Technology has been one of the best-performing sectors in 2017. The Technology SPDR(NYSEARCA:XLK) has returned 9.1% as compared with S&P 500s gain of 5.2% on a year-to-date basis. (Read More: 5 Hottest Tech ETFs of 2017)

The sector is well poised to flourish driven by improving macro-economic conditions in the U.S and global markets. The rebound in U.S. economy, as evident from an improvement in GDP numbers, the Consumer Confidence Index, unemployment rate and factory activity data presents significant growth opportunity for Technology stocks.

Moreover, sector-specific factors like rapid adoption of cloud computing, growing demand for Artificial Intelligence (AI) applications and expanding usage of Internet of Things (IoT) at home, office and car are key catalysts.

The rapid evolution of hybrid cloud (combination of on-premise servers and public cloud servers) is an important factor behind the adoption of cloud computing among enterprises, primarily due to faster data transfer and heightened privacy. Per Gartner Organizations that adopt hybrid infrastructure will optimize costs and increase efficiency.

Per MarketsAndMarkets projections, the hybrid cloud market is estimated to grow at CAGR of 22.5% over 2016-2021 time frame. In dollar terms, the market will grow from $33.28 billion in 2016 to $91.74 billion by 2021.

Meanwhile, demand for AI-based applications has gained immense popularity among enterprises owing to its positive effects on operations. According to Accenture (ACN) AI-based technologies is projected to boost labor productivity by up to 40% by fundamentally changing the way work is done and reinforcing the role of people to drive growth in business.

The advancement in AI tools and related applications like Big Data analytics, Natural Language Processing (NLP), Machine Learning and Deep Learning are primarily responsible for AIs massive growth projections. Per MarketsAndMarkets data, the overall AI market is anticipated to be worth $16.06 Billion by 2022, growing at a CAGR of 62.9% within the 2016- 2022 time frame.

Projections for IoT are also significantly bullish. According to Forbes, which quoted BCG data, spending on IoT technologies, apps and solutions will reach almost $267 billion by 2020.

The technology space continues to be investors favorite owing to its dynamic nature. The improving macro-environment, solid underlying fundamentals and impressive growth opportunities have been primarily responsible for the sectors earnings momentum, which is expected to persist in the first quarter.

According to the latest Earnings Preview, the sectors total earnings are expected to grow 10.7% from the same period last year on 6.2% higher revenues. This is higher than 9.2% earnings growth witnessed in fourth-quarter 2016 on a 5.6% increase in the top line.

With the existence of a number of industry players, finding the right stocks that have the potential to beat earnings could be a daunting task. Our proprietary methodology, however, makes it fairly simple for you. You could narrow down the list of choices by looking at stocks that have the combination of a favorable Zacks Rank #1 (Strong Buy), 2 (Buy) or 3 (Hold) and a positive Earnings ESP.

Earnings ESP is our proprietary methodology for determining stocks that have the best chance to surprise with their next earnings announcement. It provides the percentage difference between the Most Accurate estimate and the Zacks Consensus Estimate.

Our research shows that for stocks with this combination, the chance of a positive earnings surprise is as high as 70%.

Lets take a look atfour technology providers that have the right combination of elements to post an earnings beat this quarter:

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4 Stocks Set to Trump Earnings in the Technology Space - Investorplace.com

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Microsoft has started offering substantial Windows Server licence discounts as an incentive to embrace its cloud.

Redmond has rolled out its Azure Hybrid Use Benefits scheme, which it says can cut up to 40 per cent off the price of Windows Server virtual instances on Azure.

Azure Hybrid Use Benefit covers two-processor and 16-core Windows Server licences covered under Microsoft's volume Software Assurance programme.

The 40 per cent saving depends on usage, instance type and location.

Microsoft has wrapped the discount with new tools as a further incentive to drive uptake of Azure.

Also released was Azure Site Recovery to migrate virtual machines from AWS, VMware, Hyper-V or physical servers. The service lets you tag virtual machines within the Azure portal without needing to employ PowerShell.

A Cloud Migration Assessment has also been rolled out, which lets you discover servers in your on-prem setting and analyse their hardware configuration.

