Data Center as A Bottleneck: Market
  • Published Date: 2017 | Pages: 138
  • Single User: 19000 USD | Multi User: 38000 USD

  • Description

                          WinterGreen Research announces that it has published a new module Data Center as a Bottleneck: Market Strategies, Analysis, and Opportunities.  The 2017 module has 138 pages and 67 tables and figures.  This module is part of a study 2,622 pages long, with 1,273 tables and figures that addresses the business issues connected with data center modernization.  There are 20 module parts to the larger study comprised of  detailed analysis of how new infrastructure layers will work to support management of vast quantities of data.  
    
    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.
    
    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.  
    
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                          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
    Table of Contents 13
    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
    New Class of Low-Power Server SoCs 85
    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 FlatPanel
    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 require 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