Business growth depends on technology spending. Intelligent, automated process, not manual labor systems are what speed business growth. We have had the situation in the data center where 93% of spending is just to keep current systems running, many of those plagued with manual input. Mega data centers change that pattern of IT manual process. The Internet has grown by a factor of 100 over the past 10 years. To accommodate that growth, mega data centers have evolved to provide processing at scale. Facebook for one, has increased the corporate data center compute capacity by a factor of 1,000, virtually eliminating much manual process. Orchestration software is a key aspect of that process. To meet future demands on the Internet over the next 10 years, companies with that capacity need to increase capacity by the same amount again while the other companies struggle to catch up. Nobody really knows how to get to increasing compute capacity by another factor of 1,000. Business growth depends on technology spending. Intelligent, automated process, not manual labor systems are what speed business growth. We have had the situation in the data center where 93% of spending is just to keep current systems running, many of those plagued with manual input. Mega data centers change that pattern of IT manual process. Realigning data center cost structures is a core job of orchestration software. The enterprise data centers and many cloud infrastructure operations all have similar problems of being mired in administrative expense. Containers address that issue by creating vastly more efficient operations for data center infrastructure. According to Susan Eustis, lead author of the team that prepared the study, “The only way to realign cost structure is to automate infrastructure management and orchestration. Mega data centers automate server and connectivity management using orchestration software to manage multiple application containers. Other systems automate 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 Center has become a bottleneck, it needs to be completely replaced. Category 5 and Category 6 Ethernet cable is spread throughout the existing enterprise data centers and is too slow to handle all the digital data coming through the data center. Cat 5 and Cat 6 Ethernet utilized by the servers to achieve data transport using that cable does not keep up with the data coming through the data center the way optical cable and optical transceivers do. The existing servers and cable are a problem because they are too slow for modern systems. The cable is too slow to handle all the data coming at us in the new digital age, and the associated technology that operates at Ethernet category 5 and category 6 cable speeds is too slow as well, this is why the entire set of existing enterprise data centers is a bottleneck. Mobile data traffic is set to increase by a factor of eight between 2015 and 2020. Growth is anticipated at 53 percent per year, faster than systems revenue or industry revenue. The theme of this study is that the pace of data expansion creates the need for more modern means of managing data. There are some companies that are doing a better job, better than others of adapting to IT infrastructure to the wild influx of data. The four superstar companies that are able to leverage IT to achieve growth, Microsoft, Google, Facebook, and the leader AWS all use Clos architecture. What is significant is that systems have to hit a certain scale before Clos networks work Clos networks are what work now for flexibility and supporting innovation in an affordable manner. There is no dipping your toe in to try the system to see if it will work, it will not and then the IT says, “We tried that, we failed,” but what the executive needs to understand is that scale matters. A little mega data center does not exist. Only scale works. Many companies are using digital technology to create market disruption. Amazon, Uber, Google, IBM, and Microsoft represent companies using effective strategic positioning that protects the security of the data. As entire industries shift to the digital world, once buoyant companies are threatened with disappearing. A digital transformation represents an approach that enables organizations to drive changes in their business models and ecosystems leveraging cloud computing, and not just hyperscale systems but leveraging mega data centers. Just as robots make work more automated, so also cloud based communications systems implement the IoT digital connectivity transformation. 