Read the original post:
Embrace our cloud, damn you: Microsoft dangles 40% discount on Azure instances - The Register

A row of eight NVIDIA graphics processing units (GPUs) packed into a Big Sur machine learning server at Facebook's data center in Prineville, Oregon. (Photo: Rich Miller)

The data center team at eBay is plenty familiar with high density data centers. The e-commerce giant has been running racks with more than 30 kilowatts (kW) of power density at the SUPERNAP in Las Vegas, seeking to fill every available slot in racks whenever possible.

But as eBay has begun applying artificial intelligence (AI) to its IT operations, the company has deployed more servers using graphics processing units (GPUs) instead of traditional CPUs.

From a data center power and cooling perspective, theyre a real challenge, said Serena DeVito, an Advanced Data Center Engineer at eBay. Most data centers are not ready for them. These are really power hungry little boxes.

The rise of artificial intelligence, and the GPU computing hardware that often supports it, is reshaping the data center industrys relationship with power density. New hardware for AI workloads is packing more computing power into each piece of equipment, boosting the power density the amount of electricity used by servers and storage in a rack or cabinet and the accompanying heat. The trend is challenging traditional practices in data center cooling, and prompting data center operators to adapt new strategies and designs.

All signs suggest that we are in the early phase of the adoption of AI hardware by data center users. For the moment, the trend is focused on hyperscale players, who are pursuing AI and machine learning at Internet scale. But soon there will be a larger group of companies and industries hoping to integrate AI into their products, and in many cases, their data centers.

Amazon Web Services, Microsoft Azure, Google Cloud Platform and IBM all offer GPU cloud servers. Facebook and Microsoft have each developed GPU-accelerated servers for their in-house machine learning operations, while Google went a step further, designing and building its own custom silicon for AI.

AI is the fastest-growing segment of the data center, but it is still nascent, said Diane Bryant, the Executive VP and General Manager of Intels Data Center Group. Bryant says that 7 percent of servers sold in 2016 were dedicated for AI workloads. While that is still a small percentage of its business, Intel has invested more than $32 billion in acquisitions of Altera, Nervana and MobilEye to prepare for a world in which specialized computing for AI workloads will become more important.

The appetite for accelerated computing shows up most clearly at NVIDIA, the market leader in GPU computing, which has seen its revenue from data center customers leap 205 percent over the past year. NVIDIAs prowess in parallel processing was seen first in supercomputing and high-performance computing (HPC), and supported by facilities with specialized cooling using water or refrigerants. The arrival of HPC-style density in data centers is driven by the broad application of machine learning technologies.

Deep learning on Nvidia GPUs, a breakthrough approach to AI, is helping to tackle challenges such as self-driving cars, early cancer detection and weather prediction, said Nvidia cofounder and CEO Jen-Hsun Huang. We can now see that GPU-based deep learning will revolutionize major industries, from consumer internet and transportation to health care and manufacturing. The era of AI is upon us.

And with the dawn of the AI era comes a rise in rack density, first at the hyperscale players and soon at multi-tenant colocation centers.

How much density are we talking about? A kilowatt per rack unit is common with these GPUs, said Peter Harrison, the co-founder and Chief Technical Officer at Colovore, a Silicon Valley colocation business that specializes in high-density hosting. These are real deployments. These customers are pushing to the point where 30kW or 40kW loads (per cabinet) are easily possible today.

A good example is CirraScale, a service provider that specializes in GPU-powered cloud services for AI and machine learning. CirraScale hosts some of its infrastructure in custom high-density cabinets at the ScaleMatrix data center in San Diego.

These technologies are pushing the envelope, said Chris Orlando, the Chief Sales and Marketing Officer and a co-founder of ScaleMatrix. We have people from around the country seeking us out because they have dense platforms that are pushing the limits of what their data centers can handle. With densities and workloads changing rapidly, its hard to see the future.

Cirrascale, the successor to the Verari HPC business, operates several rows of cabinets at ScaleMatrix, which house between 11 and 14 GPU servers per cabinet, including some connecting eight NVIDIA GPUs using PCIe a configuration also seen in Facebooks Big Sur AI appliance andthe NVIDIA DGX-1supercomputer in a box.

Over the past decade, there have been numerous predictions of the imminent arrival of higher rack power densities. Yet extreme densities remain limited, primarily seen in HPC. The consensus view is that most data centers average 3kW to 6kW a rack, with hyperscale facilities running at about 10kW per rack.

