Veeam Software, the leader in Backup solutions that deliver Cloud Data Management, announced it has been positioned by Gartner, Inc. in the Leaders quadrant of the 2020 Magic Quadrant for Data Center Backup and Recovery Solutions1. Not only does this mark the fourth time Gartner has recognized Veeam as a category Leader, but it is the first time Veeam is positioned highest overall in ability to execute. Veeam was also the only vendor to move higher in both ability to execute and in the completeness of vision categories. We believe this recognition further validates Veeam’s investment in delivering a complete Cloud Data Management portfolio to its customers and partners, fused with robust support that ensures data protection across physical, virtual and cloud environments.

“To us, being named a Leader by Gartner for the fourth time cements our commitment to lead the industry with innovation, execution, and delivering the most simple, flexible and reliable solutions for Cloud Data Management,” said Danny Allan, CTO and Senior Vice President of Product Strategy at Veeam. “With more than 375,000 customers and $1B in annual bookings, we continue to guide our customers through their digital transformation and Hybrid/Multi-cloud journeys at a time when data protection is paramount and leveraging data reuse for overall business value is critical.”

Veeam released Veeam Availability Suite (VAS) v10 earlier this year, delivering modern file data protection for Networked Attached Storage (NAS), Multi-VM Instant Recovery™ to automate disaster recovery (DR), and heightened ransomware protection. With greater platform extensibility, data mining through APIs, and more than 150 major enhancements, Veeam has launched the industry’s most robust solution for complete data management and protection for hybrid-cloud environments.

The report included analysis of 11 data center backup and recovery solutions vendors. We think now is a time when the move toward public cloud, heightened concerns over ransomware, and complexities associated with backup and data management are forcing I&O leaders to rearchitect their backup infrastructure and explore alternative solutions.

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San Francisco: The increasing use of artificial intelligence is going to extract a heavy price in power unless the chip industry steps up and heads that off, according to one of the industry’s biggest companies.

Data centers are on course to consume 15% of the world’s electricity by 2025, according to Applied Materials Inc., the world’s largest maker of chip equipment. Those giant warehouses of computers currently suck in about 2%, the company said.

“AI has the potential to change everything,” said Applied Materials Chief Executive Officer Gary Dickerson speaking in a prerecorded remote keynote for the industry’s Semicon West conference. “But AI has an Achilles’ heel that — unless addressed — will prevent it from reaching its true potential. That Achilles heel is power consumption. Training neural networks is incredibly energy-intensive when done with the technology that’s available today.”

A flood of new devices are getting internet connections, generating more data and increasing the need for more computing power with artificial intelligence to make sense of that new information. Chipmakers, most of which use Applied’s machinery, have made their electronic components more energy efficient but not enough, according to Dickerson. The industry needs to come up with new custom designs tailored for AI processing and new ways of connecting those chips, he said.

Widths on the tiny circuits that give chips their functions are measured in billionths of a meter. But to move and store data quickly they require large amounts of power. An Intel Corp. Xeon processor may draw more than 200 watts, as much as an old-fashioned portable tube TV. Put thousands of those processors in close proximity and couple them with all of the other components needed to make a server and the electricity drain adds up.

Applied’s CEO made a promise to reduce the company’s power footprint and to innovate on the basics of manufacturing and materials to help his customers make more efficient components. One example he gave Tuesday involves a technique for growing tungsten atoms in a vacuum to make more efficient connections between parts of the chip.

Applied will move to 100% renewable energy and cut its carbon footprint by 50% over the next 10 years, Dickerson pledged. Even relatively simple changes in practices can help, he said. Flying an engineer from the U.S. to Asia and back generates about 2 metric tons of carbon dioxide. Applied has learned during the Covid-19 pandemic how to improve remote research and development and customer support, he said, making fewer such trips necessary.

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Large-scale software services fight the efficiency battle on two fronts—efficient software that is flexible to changing consumer demands, and efficient hardware that can keep these massive services running quickly even in the face of diminishing returns from CPUs. Together, these factors determine both the quality of the user experience and the performance, cost, and energy efficiency of modern data centers.

A change on one front requires adjustments on the other, and a new software architecture growing in popularity has posed a challenge to the hardware solutions current in most data centers. Called microservices, this modular approach to designing big enterprise software has left something to be desired in its interactions with another major rising force in datacenter efficiency, hardware accelerators.

To bring these two promising technologies together more effectively, CSE Ph.D. student Akshitha Sriraman, working with researchers from Facebook, has designed a way to measure exactly how much a hardware accelerator would speed up a datacenter. Appropriately named Accelerometer, the analytical model can be applied in the early stages of an accelerator’s design to predict its effectiveness before ever being installed.

