure 289. Docker Challenges and Solutions 672 Figure 290. DuPont Fabros Wholesale Data Center Characteristics 675 Figure 291. DFT Development Plan 676 Figure 292. DuPont Fabros Triple-Net Leases 677 Figure 293. DuPont Fabros Technology Data Center Locations 678 Figure 294. DuPont Fabros Key Strategic Initiatives 679 Figure 295. DuPont Fabros Base Rent Trends 680 Figure 296. DuPont Fabros Key Operating Metrics - Leasing / Renewals 681 Figure 297. DuPont Fabros Occupancy Trends 682 Figure 298. DuPont Fabros Technology Operating Portfolio: Tier 1 Markets 683 Figure 299. DuPont Fabros Annual Revenue 684 Figure 300. EdgeConneX Data Centers Positioned at the Edge 689 Figure 301. EdgeConneX Edge Data Centers North America and Europe 694 Figure 302. Equinix Global Regional Segment Revenue, Three Months 2016 700 Figure 303. Equinix Global Co Location Data Centers 701 Figure 304. Google / Nest Learning Thermostat 725 Figure 305. IBM PureSystems Target Industries 734 Figure 306. Mesosphere Target Applications 761 Figure 307. Cloud-Native Building Blocks Change Delivery Of Apps 762 Figure 308. Mesosphere DC/OS: Mesos Features: 766 Figure 309. Microsoft Productivity and Business Processes Segment 775 Figure 310. Microsoft Intelligent Cloud Segment 776 Figure 311. Microsoft / Skype / GroupMe Free Group Messaging 779 Figure 312. Microsoft Service Orientated Architecture SOA Functions 781 Figure 313. Ragingwire Wholesale Data Center Campuses: 791 Figure 314. OpenStack Cloud at CERN 799 Figure 315. Open Stack Deployments 803 Figure 316. QTS Chicago Data Center 810 Figure 317. QTS Chicago Data Center Inside Raised Floor 811 Figure 318. Rackspace London 820 Figure 319. Rackspace Global Infrastructure 821 Figure 320. Red Hat Ansible Tower 3 Job Run Metrics 822
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