Yet the interest in AI extends beyond the HPC environments at universities and research labs, bringing these workloads into cloud data centers. Service providers specializing in high-density computing have also seen growing business from machine learning and AI workloads. These companies use different strategies and designs to cool high-density cabinets.

A TSCIF aisle containment system inside the SUPERNAP campus in Las Vegas. (Photo: Switch)

The primary strategy is containment, which creates a physical separation between cold air and hot air in the data hall. One of the pioneers in containment has been Switch, whose SUPERNAP data centers use a hot-aisle containment system to handle workloads of 30kW a rack and beyond. This capability has won the business of many large customers, allowing them to pack more computing power into a smaller footprint. Prominent customers include eBay, with its historic focus on density, which hosts its GPU-powered AI hardware at the SUPERNAPs in Las Vegas.

For hyperscale operators, data center economics dictates a middle path on the density spectrum. Facebook, Google and Microsoft operate their data centers at higher temperatures, often above 80 degrees in the cold aisle. This saves money on power and cooling, but those higher temperatures make it difficult to manage HPC-style density. Facebook, for example, seeks to keep racks around 10 kW, so it runs just four of its Big Sur and Big Basin AI servers in each rack. The units are each 3U in depth.

Facebooks machine learning servers feature eight NVIDIA GPUs, which the custom chassis design places directly in front of the cool air being drawn into the system, removing preheat from other components and improving the overall thermal efficiency. Microsofts HGX-1 machine learning server, developed with NVIDIA and Ingrasys/Foxconn, also features eight GPUs.

A custom rack in a Google data center packed with Tensor Processing Unit hardware for machine learning. (Photo: Google)

While much of the AI density discussion has focused on NVIDIA gear, GPUs arent the only hardware being adopted for artificial intelligence computing, and just about all of these chips result in higher power densities.

Google decided to design and build its own AI hardware centered on the Tensor Processing Unit (TPU), a custom ASIC tailored for Googles TensorFlow open source software library for machine learning. An ASIC (Application Specific Integrated Circuits) is a chip that can be customized to perform a specific task, squeezing more operations per second into the silicon. A board with a TPU fits into a hard disk drive slot in a data center rack.

Those TPUs are more energy dense than a traditional x86 server, said Joe Kava, the Vice President of Data Centers at Google. If you have a full rack of TPUs, it will draw more power than a traditional rack. It hasnt really changed anything for us. We have the ability in our data center design to adapt for higher density. As a percentage of the total fleet, its not a majority of our (hardware).

Tomorrow: We look at data center service providers focused on GPU hosting, and how they are designing for extreme density.

See the rest here:
AI Boom Boosts GPU Adoption, High-Density Cooling - Data Center Frontier (blog)

Conventional wisdom in the public cloud market is that there are three leaders: Amazon Web Services followed by Microsoft Azure, and Google Cloud Platform.

Those companies assemble and sell massive arrays of servers, storage, and networking to businessesmost of which don't want to build more of their own data centers. Towards that end, those three cloud superpowers alone spent roughly $31 billion last year to extend their data center capacity around the world, according to the Wall Street Journal , which tabulated that total from corporate filings.

By comparison, Oracle ( orcl ) , which is making its own public cloud push, spent about $1.7 billion. To most observers, that looks like a stunning mismatch.

But Mark Hurd, Oracle's co-chief executive, would beg to differ. In his view, there are data centers and then there are data centers. And Oracle's data centers, he said, can be more efficient because they run Oracle hardware and supercharged databases.

"We try not to get into this capital expenditure discussion. It's an interesting thesis that whoever has the most capex wins," Hurd said in response to a question from Fortune at a Boston event on Tuesday. "If I have two-times faster computers, I don't need as many data centers. If I can speed up the database, maybe I need one fourth as may data centers. I can go on and on about how tech drives this."

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"Our core advantage is what we've said all along, which is that it's about the intellectual property and the software, not about who's got the most real estate," Hurd added. "We have spent billions over the past year, but in isolation, that's a discrete argument that I find interesting, but not fascinating."

Following up via email, Hurd said: This isnt a battle of capex. This is about R&D, about technology, software, innovation and IP; and then the capex to make it work."