Still a somewhat new technology in general computing usage, the effectiveness of hardware accelerators isn’t as easy to predict as CPUs, which have decades of experience behind them. Investing in this sort of diverse custom hardware presents a risk at scale, since it might not live up to its expectations.

But the potential for a big impact is there. Designed to perform one type of function extremely quickly, accelerators could theoretically be called upon for all the redundant, repetitive tasks used in common by bigger applications.

That includes microservices. This software architecture approach conceives of a larger application as a collection of modular, task-specific services that can each be improved upon in isolation. This allows for changes to be made to the larger application without needing to change one huge, central codebase. It also allows for more services to be added more easily.

Sriraman demonstrated that as few as 18% of most microservices’ CPU cycles are spent executing instructions that are core to their functionality. The remaining 82% are spent on common operations that are ripe for accelerating.

“Accelerating these overheads we identified can indeed improve speedup to a significant extent,” Sriraman says. Beyond speed, it would make all of the datacenter’s functions cheaper and more energy efficient. “Acceleration will allow us to pack more work for the same power constraints and improve resource utilization at scale, so data center energy and cost savings will improve greatly.”

The issue with microservices is that their designs can turn out to be quite dissimilar, particularly with regard to how they interact with hardware. For example, a microservice can communicate with an accelerator while continuing to run other instructions on a CPU, or it could bring all of its functions to a halt while it offloads to the accelerator. Both of these cases face different “offload overheads” (the time spent sending a task from one processor to another), which becomes lost time for the datacenter if it’s not accounted for.

“Each of these software design choices can result in different overheads that affect the overall speedup from acceleration,” says Sriraman. This overhead is left out of the picture in prior work, she continues, as is the impact of the different microservice designs on performance.

Additionally, accelerators themselves have to be used judiciously to have a net positive effect.

“Throwing an accelerator at every problem is ridiculous because it takes a lot of time, cost, and effort to build, test, and deploy each one,” she concludes. “There is a real need to precisely understand what and how to accelerate.”

Accelerometer is an analytical model that measures exactly how much performance would be improved by installing a given processor, if at all, with all of these nuances taken into account. That means it measures the positive effect of acceleration as well as the negative effect of spending time shuffling instructions around between computing components. And its capabilities aren’t limited to new accelerators—the model can be applied to any kind of hardware, ranging from a simple CPU optimization to an extremely specialized remote ASIC.

The tool was validated in Facebook’s production environment using three retrospective case studies, demonstrating that its real speedup estimates have less than 3.7% error.

The model is sufficiently accurate to already be put to use by Facebook, with early interest from other companies.

“We have received word that several of the big cloud players have started using Accelerometer to quickly discard bad accelerator choices and identify the good ones, to make well-informed hardware investments,” Sriraman says. Facebook is using the model to explore new accelerators, incorporating it as a first step to quickly sort out good and bad hardware choices.

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Applications are expected to move from data centers to edge facilities in record numbers, opening up a huge new market opportunity. The edge computing market is expected to grow at a compound annual growth rate of 36.3 percent between now and 2022, fueled by rapid adoption of the “internet of things,” autonomous vehicles, high-speed trading, content streaming and multiplayer games.

What these applications have in common is a need for near zero-latency data transfer, usually defined as less than five milliseconds, although even that figure is far too high for many emerging technologies.  

The specific factors driving the need for low latency vary. In IoT applications, sensors and other devices capture enormous quantities of data, the value of which degrades by the millisecond. Autonomous vehicles require information in real-time to navigate effectively and avoid collisions. The best way to support such latency-sensitive applications is to move applications and data as close as possible to the data ingestion point, therefore reducing the overall round-trip time. Financial transactions now occur at sub-millisecond cycle times, leading one brokerage firm to invest more than $100 million to overhaul its stock trading platform in a quest for faster and faster trades.

Operational challenges

As edge computing grows, so do the operational challenges for telecommunications service provider such as Verizon Communications Inc., AT&T Corp. and T-Mobile USA Inc. For one thing, moving to the edge essentially disaggregates the traditional data center. Instead of massive numbers of servers located in a few centralized data centers, the provider edge infrastructure consists of thousands of small sites, most with just a handful of servers. All of those sites require support to ensure peak performance, which strains the resources of the typical information technology group to the breaking point — and sometimes beyond. 

Another complicating factor is network functions moving toward cloud-native applications deployed on virtualized, shared and elastic infrastructure, a trend that has been accelerating in recent years. In a virtualized environment, each physical server hosts dozens of virtual machines and/or containers that are constantly being created and destroyed at rates far faster than humans can effectively manage. Orchestration tools automatically manage the dynamic virtual environment in normal operation, but when it comes to troubleshooting, humans are still in the driver’s seat. 

