Mega Data Center Revolution: Market Strategies, Analysis, and Opportunities

  • ID: 4050630
  • Report
  • Region: Global
  • 2622 Pages
  • Wintergreen Research, Inc
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There Is An Enormous Cultural Change Coming As Digital Data Overwhelms the Enterprise

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  • 365 Data Centers
  • China Mobile
  • Docker
  • Google
  • Microsoft
  • Qualcom
  • MORE

This study comprised of a set of modules is a compelling look at the change about to occur in data centers that addresses the business issues connected with data center modernization. There are 20 module parts to the large study comprised of detailed analysis of how new infrastructure layers will work to support management of vast quantities of data.

There is an enormous cultural change coming as digital data overwhelms the enterprise, providing compelling needs to adapt to a digital economy. All products will be marketed with accompanying apps.  All machines come with a digital aspect. This accelerates the production process, minimizes errors and increases transparency of the order processing.

Smart Data has key use cases that involve transforming big data into business value by providing context, increasing efficiency and addressing large, complex problems. These include applications for oil rigs, wind turbines and process control industries. In these industries, the smallest productivity increase translates to huge commercial gains. Intelligent gateways combine the tasks of interfacing to the ERP system and the corresponding communication of automation components.

All mechanical devices adopt an electronic component that produces digital data used to provide trending and monitoring that can generate alerts. The ability to implement a monitor that can detect imminent failure will spawn vast new centers able to react to the alerts generated. Just as the cardiac monitor spawned Intensive care units worldwide, so also IoT software will generate monitoring stations in every industry.

This is the promise of the digital revolution; better management of mechanical process will change every aspect of life. All mechanical devices become digitized and that data requires vast new data center capability, able to scale rapidly, able to operate far more inexpensively than existing data centers. From a business perspective, the executive needs to understand what is coming, how to manage it, and what the competitive opportunities are in every segment: marketing, sales, services, repair.

This study is a group of 20 modules, rolled out at a pace of one a week or so, addressing the data center issues from a business perspective.

According to the 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 of data processing".

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. This study takes a hard look at the alternatives open to business leaders.

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FEATURED COMPANIES

  • 365 Data Centers
  • China Mobile
  • Docker
  • Google
  • Microsoft
  • Qualcom
  • MORE

Mega Data Center: Enterprise Data Center Has Become a Bottleneck

The business benefit of modern mega data centers relates to the ability to increase innovation. The IT spending can be directed toward new projects instead of supporting manual process for infrastructure. With automated process inside the data center at the infrastructure level, more innovation can occur at the applications level. The business leadership needs to search out the mega data center availability and leverage market opportunity brought by Internet of Things automation of process. Managers need to move to intelligent cloud computing. The onset of the data center as a bottleneck occurs because the Ethernet cable in the current enterprise data centers is too slow to manage the increase in digital data that comes with the onset of the digital economy. The current system is outdated and will be replaced by cloud computing that is able to manage the increase in digital data at half the prices and in some cases to provide economies of scale that represent improvements in overall cost.

Hyperscale Data Center: Automated Process Implementation

Cloud computing leverages shared resource. Hyperscale cloud computing embraces the technologies that provide economies of scale. Large data center buildings are put together where compute resource is connected to power and communications providers to achieve a level of cost savings that often achieves 50%, a very noticeable number to a C-level executive tasked with managing a large enterprise organization.

Mega Data Center Layers: Infrastructure and Applications

The importance of the data center in everyday life is growing. The culture is adapting to on-demand, high-quality and real-time access to content via a multitude of applications across a wide range of devices. By creating two-tier architecture and automating the infrastructure layer to virtually eliminate manual process, at that layer, automation is implemented. Then at the application layer, developers can spin virtual servers whenever they need them as part of automatic process, eliminating the need for the three Dell trucks to back up to the warehouse door every week and for leagues of technicians to manually configure the data center.