Oracle has said it runs its data centers on Oracle Exadata servers , which are turbocharged machines that differ fundamentally from the bare-bones servers that other public cloud providers deploy by the hundreds of thousands in what is called a scale-out model. The idea is that when a server or two among the thousands failas they willthe jobs get routed to still-working machines. It's about designing applications that are easily redeployed.

Oracle is banking more on what techies call a "scale-up" model in which fewer, but very powerful computersin Exadata's case each with its own integrated networking and storagetake on big workloads.

Oracle execs, including executive chairman Larry Ellison, have argued that Oracle's big machines can actually work cheaper and more efficiently than the other public cloud configurations. Many industry analysts have their doubts on that, maintaining Oracle must spend much more to catchup with Amazon. Toward that end, in January, Oracle announced plans to add three new data center farms within six months and more to come.

There are those who think that Fortune 500 companies relying on Oracle databases and financial applications give Oracle an advantage because they are loathe to move those workloads to another cloud providerdespite AWS wooing them with promises of easy migrations other perks.

In late March, AWS chief executive Andy Jassy claimed the company had converted 22,000 databases from other vendors to its own database services. AWS ( amzn ) does not break out which databases those customers had been using.

Hurd took up that point as well: "How much database market will Oracle lose to [Amazon] Aurora? My guess is close to zero." (Aurora is one of several database options that AWS offers.)

"The third largest database in the world is IBM DB2, and it's been going out of business for 20 years," Hurd said in a characterization that IBM ( ibm ) would dispute. "If it was so easy to replace databases, DB2 market share would be zero."

That is because most databaseswhich companies rely on as the basis for core accounting and financial operationsrun custom programming, which is hard to move.

Still, in Amazon, Oracleas well as IBM, Microsoft, and virtually every legacy information technology providerfaces a huge challenge. AWS is on track to log more than $14 billion in revenue this year.

Note: (April 12, 2017 12:55 p.m.) This story was updated to add an additional quote from Oracle's Mark Hurd.

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Oracle CEO: We Can Beat Amazon and Microsoft Without as Many Data Centers - Fortune

Data centers are declining worldwide both in numbers and square footage, according to IDC -- a remarkable change for an industry that has seen booming growth for many years.

Users are consolidating data centers and increasingly renting server power. These two trends are having a major impact on data center space.

The number of data centers worldwide peaked at 8.55 million in 2015, according to IDC. That figure began declining last year, and is expected to drop to an expected 8.4 million this year. By 2021, the research firm expects there to be 7.2 million data centers globally, more than 15% fewer than in 2015.

The global square footage of data centers, after recent boom times, is also expected to slide. In 2013, data centers totalled 1.6 billion square feet. That was when big service providers like Amazon, Microsoft and Google were building huge data center complexes -- pushing square footage globally to 1.8 billion this year.

But IDC expects that number to decline from now on. Cloud adoption is a major reason for the trend.

Consider the adoption of Office 365, said Tad Davies, who heads consulting services at Bick Group, a data center consultancy. "Easy to move to and eliminates infrastructure in my data center," he said. "Same for CRM."

Consolidation is also playing a role, said Davies, as are new approaches to computing. New firms are adopting "cloud first" strategies, he said. "As they grow into larger organizations, the data center is never created."

Large users -- especially the U.S. government -- have been shrinking their data center space to drive efficiency. Better server utilization often means more consolidation.

While the biggest decline is affecting in-house data centers, said IDC, service provider data centers continue to expand. But even there, the pace of growth is moderating as the market matures.

Despite stagnant growth, data centers are still needed, Davies said. That's because there's limits to what can go into the cloud.

"Many applications that end users have built and further refined over the years are not cloud compatible," he said. "To get there requires significant re-architecture as well as investment."

The cloud is not necessarily less expensive than an on-premise operation, said Davies. But it does provide speed, flexibility and an operating expense, or OPEX, model.

In terms of revenue, the data center system market, which includes software and hardware, is barely growing, according to research firm Gartner.

"Enterprises are moving away from buying servers from the traditional vendors and instead renting server power in the cloud from companies such as Amazon, Google and Microsoft," John-David Lovelock, research vice president at Gartner, said in a statement. "This has created a reduction in spending on servers, which is impacting the overall data center system segment."

Last year, spending on data centers declined 0.1%, said Gartner. This year it's expected to increase by only 0.3%.

Link:
Data centers decline as users turn to rented servers - Computerworld