And it’s a hot seat to be in. Poor performance and service disruptions hurt the service provider’s business, so the organization puts enormous pressure on the IT staff to resolve problems quickly and effectively. The information needed to identify root causes is usually there. In fact, navigating the sheer volume of telemetry data from hardware and software components is one of the challenges facing network operators today. 

Machine learning and automation 

A data-rich, highly dynamic, dispersed infrastructure is the perfect environment for artificial intelligence, specifically machine learning. The great strength of machine learning is the ability to find meaningful patterns in massive amounts of data that far outstrip the capabilities of network operators. Machine learning-based tools can self-learn from experience, adapt to new information and perform humanlike analyses with superhuman speed and accuracy.  

To realize the full power of machine learning, insights must be translated into action — a significant challenge in the dynamic, disaggregated world of edge computing. That’s where automation comes in.

Using the information gained by machine learning and real-time monitoring, automated tools can provision, instantiate and configure physical and virtual network functions far faster and more accurately than a human operator. The combination of machine learning and automation saves considerable staff time, which can be redirected to more strategic initiatives that create additional operational efficiencies and speed release cycles, ultimately driving additional revenue. 

Scaling cloud-native applications

Until recently, the software development process for a typical telco consisted of a lengthy sequence of discrete stages that moved from department to department and took months or even years to complete. Cloud-native development has largely made obsolete this so-called “waterfall” methodology in favor of a high-velocity, integrated approach based on leading-edge technologies such as microservices, containers, agile development, continuous integration/continuous deployment and DevOps. As a result, telecom providers roll out services at unheard-of velocities, often multiple releases per week. 

The move to the edge poses challenges for scaling cloud-native applications. When the environment consists of a few centralized data centers, human operators can manually determine the optimum configuration needed to ensure the proper performance for the virtual network functions or VNFs that make up the application.

However, as the environment disaggregates into thousands of small sites, each with slightly different operational characteristics, machine learning is required. Unsupervised learning algorithms can run all the individual components through a pre-production cycle to evaluate how they will behave in a production site. Operations staff can use this approach to develop a high level of confidence that the VNF being tested is going to come up in the desired operational state at the edge. 

Troubleshooting at the speed of AI 

AI and automation can also add significant value in troubleshooting within cloud-native environments. Take the case of a service provider running 10 instances of a voice call processing application as a cloud-native application at an edge location. A remote operator notices that one VNF is performing significantly below the other nine.  

The first question is, “Do we really have a problem?” Some variation in performance between application instances is not unusual, so answering the question requires a determination of the normal range of VNF performance values in actual operation. A human operator could take readings of a large number of instances of the VNF over a specified time period and then calculate the acceptable key performance indicator values — a time-consuming and error-prone process that must repeated frequently to account for software upgrades, component replacements, traffic pattern variations and other parameters that affect performance.

In contrast, AI can determine KPIs in a fraction of the time and adjust the KPI values as needed when parameters change, all with no outside intervention. Once AI determines the KPI values, automation takes over. An automated tool can continuously monitor performance, compare the actual value to the AI-determined KPI and identify underperforming VNFs.

That information can then be forwarded to the orchestrator for remedial action such as spinning up a new VNF or moving the VNF to a new physical server. The combination of AI and automation helps ensure compliance with service-level agreements and removes the need for human intervention — a welcome change for operators weary of late-night troubleshooting sessions. 

Harnessing the competitive edge

As service providers accelerate their adoption of edge-oriented architectures, IT groups must find new ways to optimize network operations, troubleshoot underperforming VNFs and ensure SLA compliance at scale. Artificial intelligence technologies such as machine learning, combined with automation, can help them do that.

In particular, there have been a number of advancements over the last few years to enable this AI-driven future. They include systems and devices to provide high-fidelity, high-frequency telemetry that can be analyzed, highly scalable message buses such as Kafka and Redis that can capture and process that telemetry, and compute capacity and AI frameworks such as TensorFlow and PyTorch to create models from the raw telemetry streams. Taken together, they can determine in real time if operations of production systems are in conformance with standards and find problems when there are disruptions in operations.

All that has the potential to streamline operations and give service providers a competitive edge — at the edge.

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When it comes to advancing the field of artificial intelligence, the ultimate prize is still clear. The goal is to come as close as possible to the power of the human brain.

For researchers at the forefront of AI development, such as Naveen Rao (pictured), vice president and general manager of the artificial intelligence products group at Intel Corp., achieving near parity with a human’s cognitive ability remains a long way off.

“Back in 2013, there were 10 million or 20 million parameters, which was very large for a machine-learning model,” Rao said. “Now they’re in the billions. The human brain is 300 trillion to 500 trillion models, so we’re still pretty far away from that; we’ve got a long way to go.”