Co-Location and Wholesale Data Centers

Vendors that are leaders in outsourced data centers are using great technology that illustr4ates the value of making the data center a profit center rather than an expense. VENDORS have specialized expertise and 24x7x365 operations. The customer service is results-focused. World-class data centers and industry-leading SLAs are provided. Highly trained engineers monitor systems around the clock, constantly overseeing the security and uptime of infrastructure. To create a highly resilient environment for business-critical apps and data, Rackspace has built in multiple layers of redundancy. Physical security, power, cooling and networks are each at least N+1 redundant. The redundant design helps deliver reliability and uptime to thousands of businesses every day

Retail Data Centers

Retail cloud providers offer outsourced data centers. They have specialized expertise and 24x7x365 operations. The customer service is results-focused. World-class data centers and industry-leading SLAs are provided. Highly trained engineers monitor systems around the clock, constantly overseeing the security and uptime of infrastructure. To create a highly resilient environment for business-critical apps and data, Rackspace has built in multiple layers of redundancy. Physical security, power, cooling and networks are each at least N+1 redundant. The redundant design helps deliver reliability and uptime to thousands of businesses every day

Mega Data Center Software: Real Time Systems

Mega data center software: real time systems provide business benefit because they permit reacting to customer needs in real time. The ability to make customer service more customized and more responsive pays huge benefits in achieving competitive advantage. Amazon got a commanding head start on the cloud markets because it had built a global retail online sales organization tuned to the needs of thousands of partners offering goods through the Amazon store. The functionality built to support the online store and all the different partner IT presentations, meant that Amazon had a versatile, functioning cloud long before any other market participant did. Amazon AWS global infrastructure is the premier Cloud 2.0 mega data center worldwide, implementing cloud computing available to everyone. The AWS cloud operates in 38 availability zones within 14 geographic regions around the world in 2016. Nine more availability zones and four more regions are coming online throughout the next year 2017.

Mega Data Center Infrastructure Layer

The business benefit of the mega data center infrastructure layer is the ability to replace hardware and machines that break by automatically routing around them. The mega data centers are too complex for any human to understand them in their entirety, so the software orchestration provides a controller that implements automated process. This automation of infrastructure decreases the cot of IT by at least a factor of two and sometimes by a factor of 50, A large fabric network has complex topology built on a couple simple processor ASICs and switch ASICs. A greater number of devices and interconnect is implemented than has been available with a cluster topology. A large fabric network is not an environment that can be configured and operated manually. The uniformity of the topology helps enable better programmability. These are mega data centers. The fabric forwarding plane is independent of tooling. The tooling is independent of the fabric forwarding plane. This allows manual or robotic replacement of specific components without changes to software.

Mega Data Center Application Layer

Data center Linux distros. Facebook has open sourced its FBOSS open switching system. Facebook open sourced its FBOSS open switching system is more a set of applications riding atop a Linux-based open networking operating system. It is much like the application layer in Microsoft’s Azure Cloud Switch stack. Back-end service tiers and applications are distributed and logically interconnected. They rely on extensive real-time cooperation with each other to deliver a fast and seamless experience on the front end. The exchange of data between servers represents a complex automation of process to deliver a range of features to users. Nowhere in this scenario is there a departmental, project based data center system. The mega data center leverages different types of traffic in a stream. One job goes, then the next in the manner similar to the way the mainframe works. This is automated process at its best. The manual process in the data center got implemented as the departments paidto run the departmental applications and each department started running servers at 5% utilization on average.

Mega Data Center Robots

Robotic business benefit in the mega data center relates to lower cost IT and more accurate job performance. The robots go 24 x 7 without making a mistake and they do not take breaks to eat. The list of robotic services includes managed hosting support, remote management, custom applications, helpdesk, messaging, databases, disaster recovery, managed storage, managed virtualization, managed security, managed networks and systems monitoring. Managed hosting services are typically used for managed storage solutions including large drive arrays utilizing backup robots. Mega data centers support robot implementations in every industry. The growing network of connected products means a new set of mechanical engineering skills are needed. Robots are part of smart products. To design smart products, engineers need to learn how to seamlessly integrate digital and mechanical aspects. Users need to learn how to leverage big data to figure out what new designs are most effective. Digital robotic building skills can reap huge benefits for engineers. Closed loop design processes provide added consumer value. The Internet of Things allows engineers to follow the products they design throughout their entire lifecycle, enabling engineers to learn how their designs perform in reality. The mega data center is the technology that permits this end to end tracking.