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 • Amazon • Microsoft • Google • Facebook Key Topics • Orchestration Software In The Mega Data Center • Realign IT Cost Structure • Mega Datacenter Physical Infrastructure • Automation of Mega Data Center • Networking Fabric • Exchange Of Data Between Servers • Complex Automation Of Process • Applications Customized For Each User • Machine-To-Machine Management of Traffic Growth • Fabric Network Topology • Building-Wide Connectivity • Highly Modular Data Cebter Design • Scale Capacity • Back-End Service Tiers • Applications Scaling • Mega Data Center Network • Fabric Next-Generation Data Center Network Design • Pod Unit of Network • Mega Data Center Server Pods • Non-Blocking Network Architecture • Data Center Auto Discovery • Large-Scale Network • Rapid Deployment Architecture • Expedites Provisioning And Changes • Programmable Access To Network Stack • Software Defined Networking (SDN)-Supports Scale and Automation • Compute Engine Load Balancing • Load Balanced Requests Architecture • Scale-Out: Server And Storage Expansion • Switches and Routers Deployed in Fabrics • Mega Data Center Multipathing • Routing Destinations • Clos Topology Network • Capacity Scalability • Aggregation Switches • Intelligent Cloud Platform • Linux For Azure
Table of Contents Sea Change Series: Orchestration Software in the Mega Data Center Sea Change Series: Orchestration Software in the Mega Data Center, Amazon, Google, Microsoft, Facebook 2 Aim to Realign IT Cost Structure 3 Internet Has Grown by a Factor of 100 Over The Past 10 Years 4 Table of Contents 5 Orchestration Software Automates Data Center Infrastructure 14 Software Containers 15 Orchestration Schedulers Manage Containers 16 Orchestration Software Supports Container Automation 17 Realigning Data Center Cost Structures 18 IT Relies On Replacing Virtual Machine: VM Virtualization 19 Microservices 20 Microservices Features 21 Microservices Modules 23 Difficulties with Virtual Machines 24 Hypervisor a Difficulty 25 Virtual Machines Use Bare Metal, Containers Use Orchestration Software 27 Bare Metal an Inefficient Use of Compute Resource 28 Bare Metal Less Efficient 28 Industry Uses Robots Because Manual Labor Is Slow And Error Prone 29 IT processes Replace Manual Labor 30 Mega Data Center Orchestration Software 31 Large Fabric Network Not The Kind Of Environment That Can Be Realistically Configured And Operated In A Manual Way 32 To Automate the Data Center Fabric 33 Value of Data Center Fabric 33 Google Shift from Bare Metal To Container Controllers 34 Google Container Controller Shift From Bare Metal In A Mixed Workload And In Nested Compute Units 35 Google Kubernetes Groups Software Containers 36 Fabric Services Inside A Container 37 Architecting Microsoft Cloud 38 Microsoft Managed Clustering and Container Management: Docker and Mesos 39 Microsoft Azure Service Fabric 41 Microsoft Is Seen As The Overall Winner In The Move To Application Containers 42 Microsoft Dublin Cloud 2.0 Mega Data Center 43 Microsoft Data Center, Dublin, 550,000 Square Feet 43 Microsoft Dublin Center Operates at a Power Usage Effectiveness (PUE) of 1.25. 44 Microsoft Data Center Container Area in Chicago. 