Rao spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, and guest host Justin Warren, chief analyst at PivotNine Pty Ltd., during Amazon Web Services Inc.’s re:Invent conference in Las Vegas. They discussed the role of Intel’s processor technology in machine learning, intelligence for cloud and edge computing, the impact of recent neural network training tools, AI for good and the future of autonomous cars. (* Disclosure below.)

This week, theCUBE features Naveen Rao as its Guest of the Week.

Research advances AI

While the processing gap remains sizable, Rao and Intel are working on several projects to move AI forward. It is work not only fundamental to the field, but also integral to Intel’s own business strategy and long-term future.

Intel’s PC chip business still accounts for approximately half of its total revenue, but the second-largest segment revolves around the data center where AI is having the greatest impact. The company has been adjusting its powerful Xeon central processing unit chips to handle complex machine-learning tasks, most recently adding DL Boost to facilitate neural net performance.

As developers and data scientists iterate large data sets to generate a series of outcomes, the inference of how results are rolled out and deployed becomes more significant.

“Inference is all about the best performance per watt,” Rao explained. “How much processing can I shove into a particular time and power budget? On the training side, it’s much more about what kind of flexibility I have for exploring different types of models and training them very fast.”

Acquisitions bolster portfolio
One indicator of how seriously Intel is taking its role as a provider of AI processing in the data center can be found in its acquisition of Israel-based Habana Labs Ltd. for $2 billion in December. Habana Labs’ Goya AI Inference Processor is currently used by Facebook Inc. for its own machine-learning compiler.

Intel’s acquisition of Habana Labs followed other moves the company has made in the AI space since 2016 when it purchased Nervana Inc., where Rao served as co-founder and chief executive officer. In 2018, Intel bought Vertex.Ai and its platform-agnostic AI model technology and last year open-sourced its deep neural network framework, nGraph.

In November, Intel introduced its Nervana Neural Network Processors for training and inference, designed to accelerate AI system deployment from cloud to edge.

“From its very inception, the machine was really meant to be something that recapitulated intelligence,” Rao said. “Everything we do is impacted by AI and will be in service of building better AI platforms for intelligence at the edge, intelligence in the cloud, and everything in between.”

Training neural networks
Building better AI platforms will require training deep neural networks to run more powerfully in data centers today. But for these networks to become truly effective, they must be able to generalize to understand a range of possibilities while improving overall intelligence. Welcome to the world of the 16-bit brain floating point and generative adversarial networks, or GANs.

Intel will soon begin leveraging the floating point, or “bfloat16,” instruction for its Cooper Lake Xeon processors and Nervana-based training models. AI researchers have found that bfloat16 worked well across workloads and can be used for vision, speech and language applications, which explains why Intel is moving boldly down that path.

The instruction is also useful in helping move key learning networks forward, such as GANs. Using generative adversarial networks has been called “the most interesting idea in the last 10 years in machine learning” by Yann LeCun, director of AI research at Facebook.

“You can think of it as two competing sides of solving a problem,” Rao explained. “If you have two neural networks that are working against each other, one is generating stuff and the other one is asking if it’s fake or not. Eventually, you keep improving each other.”

Deepfakes remain a concern
Discussion of AI continues to center around the positive and negative. While GANs may indeed be instrumental in moving machine intelligence forward, they are also a key ingredient in creating “deepfakes,” which is raising alarm in some sectors of the tech community.

Deepfake technology has been used to create deceptive videos and nonconsensual pornography and even to disseminate fictitious news reports. One Forrester Research Inc. analyst has estimated that deepfake scams will exceed $250 million for the coming year.

Despite those concerns, Rao believes that the good will overcome the bad.

“One radiologist plus AI equals 100 radiologists,” Rao said. “It solves problems that we have in healthcare today; that’s where we should be going with this. I look at AI as a way to push humanity to the next level.”

In an AI-driven world, what will humanity at the next level look like? The continued march toward autonomous driving has the potential to impact human lives in a major way.

The fastest-growing business for Intel on an annualized basis is Mobileye Technologies Ltd., a company it acquired for $15 billion two years ago. Mobileye makes autonomous vehicle technology and is focused on the robotaxi market.

Autonomous vehicles provide yet another example of how Intel is moving from a PC-centered company to a data-driven business, and self-driving cars will be an inevitable outcome of AI progress, according to Rao.

“Autonomous driving is a bit of a black box, and the number of situations one can incur on the road are almost limitless,” Rao said. “For a 16-year-old, we say go out and drive. And, eventually, they sort of learn it. The same thing is happening now for autonomous systems.”

Driving cars is an area that Rao knows quite well. When he’s not guiding Intel’s AI products group, the technologist races semi-professionally on the Ferrari automotive circuit.