Mainframe vs. Cloud

The mainframe is still needed in the current data centric cloud computing era because it handles complex financial systems interchanges that are too complex for current cloud computing configurations. The mainframe is optimized for the financial services and airline work, among others, that it does. As data is stored in place in the cloud computing centers and more straight through processing is implemented, the processing has fewer complexities, and the computing configurations may change with time. Companies are implementing initiatives in a one to two-year time frame to migrate all of their applications, except most of those on the mainframe zOS to the public cloud. They want to cut costs. This is especially true among financial firms, which are constrained by the high costs of growth on the mainframe at this point. Application-centric, rather than machine-centric data centers are more adaptive to efficient computing. Business managers can negotiate pay per use cloud contracts, and they can build business initiatives that utilize the paid for capacity. Google Shift from Bare Metal To Container Controllers Google container controller shift from bare metal is a profound one. Containers are a better way of doing server virtualization and driving up server utilization. Everything becomes application-centric rather than machine-centric.

Effect of Scale on Mega Data Center Implementation

Scale is what brings enough pathways inside a data center to create a non-blocking architecture. Non-blocking architecture benefits the business because it permits launching thousands of virtual severs on demand at the application layer developers use without backing the Dell truck up to the data center every week and it provides compute on demand capacity. Modern data centers are organized into processing nodes that manage different applications at a layer above infrastructure. Data is stored permanently and operated on in place. These are the two technologies to check for when choosing a data center. These architectures provide economies of scale that greatly reduce the IT spend while offering better quality IT. If scale is in place, then the economies of scale kick in. When negotiating for cloud capability, managers need to check to see that multiple pathways are available to reach any node in a nonblocking manner. Non-blocking architecture is more efficient that other IT infrastructure and supports better innovation for apps and smart digitization. Not all cloud architectures offer this business benefit. Cloud 2.0 mega data center platform fabric technologies support the digital economy by creating scale with a network internal to the system. Without scale, there are not enough pathways inside the data center to create system integration. Access to every node in the fabric, and multiple duplicate pathways to every node are needed to make application integration work. With sufficient scale, if one pathway is blocked, there are enough other pathways to get to the desired node. To move applications to the cloud efficiently this means adopting containers, period. There is no bare metal at Google. HPC shops or other hyper-scalers or cloud builders think they need to run in bare metal mode but this is not the case. Google has created and released Kubernetes that supports an open system container controller, able to implement application integration efficiently at the application level of the stack instead of the bare metal level.

Mainframe vs. Cloud

The mainframe is still needed in the current data centric cloud computing era because it handles complex financial systems interchanges that are too complex for current cloud computing configurations. The mainframe is optimized for the financial services and airline work, among others, that it does. As data is stored in place in the cloud computing centers and more straight through processing is implemented, the processing has fewer complexities, and the computing configurations may change with time. Companies are implementing initiatives in a one to two-year time frame to migrate all of their applications, except most of those on the mainframe zOS to the public cloud. They want to cut costs. This is especially true among financial firms, which are constrained by the high costs of growth on the mainframe at this point. Application-centric, rather than machine-centric data centers are more adaptive to efficient computing. Business managers can negotiate pay per use cloud contracts, and they can build business initiatives that utilize the paid for capacity. Google Shift from Bare Metal To Container Controllers Google container controller shift from bare metal is a profound one. Containers are a better way of doing server virtualization and driving up server utilization. Everything becomes application-centric rather than machine-centric.