44 Microsoft 45 Advantages of Using Containers 46 Orchestration Software Used to Create Containers 47 Disadvantages of Using Containers 48 Advantages of Virtual Machines 49 Container Orchestration 50 Use of Containers Eliminate Manual Process 51 IT Pros Increasingly Turn to Chef and Puppet 52 Hardware Containers Do Not Scale 53 Facebook Data Center Positioning 54 IBM Data Center Orchestration Software Automates Application Integration 55 Docker Orchestration & Docker Swarm 56 Docker Container Platform 57 Common Feature Sets For Orchestration Tools 58 Not all Orchestrators Are Created Equal 59 AWS Cloud Container Adoption Criteria 60 AWS Cloud Adoption Methodology 63 AWS Cloud Adoption Framework 64 AWS Market Leader In Cloud Computing 65 Facebook Fabric and Node are Core Structures Leveraging Software Orchestration 66 Apache Mesos Orchestration Software 67 Google Kubernetes Container 68 Google Container Builder Step Toward Building Pluggable Components in a Pipeline 69 Google Programmable Access To Network Stack 70 Google Andromeda Software Defined Networking (SDN)- 71 Google Compute Engine Load Balancing 72 Google Compute Engine Load Balanced Requests Architecture 73 Google Scaling Of The Compute Engine Load Balancing 74 Google Compute Engine (GCE) TCP Stream Performance Improvements 75 Google Cloud Platform TCP Andromeda Throughput Advantages 76 Google Open Sourced Its Container Management System Called Kubernetes 77 Facebook 79 Ability To Move Fast And Support Rapid Growth At The Core Of Facebook Infrastructure Design Philosophy 80 The Right Type of Cloud: Mega Data Centers 82 AWS Has Been Able To Adapt To Change 83 Manual Labor Is Slow And Error Prone 84 Mega Data Center Orchestration Software 86 Amazon, Google, Microsoft, Facebook 87 Cloud 2.0 Mega Data Center Fabric Implementation 87 Fabric and Node are Core Structures Leveraging Software Orchestration 89 Multi-Threading, Dynamic Systems 91 Oracle Multi-Threading Mega Data Center 92 Orchestration Tools Manage A Cluster As A Single Deployment 93 Microservice Monitoring with Google Kubernetes 94 Docker Container 94 Cluster Functions and Pod Benefits 95 Mesosphere DC/OS an Open-Source Project Built on Apache Mesos 96 Mesosphere Enterprise DC/OS Orchestration Software 96 Mesosphere DC/OS Production Containers Uses 97 Mesosphere DC/OS Orchestration Software 97 Mesosphere DC/OS Extending Capabilities Within Container Orchestration 98 Mesosphere DC/OS Certification Compliance 98 Mesosphere Market Leadership Position 99 Mesosphere DC/OS Runs Data Services on One Single Platform 99 Cloud Computing Not Enough: Entire Warehouse Building As A Single Mega Data Center System 100 Red Hat Ansible 101 Red Hat Ansible Architecture, Agents, And Security 102 Red Hat Ansible Advanced Features 102 Red Hat / Ansible 103 Red Hat Ansible Tower 3 Job Run Metrics 104 Cisco Integrated Infrastructure Management 105 Cisco UCS Helps Manage Administrative Costs And Reign In Complexity 106 Mesosphere DC/OS: Mesos Features 108 Heart of DC/OS: Apache Mesos 109 DC/OS Implements Containers 110 WinterGreen Research, 111 WinterGreen Research Methodology 112
List of Figures Mega Data Center Depends on Orchestration Software to Control Operations, Implement Microservices Figure 1. Slow Growth Mode of Companies with Enterprise Data Centers 3 Figure 2. Mega Data Center Fabric Implementation 4 Figure 3. Business Innovation and Technology 14 Figure 4. Docker Orchestration Software Creates Containers 15 Figure 5. Docker Compose 16 Figure 6. Mesosphere Marathon 16 Figure 7. Google Kubernetes 16 Figure 8. Orchestration Software Supports Container Automation 17 Figure 9. Orchestration Software Decreases Data Center Cost Structure 18 Figure 10. Files Bundled into a Container 19 Figure 11. Microservices: Suite Of Independently Deployable Service Modules with a Unique Process And Well-Defined, Lightweight Communication Portal: Mechanism To Serve A Business Goal 20 Figure 12. Microservices Distinct Features: Taxi Hailing Example 21 Figure 13. Microservices Market Segments 22 Figure 14. Microservices Modules 23 Figure 15. Hypervisor Virtualization Operating System Interface 24 Figure 16. Hypervisor Virtualization Operating