And while he envisions a world where autonomous cars will become a normal part of daily life, Rao doesn’t see human-driven cars disappearing completely.

“Five to seven years from now, we will be using autonomy much more on prescribed routes,” Rao said. “It won’t be that it completely replaces a human driver even in that time frame because it’s a very hard problem to solve. It’s going to be a gentle evolution over the next 20 to 30 years.”

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A huge change is happening in the back offices of companies over the globe. Many name it as the ascent of the robots, however, an increasingly fitting term is robotic process automation (RPA). While the association between robotics technology and RPA might be to some degree inexactly characterized, the simple fact is RPA is only an extravagant abbreviation for a software robot, or “bot” in IT vernacular. RPA delivers innovation that joins scripting with intelligence and execution. It is a mix of automated prowess that has been a genuine boon for back-office tasks.

However, for all its ability to drive profitability and diminish physical work, RPA has been met with dread. That dread of job losses and staff decreases, has an exceptionally human component, leading to unwarranted presumptions about robots. Furthermore, nowhere is that fear more noteworthy than in the data center, where highly compensated experts feel undermined by the ascent of the bots, with doubts that RPA will diminish their significance to overall activities and put data center administrators out in the city.

Organizations structure the customer base of most of the data centers, regardless of whether that is a small regional data center, a bustling colocation or the universally distributed network of huge data centers that underlie people in public cloud providers.

As organizations wake up to the power and effectiveness of the cloud, they’re setting up DevOps teams and microservices which demand real-time processing, elastic scalability, big data storage capacity and 99.99% or more reliability. To satisfy the new needs required by these business models while keeping costs aggressive, data centers need to lessen overheads while improving reliability and performance

As framework turns out to be increasingly complex and distributed, there’s an extra contention for robotic assistance. People are essentially incapable to screen and process the numerous floods of data coming into a data center without making mistakes or lessening pace and performance. Network downtime is serious enough, however, with data ruptures presently pulling in record fines, mix-ups can compromise the very presence of a data center.

As we enter this new revolution in how organizations work, it’s important that each bit of data is dealt with and utilized appropriately to improve its value. Without cost-effective storage and progressively amazing hardware, digital transformation and the new business models related to it wouldn’t be possible.

Specialists have been foreseeing for quite a while that the automation advances that are applied in processing plants worldwide would be applied to data centres later on. In all actuality, we’re quickly advancing this probability with the use of Robotic Process Automation (RPA) and machine learning in the datacenter environment.

Human mistake is by a wide margin the most critical reason for network downtime. This is trailed by hardware failures and breakdowns. With practically zero oversight of how hardware is functioning, move must be made once the downtime has just happened. The cost effect is a lot higher as the focus is detracted from different things to deal with the reason for the issue, joined with the effect of the actual network downtime. Dependability, cost and management must be fixed to give an increasingly productive data center. Automation can help accomplish this.

With the fear of bots suppressed, numerous CIOs and data center managers are considering how to best embrace RPA and where to apply the innovation. Apparently, data center tasks today are tied in with accomplishing more with less and are on the leading edge of changing wetware into processes that carry extra value to business operations. All things considered, it turns out to be evident that RPA, particularly as intelligent automation, can carry phenomenal efficiencies to operations. A valid example is data center management, where bots can be made to perform backups, spool up virtual machines on demand, move bots from near online to online frameworks, resolve issues. etc. Everything comes down to the degree of creative mind present and the capacity to distinguish tasks that lend themselves well to automation.

However, RPA is substantially more than macros or contents. RPA presents a level of intelligence that enables bots to decide, which lets them go about as an intelligent automation agent. For instance, bots can be deployed to screen network traffic and trained to make a move dependent on a threshold being accomplished. In addition, the bots can utilize pattern recognition alongside analytics to characterize the thresholds in real-time, enabling them to respond a lot quicker than any human can.

Such abilities look good for data centers that should be flexible and are under consistent security threats. Bots can be worked to recognize usage patterns, standardized traffic, CPU cycles, etc as a reason for scaling up or down. Activities that once spurred a technician into action would now be able to be mechanized. Intelligent automation has additionally demonstrated to be a decent line of barrier against malware, ransomware, and information spillage. With bots checking activity, normalized patterns of usage can be deducted and anticipated behaviors of uses, clients, and different components can be measured. When activity falls out of norms, bots can make a move utilizing either foreordained guidelines or significantly increasingly inventive responses driven by AI.

Another RPA advantage is the normalization of procedures and strategies. By evacuating the variable activities of people from a procedure, data centers can anticipate a more significant level of standardization with considerably more unsurprising results. That in itself is a help for companies driven by compliance regulations, where the upcoming strategies is a critical aspect of meeting compliance.