Effect of Scale on Mega Data Center Implementation

Scale is what brings enough pathways inside a data center to create a non-blocking architecture. Non-blocking architecture benefits the business because it permits launching thousands of virtual severs on demand at the application layer developers use without backing the Dell truck up to the data center every week and it provides compute on demand capacity. Modern data centers are organized into processing nodes that manage different applications at a layer above infrastructure. Data is stored permanently and operated on in place. These are the two technologies to check for when choosing a data center. These architectures provide economies of scale that greatly reduce the IT spend while offering better quality IT. If scale is in place, then the economies of scale kick in. When negotiating for cloud capability, managers need to check to see that multiple pathways are available to reach any node in a nonblocking manner. Non-blocking architecture is more efficient that other IT infrastructure and supports better innovation for apps and smart digitization. Not all cloud architectures offer this business benefit. Cloud 2.0 mega data center platform fabric technologies support the digital economy by creating scale with a network internal to the system. Without scale, there are not enough pathways inside the data center to create system integration. Access to every node in the fabric, and multiple duplicate pathways to every node are needed to make application integration work. With sufficient scale, if one pathway is blocked, there are enough other pathways to get to the desired node. To move applications to the cloud efficiently this means adopting containers, period. There is no bare metal at Google. HPC shops or other hyper-scalers or cloud builders think they need to run in bare metal mode but this is not the case. Google has created and released Kubernetes that supports an open system container controller, able to implement application integration efficiently at the application level of the stack instead of the bare metal level.

Data Center Processors

More powerful processor provide business benefit in the cloud as executives can use increased data center capacity to act on a vision of vastly expanding the digital monitoring of process. Extra dada center capacity can provide automatic systems that implement robotic process control. The ability to monitor process gives vision automatically that is not otherwise available and sends alerts upon which action can be taken. Robots significantly decrease labor costs for process or services. Cloud computing is enabled by new, more powerful ASIC processors that enable servers to operate in a multi-threading mode. Microsoft second generation of Open Cloud Server machines are based on Intel “Haswell” Xeon E5 processors. DV2 Azure instances are on Haswell. The servers in design do not have silicon photonics in them. Silicon photonics technologies are in the pipeline. When they become viable they will be launched immediately. The aim is to roll optical networks out to Azure. A pod of compute capacity on the Azure cloud is comprised of 960 nodes of Microsoft open cloud server design housed in 20 racks. This pod has enough capacity to put some in reserve for failovers when they will occur. In the enterprise, companies might have some reserve servers. Simplifying the data center network with SDN is evolving. The move towards SDN brings afundamental change in data center networking. Next generation SDN-enabled communications systems have high-performance, low-latency communications processors and switch chips. IP is implemented as Ethernet, PCI Express and DDR. These support advanced data center protocols, improve reliability-availability-serviceability (RAS), and meet advanced FinFET process technology requirements.

Mega Data Center Storage

SANs provide block level or file level access to data that is shared among computing and personnel resources. Data has become the most valuable corporate asset both tangibly and intangibly. The health of the business depends on the ability to effectively store, access, protect and manage critical data. This is a new challenge facing IT departments. SANs operate behind the servers to provide a common path between processors and storage devices. The business needs to understand the benefits of storing data in place. More analytics can provide monitoring and alerts, departments and partners can share data, creating better business opportunity. Storage Area Networks are part converged applications that traverse data center networks. The benefits are essential to a business. They provide the bandwidth necessary for networked applications using a high performance structured cabling infrastructure to ensure their functionality. Storage architectures depend on the application requirements. Fibre Channel in native form is the predominant architecture for storage, iSCSI and FCoE are gaining some momentum. Using multiple 10GbE links or 40/100GbE speeds support business needs for storage in place.