System Interface 25 Figure 17. Virtual Machines Less Efficient Than Containers 26 Figure 18. Difference Between Virtual Machines and Containers 26 Figure 19. Bare Metal Management Replaced by Container Controllers 27 Figure 20. Containers vs. VMs 28 Figure 21. Industrial Robots Eliminate Manual Labor 29 Figure 22. Industry Uses Robots To Replace Manual Labor 29 Figure 23. Data Centers Need The Precision and Automation Similar to that Provided by Multi-Step Sequential Task Industrial Robots 30 Figure 24. Mega Data Center Orchestration Software 31 Figure 25. Single-Fabric Data Center Network Architecture 32 Figure 26. Bare Metal Presents a Lot of Extra Parameters and Metrics, Significantly More than With Containers 34 Figure 27. Nested Compute Units 35 Figure 28. Kubernetes Orchestration Software Groups Containers That Make Up An Application Into Logical Units 36 Figure 29. Kubernetes Orchestration Software Functions 36 Figure 30. Container Features as it integrates with the Service Fabric Runtime 37 Figure 31. Microsoft Setting Up A Secure Service Fabric Cluster in Azure using the Azure Portal. 40 Figure 32. Microsoft Data Center, Dublin, 550,000 Sf 43 Figure 33. Container Area In The Microsoft Data Center In Chicago 44 Figure 34. Microsoft Cloud Network Features 45 Figure 35. Like Physical Containers on a Ship, Software Containers Bring Many Servers Densely Packed 46 Figure 36. Advantages of Using Containers 47 Figure 37. Software Orchestration Container Challenges 48 Figure 38. Manual Process 51 Figure 39. Containers Need Orchestration Software 51 Figure 40. Virtual Machine Data Center Management Tasks: 52 Figure 41. FaceBook Open Compute Project 53 Figure 42. Facebook Data Center Modernization Functions 54 Figure 43. Facebook Altoona Iowa Cloud 2.0 Mega Data Center 54 Figure 44. Manual Process for Application Integration Deployment 55 Figure 45. Feature Sets For Orchestration Tools 58 Figure 46. Issues for Orchestration Software 59 Figure 47. AWS Cloud Container Adoption Criteria 60 Figure 48. AWS Cloud Container 61 Figure 49. AWS Cloud Adoption Framework 62 Figure 50. AWS Market Leader In Cloud Computing 65 Figure 51. Description of the Orchestration Software 67 Figure 52. Advantages of Using the Container Builder Cloud Architecture as a Service: 69 Figure 53. Google Andromeda Cloud High-Level Architecture 70 Figure 54. Google Andromeda Software Defined Networking (SDN)-Based Substrate Functions 71 Figure 55. Google Andromeda Performance Factors Of The Underlying Network72 Figure 56. Google Compute Engine Load Balanced Requests Architecture 73 Figure 57. Google Compute Engine Load Balancing 74 Figure 58. Google Cloud Platform TCP Andromeda Throughput Advantages 76 Figure 59. IoT: Open Source IoT High Level Platform, OpenStack and Kubernetes 78 Figure 60. Facebook DuPont Fabros Technology Ashburn, VA Data Center 81 Figure 61. Cloud 2.0 Mega Data Centers Support 1.5 Billion Facebook Users Worldwide. 82 Figure 62. AWS Market Leader In Cloud Computing 83 Figure 63. Data Centers Need The Precision and Automation Provided by Multi-Step Sequential Task Industrial Robots 85 Figure 64. Mega Data Center Orchestration Software Functions 86 Figure 65. Multiple Pathways Open To Processing Nodes In The Cloud 2.0 Mega Data Center Functions 90 Figure 66. Dynamic Load Balancing 91 Figure 67. Mesosphere Customer References 96 Figure 68. Mesosphere DC/OS Certification Compliance 98 Figure 69. Cloud Is Not Enough 100 Figure 70. Red hat Ansible Playbook Language Advanced Features 103 Figure 71. Red Hat Ansible Tower 3 Job Run Metrics 104 Cisco UCS Helps Manage Administrative Costs And Reign In Complexity 106 Figure 72. Cisco UCS Helps Manage Administrative Costs And Reign In Complexity 106 Figure 73. Cisco UCS Director Delivers Comprehensive Infrastructure Management and Orchestration 107 Figure 74. Mesosphere DC/OS: Mesos Features: 108 Figure 75. Native Mesos Containerizer Functions 110
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