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Leading software company Zoho Corporation has recently launched a fullstack serverless application called Catalyst which is specifically aimed at developers. Zoho claims that the software is an easy to use yet powerful applicaiton that allows developers to build and run microservices and applications.

Catalyst evidently grants developers access to the same frameworks and services that power more than 45 Zoho applications used by around 45 million users globally.

Apparently, Catalyst has already allowed developers to build applications and services which include a CRM user import, bug-filing bridge, a lead distribution for microservice, and a data-cleaning microservice.

Sources with relevant information stated that Zoho has developed and vertically integrated all the layers of its technological stacks, ranging from applications to infrastructures to operating systems to data centers eventually.

The decade long technological investment which has resulted into a safe, robust and scalable infrastructure is now accessible to the developers.

Zoho’s Chief Evangelist Raju Vegesna was reported saying that the company’s years of experience along with its vision to provide end-to-end solutions has empowered them to create comprehensive pro-code, low-code and no-code tools for a range of business applications.

Reg Horman, Director of IT and Systems for The Pexion Group stated that Catalyst’s latest innovation allows users to quickly develop, test, deploy and support applications without having to worry about global standards, infrastructure, and support.

Catalyst is one of the core serverless strategies of the Pexion Group which provides the company with the flexibility to digitize manufacturing processes, Horman added

For the record, The Pexion Group is a privately owned, world-class precision engineering company with fully accredited production facilities based around the UK. Like other Zoho applications, Catalyst will seemingly be priced competitively and will be available on Zoho’s website.

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Intel revealed its first chipsets designed for artificial intelligence (AI) in large data centers. The Intel Nervana NNP-T and Nervana NNP-I processors are dedicated AI accelerators built to support increasingly complex computing workloads and techniques derived from deep learning models and inference.

The Intel Nervana NNP-I chip, or Spring Hill, is built on Intel’s 10 nanometer (nm) Ice Lake processor technology, which provides large data centers with high performance computing at a lower energy cost, according to the chipmaker. Spring Hill will also foster deep learning inference and deployment at scale across major data center workloads.

The processor offers increased programmability and includes a dedicated inference accelerator with short latencies, fast code porting, and support for all major deep learning frameworks, according to Intel. The company claims Spring Hill leads in performance and power efficiency for major data center inference workloads.

Intel announced the product earlier this year alongside Facebook, one of the chipmaker’s development partners and initial large enterprises using the AI chip. Intel began developing AI chips in earnest after it acquired Nervana Systems in 2016. The company says the next two generations of the chip are already under development.

Deep Learning at Scale

The Intel Nervana NNP-T chip, or Spring Crest, is also built on Intel’s 10nm Ice Lake processor. The chip is designed to train deep learning models at scale, and by that the chipmaker means it will train networks quickly and do so with minimal impact on energy consumption.

Spring Crest is also built to be flexible and balance an enterprise’s needs for computing, communication, and memory. Intel claims the chip can be programmed to accelerate many existing and yet-to-emerge workloads in data centers.

Research conducted by OpenAI in May 2018 concluded that the amount of compute used in the largest AI training runs have doubled every 3.5 months since 2012. Intel aims to address this exponential increase by focusing on the four primary factors that drive a deep learning training accelerator: power, compute, memory and communication, and scale out (or hardware capacity expansion).

“To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense, and making smarter use of their upstream resources,” said Naveen Rao, VP and GM of Intel’s artificial intelligence group, in a prepared statement. “Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications.”

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Network traffic across the internet, within data centers and in end-user networks has increased rapidly over time and shows no signs of stopping. While 100 Gigabit Ethernet, or GbE, technology has only been available for a few years, a variety of applications already show a need for even higher data rates.

Industry experts expect large public cloud providers to be the initial adopters of 400 GbE switches. Yet, as links in cloud server racks upgrade to 50 or 100 Gb, IT teams must upgrade their network backbones to carry increasing amounts of traffic.

Large public cloud providers maintain geographically dispersed clouds to support worldwide enterprises and provide backup and failover. Traffic levels between clouds increase along with traffic levels within each cloud, so providers must also upgrade inter-cloud links.

Where will users see the effects of 400 GbE technology?

In addition to cloud environments, the internet also requires 400 GbE links. Traffic continues to grow rapidly due to multiple factors, including the following:

• Users stream popular TV networks, such as HBO and Netflix, over the internet.
• Viewers access a multitude of YouTube videos with individual end-user streams, which adds to internet traffic.
• Phone calls previously made over cell networks increasingly moved to the internet — e.g., WhatsApp, Skype and Wi-Fi Calling on iPhones and Androids.
• 5G greatly increases cell network data rates, and much of this traffic will travel over the internet for some portion of its route.
• Online shopping continues to increase, with many vendors providing video to showcase products.