Mega Data Center Switching and Networking

Business managers need to be able to differentiate between the different types of Cloud computing infrastructure. The hyperscale cloud computing facilities are not by any means a mega data center that operates as a fabric and provides 10 x or 100 x economies of scale for running a data center. Alarge fabric network has complex topology built on a couple simple processor ASICs and switch ASICs. A greater number of devices and interconnect is implemented than has been available with acluster topology. A large fabric network is not an environment that can be configured and operated manually. The uniformity of the topology helps enable better programmability. These are mega data centers. When choosing cloud computing the business manager needs to be able to differentiate between the choices available and to make accurate assessments of what is right for the enterprise organization. Software-based approaches are used to introduce more automation and more modularity to the network.

Optical Transceivers in the Mega Data Center

As we wrote in 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. This is where the optical transceivers come in, helping replace old Ethernet cable for moving data around. The optical systems are a vast improvement in quality.

Data Center Application and Systems Integration

To move applications to the cloud efficiently this means adopting containers, period. There is no bare metal at Google. HPC shops or other hyper-scalers or cloud builders think they need to run in bare metal mode but this is not the case. Google has created and released Kubernetes that supports an open system container controller, able to implement application integration efficiently at the application level of the stack instead of the bare metal level.

Data Center Energy Systems and PUE

The on-going IT transformation is at a faster pace than anticipated. Cloud first is the rule in many companies. It turns out to be harder to actually move to the cloud than to say that IT should move to the cloud. One reason is that there are different kinds of cloud. Cloud 2.0 is a lot more efficient than an ordinary cloud full of manual process There are mandates from the CEOs and CIOs to move to the public cloud as fast as possible. The cloud had been used for development for many years, so companies have experience with the public cloud but until now have been reluctant to move mission critical workload onto the cloud. Now, there is a change, real urgency in achieving real cloud based implementation of application projects. For Q3 2016, Google TTM PUE was 1.12, which has remained constant for the past sixteen quarters. Quarterly PUE was 1.14, equal to Q3 of the previous year. For individual campuses, Google lowest TTM PUE was 1.09, in Belgium. The lowest quarterly PUE was 1.10, in Belgium. The highest TTM PUE was 1.21, in Singapore. The highest quarterly PUE was 1.20, in Taiwan and Singapore.

Mega Data Center Straight Through Processing

Straight-through processing (STP) enables the entire trade process for capital markets. Improving levels of internal STP within a firm while encouraging groups of firms to work together to improve is a trend underway. The quality of the automation of transaction information between them, either bilaterally or as a community of users is a business trend in certain sectors. The emergence of varying levels of straight through processing is anticipated. Business process interoperability is furtherevolving. Straight through processing (STP) is an initiative that financial companies use to optimize the speed at which they process transactions. Straight Through Processing (STP) is a process technology supported by policy that allows an organization to enter the elements of a loan request along with all supporting data (financial statements, tax returns, balance sheets, profit/loss statements, etc) and then re-use the information throughout the entire process.

Mega Data Center IoT: Middleware and SOA Integration APIs

The Cloud 2.0 mega data center is fully automated. The digital economy demands implementation of fully functioning fully automatic, self-healing, networked mega datacenters that operate at fiber optic speeds. The aim is to create a fabric that can access any node because there are multiple pathways to every node. This architecture can scale efficiently; can handle the explosive growth in the quantity of web based data coming with IoT, augmented reality, 10 Gbps speed has been used in data centers for years. The jump to 40 Gbps or past it has come rapidly. Many of the Cloud 2.0 mega data centers have moved to 100 Gbps, presaging the move to 400 Gbps. One reason for the increase in speed is the growth of data consumption, attributed to smartphones, social media, video streaming, Internet of Things (IoT), and big data. Big pipes are used to cope with the huge quantities of data that are being transferred. Users, partners, suppliers and other mega-datacenters all will communicate using digital systems that are automated and self-healing. The effect on the business is compelling, managers have much more responsibility to create maps of strategy and work with IT to see that developers tune the software to fit the current competitive environment. The explosion of data comes from smart phone apps and IoT digital onslaught of streaming data that needs to be processed in real time to look for anomalies, look for change, set alerts, and provide automated response to shifts. Like a cardiac monitor that looks at a stream of heart rhythm looking for trouble, the IoT streams are full of data that is unremarkable, and it is only a change, a shift that needs a response. The tax on the data center is very heavy however, all that analytics requires processing.