All of these applications contribute to the need for transport networks that carry internet traffic to move toward higher data rates. Transport providers anticipated this growth and began trials as soon as early 400 GbE equipment was available. Now that these trials are complete, experts anticipate providers will now upgrade wide area links, as well.

Additional applications that require higher bandwidth communication generally involve efficient moving and processing of large data amounts, such as files used in advanced data mining activities and AI, including machine learning.

IEEE developments with 400 GbE

It takes time to develop technology that supports higher throughputs. Work on the Institute of Electrical and Electronics Engineers (IEEE) 802.3bs standard for 400 GbE began in 2013, and the IEEE completed the work in late 2017. The IEEE devoted its time between the standard’s release and product availability to interoperability testing, which ensures all components function and integrate as intended. With its work on 400 GbE complete, the IEEE began to develop the increased signaling rates needed to support even higher data rates.

400 GbE links are relatively new, and many prospective customers are still in the evaluation stage, but many networks will soon adopt 400 GbE to address support for increased traffic.

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We have an almost mystical faith in the ability of artificial intelligence (AI) to understand and solve problems. It’s being applied across many areas of our daily lives and, as a result, the hardware to enable this is starting to populate our data centers.

Data centers in themselves present an array of complex problems, including optimization and prediction. So, how about using this miracle technology to improve our facilities?

Machine learning, and especially deep learning, can examine a large set of data, and find patterns within it that do not depend on the model that humans would use to understand and predict that data. It can also predict patterns that will repeat in the future.

Data centers are already well-instrumented, with sensors that provide a lot of real-time and historical data on IT performance and environmental factors. In 2016, Google hit the headlines when it applied AI to that data, in order to improve efficiency.

Google used DeepMind, the AI technology it owns, to optimize the cooling in its data centers. In 2014, the company announced that data center engineer Jim Gao was using the AI tech to implement a recommendation engine.

In 2016, the project optimized cooling at Google’s Singapore facility, using a set of neural networks which learned how to predict future temperatures and provide suggestions to respond proactively,

The results shaved 40 percent off the site’s cooling bill, and 15 percent off its PUE (power utilization effectiveness), according to Richard Evans, a research engineer at DeepMind. In 2016, he promised: “Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data center environment and beyond.”

The next step, announced in 2018, was to move closer to a self-driving data center cooling system, where the AI tweaks the data center’s operational settings – under human supervision. To make sure the system operated safely, the team constrained its operation, so the automatic system “only” saves 30 percent on the cooling bill.

The system takes a snapshot of the data center cooling system with thousands of sensors every five minutes, and feeds it into an AI system in the cloud. This predicts how potential actions will affect future energy consumption and picks the best option. This is sent to the data center, verified by the local control system, and then implemented.

The project team reported that the system had started to produce optimizations that were unexpected. Dan Fuenffinger, one of Google’s data center operators who has worked extensively alongside the system, remarked: “It was amazing to see the AI learn to take advantage of winter conditions and produce colder than normal water, which reduces the energy required for cooling within the data center. Rules don’t get better over time, but AI does.”

According to Gao, the big win here was proving that the system operates safely, as well as efficiently. Decisions are vetted against safety rules, and human operators can take over at any time.

At this stage, Google’s AI optimization has one customer: Google itself. But the idea has strong backing from academia.

Stability matters

Humans, and simple rule-based systems can respond to any steady-state situation, but when the environment changes, they react in a “choppy” way – and AI can do better, because it is able to predict changes, according to DCD keynote speaker Suvojit Ghosh, who heads up the Computing Infrastructure Research Centre (CIRC) at Ontario’s McMaster University.

“We know it’s bad to run servers too hot.” said Ghosh. ”But it’s apparently even worse if you have temperature fluctuations.” Simple rules take the data center quickly to the best steady state position, but in the process, they make sudden step changes in temperature, and it turns out that this wastes a lot of energy. If the conditions change often, then these energy losses can cancel out the gains.

“If you have an environment that goes from 70°F to 80°F (21-27°C) and back down, that really hurts,” said Ghosh.

Companies in data center services are responding. Data center infrastructure management (DCIM) firms have added intelligence, and those already doing predictive analytics have added machine learning.

“The current machine learning aspects are at the initial data processing stage of the platform where raw data from sensors and meters is normalized, cleaned, validated and labeled prior to being fed into the predictive modeling engine,” said Zahl Limbuwala, co-founder of Romonet, an analytics company now owned by real estate firm CBRE.

The move for intelligence in power and cooling goes by different names. In China, Huawei’s bid to make power, cooling and DCIM smarter goes under the codenames iPower, iCooling and iManager.

Like Google and others, Huawei is starting with simple practical steps, like using pattern matching to control temperature and spot evidence of refrigerant leaks. In power systems, it’s working to identify and isolate faults using AI.

In its Langfang data center, with 1,540 racks, Huawei has reduced PUE substantially using iCooling, according to senior marketing manager Zou Xiaoteng. The facility operates at around 6kW per rack with a 43 percent IT load rate.

DCIM vendor Nlyte nailed its colors firmly to the DCIM mast in 2018, when it signed up to integrate its tools with one of the world’s highest profile AI projects, IBM’s Watson.

Launching the partnership at DCD>New York that year, Nlyte CEO Doug Sabella predicted that AI-enhanced DCIM would lead to great things: “The simple things are around preventive maintenance,” he told DCD. “But moving beyond predictive things, you’re really getting into workloads, and managing workloads. Think about it in terms of application performance management: today, you select where you’re going to place a workload based on a finite set of data. Do I put it in the public cloud, or in my private cloud? What are the attributes that help determine the location and infrastructure?

“There’s a whole set of critical information that’s not included in that determination, but from an AI standpoint, you can contribute into it to actually reduce your workloads and optimize your workloads and lower the risk of workload failure. There’s a whole set of AI play here that we see and our partner sees, that we’re working with on this, that is going to have a big impact.”

Amy Benett, North American marketing lead for IBM Watson IoT, saw another practical side: “Behold, a new member of the data center team, one that never takes a vacation or your lunch from the breakroom.”

DCD understands the partnership continues. The Watson brand has been somewhat tarnished by reports that it is not delivering as promised in more demanding areas such as healthcare. It’s possible that this early brand leader has been oversold, but if so, data centers could be an arena to restore its good name. The vital system of a data center is much more simple than the human body.

The next stage

It’s time for AI to reach for bigger problems, says Ghosh, echoing Sabella’s point. After the initial hiccups, efforts to improve power and cooling efficiency will eventually reach a point of diminishing returns. At that point, AI can start moving the IT loads themselves:

“Using the cost of compute history to do intelligent load balancing or container orchestration, you can bring down the energy cost of a particular application,” Ghosh told his DCD audience. This could potentially save half the IT energy cost, “just by reshuffling the jobs [with AI] – and this does not take into account turning idle servers off or anything crazy like that.”

Beyond that, Ghosh is working on AI analysis of the sounds in a data center. “Experienced people can tell you something is wrong, because it sounds funny,” he said. CIRC has been creating sound profiles of data centers, and relating them to power consumption.

Huawei is doing this too: “If there is a problem in a transformer, the pattern of noise changes,” said Zou Xiaoteng. “By learning the noise pattern of the transformer, we can use the acoustic technology to monitor the status of the transformer.”

This sort of approach allows AI to extend beyond expert human knowledge and pick up “things that human cognition can never understand,” said Ghosh.

“In the next 10 years, we will be able to predict failures before they happen,” said Ghosh. “One of my dreams is to create an algorithm that will completely eliminate the need for preventative maintenance.”

Huawei’s Xiaoteng reckons there are less-tangible benefits too: AI can improve resource utilization by around 20 percent, he told DCD, while reducing human error.

Xiaoteng sees AI climbing a ladder from level zero, the completely manual data center. “On level one the basic function is to visualize the contents of the data center with sensors,and on level two, we have some assistance, and partially unattended operation,” where the data center will report conditions to the engineer, who will respond appropriately.

At level three, the data center begins to offer its own root cause analysis and virtual help to solve problems, he said. Huawei has reached this stage, he said: “In the future, I believe we can use AI to predict if there’s any problem and use the AI to self-recover the data center.”

At this stage, DCIM systems may even benefit from specialized AI processors, he predicted. Huawei is already experimenting with using its Ascend series AI processors to work in partnership with its DCIM on both cloud and edge sides.

Right now, most users are still at the early stages compared with these ideas, but some clearly share this optimism: “Today we use [AI] for monitoring set points,” said Eric Fussenegger, a mission critical facility site manager at Wells Fargo, speaking at DCD>New York in 2019, adding to DCIM and “enhancing the single pane of glass.”

AI could get physical, further in the future, said Fussenegger, in a fascinating aside. “The ink is not even dry yet, maybe it hasn’t even hit the paper.” he said, but intelligent devices could play a role in the day-to-day physical maintenance and operation of a data center.

One day, robots could take over “cleaning or racking equipment for us, so I don’t have to worry about personnel being in hot and cold aisle areas. There are grocery stores that are using AI to sweep.”

Even these extreme views are tempered, however. Said Fussenegger: “I think we’re always going to need humans in there as a backup.”

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