Mega Data Center Overview: Recommendations and Challenges

The adoption of server and storage virtualization, the deployment of green IT initiatives to reduce power consumption, and the emergence of software-defined technology is underway.

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FEATURED COMPANIES

  • 365 Data Centers
  • China Mobile
  • Docker
  • Google
  • Microsoft
  • Qualcom
  • MORE

Business is faced with increasing digitization. Smart devices are leveraging apps to provide people more information. In the coming years, competitive advantage will be gained by leveraging the ability to manage data to increase operations efficiency and by using real time analytics to implement more intelligent responses to changing market conditions. The best companies will be agile and flexible, rewarding employees for working off hours to come up with new products or product advances.

With the ability to rapidly understand and react to changes in markets, customer behavior and competitive thrusts get managed. The enterprise must analyze and digest, and in some casesbroadcast, massive amounts of raw data. These needs will put enormous stress on enterprise datacenters (servers and storage), networks, and applications to cope with and comprehend all this data.

In this context, in the face of mountains of more data to contend with, the data center which has been the nucleus of an enterprise organization becomes all pervasive, a potential bottleneck.

  • A massive change is occurring in how IT is managed and delivered to the enterprise
  • Change is occurring in every facet from datacenter infrastructure, to networking, to storage, and data management, and in how applications are structured and perform in multiple environments
  • The enterprise is now faced with an array of choices in how IT will be produced and delivered, including proprietary infrastructure, external infrastructure (i.e. Cloud), As-a-Service and other emerging service models.

Overarching issues remain for the enterprise executive including:

  • How do I develop and differentiate my goods and services to better serve my customers and beat my competition?
  • How do I achieve affordability in both OPEX and CAPEX with my IT spend while improving productivity with my workforce?
  • How do I maintain excellent reliability avoiding costly outages and downtime?
  • And, most importantly, how do I secure my data and IP in an increasingly dangerous environment where even the most secure information of sovereign governments has not been successfully secured from intrusion and hacking?

This study will address each of these issues in the broader context, and further provide a more detailed “drill down” on each of the various subtopics including fully automated datacenters, “straightthrough processing”, data storage and security, etc.

This study is a set of rolling modules that detail answers to all the questions above and provide ongoing input into insight as the markets for data center hardware and software unfold.

There are a few conundrums regarding data center technology that even the experts are not agreed upon as to the proper response and we will address those issues, doing our best to provide useful insight:

  • What about the mainframe? Is there a role for traditional mainframe processing in the new era of massively big data and analytics?
  • What about the mega data centers? Are they different from a cloud data center? In what ways? Does the two layers of automated infrastructure at the lower layers and applications as the upper layer, both inside a building make a difference? How does a cloud data center represents shared resource in a manner that provides competitive advantage?
  • What about cloud computing? Why can I not get my existing enterprise data center migrated to the cloud, just by backing a truck up and taking the servers to the new location?
  • How does systems integration work in the new computing environments?
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  • 365 Data Centers
  • Amazon
  • Apple
  • Alibaba
  • Baidu
  • Chef
  • China Mobile
  • Colocation America
  • Colo-D
  • CoreSIte
  • CyrusOne
  • Digital Realty
  • Docker
  • DuPont Fabros
  • Edge ConneX
  • Equinix
  • Facebook
  • Forsythe
  • Google
  • Hewlett Packard Enterprise
  • IBM
  • Intel
  • I/O
  • InterXion
  • Mesosphere
  • Microsoft
  • NEC
  • NTT / RagingWire
  • OpenStack Cloud
  • Puppet
  • QTS
  • Qualcom
  • Rackspace
  • Red Hat / Ansible
  • Switch
  • Tango
  • Tencent
  • Twitter
  • Yahoo
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