Friday, March 25th, 2016

Time	Event
12:00p	What Enterprise Data Center Managers Can Learn from Web Giants This month, we focus on the open source data center. From innovation at every physical layer of the data center coming out of Facebook’s Open Compute Project to the revolution in the way developers treat IT infrastructure that’s being driven by application containers, open source is changing the data center throughout the entire stack. This March, we zero in on some of those changes to get a better understanding of the pervasive open source data center. Here’s part two of our interview with Amir Michael, who spent most of the last decade designing servers for some of the world’s biggest data centers, first at Google and then at Facebook. He was one of the founders of the Open Compute Project, the Facebook-led open source hardware and data center design community. Today, Michael is a co-founder and CEO of Coolan, a startup that aims to help data center operators make more informed decisions about buying hardware and make their data centers more efficient and resilient using Big Data analytics. Read the first part of our interview with Amir Michael here. Data Center Knowledge: How did the idea to start Coolan come about? Amir Michael: My team built large volumes of servers while at Facebook, hundreds of thousands of them. As we built them, we put them in the data center and then turned around and started working on the next generation of design and didn’t really look back to see how decisions we made during the design actually panned out operationally. We made a decision to buy premium memory and paid more for that because we thought it wouldn’t fail. We made certain design decisions that we thought would make the system more or less reliable at a cost trade-off, but never actually went back and measured that. And we’re always making decisions around what kinds of components or system to buy and trying to decide if we pay more for an enterprise type of component, or maybe we can do with a consumer type of component. New technology, especially new technology entering the data center, doesn’t have good information around reliability. You don’t have a track record around that. When I was at Facebook, I started to look back and say, “Hey, so what were the operational costs of all these decisions we made?” And we didn’t have a lot of data. I started talking to peers in the industry and said, “Let’s compare notes. What does your failure rate look like compared to mine?” And there wasn’t a lot of information there, and a lot of the people in this industry aren’t’ actually measuring that. The idea for Coolan is to create a platform that makes it very easy for people to share data about their operation, about failure rates, about quality of components, about errors that they’re generating, about the environments that their servers are running in, both utilization and also the physical environment around them, and make that as easy as possible to do so people can have this rich data set that we collect for them and analyze. Once you have this large data set, not only are we measuring and benchmarking someone’s infrastructure, we can now allow them to compare themselves to their peers. Your failure rate is lower, and here’s why it is: because you’re running at optimal temperature, your firmware is the latest version, and it’s more stable. Now that we have this type of comparison, we add a whole new layer of transparency into the industry, where people are making decisions based on actual data, informed decisions, not trying to guess what component is right for them. Once you have that, you’ll quickly understand which vendors are right for you, which ones are not right for you, and you’re making much more informed decisions about this large amount of capital you’re about to deploy. It adds a whole new layer of transparency to the industry, which I desperately wanted when I was at Facebook. I wanted to know if I should go to vendor X or Y. I didn’t have information, and when you ask [vendors] about quality of the product, you didn’t get a good answer. They gave you some mathematical formula they used to calculate [Mean Time Between Failures], but it didn’t actually correlate to what was in the field. (Comment on this)
4:31p	Security Provider Verizon Enterprise Suffers Data Breach By The VAR Guy “With cyber attacks growing in strength and number, it’s harder to avoid becoming a victim,” according to Verizon Enterprise Security Solutions website. Ironically, the company learned this week just how true that is after a data breach resulted in contact information for 1.5 million of its customers being put on sale on an underground cybercrime forum, as reported by KrebsOnSecurity. According to the report, a “prominent member” of the secret forum offered the whole data set at $100,000, or chunks of 100,000 records at $10,000 each. In addition, the seller offered to sell the inside scoop about the security vulnerabilities in Verizon’s website. “Verizon recently discovered and remediated a security vulnerability on our enterprise client portal,” the company said in an emailed statement to Brian Krebs. “Our investigation to date found an attacker obtained basic contact information on a number of our enterprise customers. No customer proprietary network information (CPNI) or other data was accessed or accessible.” According to Verizon, 97 percent of Fortune 500 customers are users of Verizon Enterprise. Krebs points out that even if the contact information is limited to technical managers, the stolen data is bound to be rich with targets for phishing and other email scams. This is bound to be a little embarrassing for a company known for assisting its customers after a data breach, with special services in forensics and investigations. Verizon even publishes an annual Data Breach Investigations Report that’s filled with interesting case studies of actual breaches, and Gartner named Verizon a leader in managed security services. This first ran at http://thevarguy.com/network-security-and-data-protection-software-solutions/security-provider-verizon-enterprise-suffers (Comment on this)
6:25p	Kava: Google Redesigns Data Center Cooling Every 12 to 18 Months For any large-scale internet company, data center efficiency and profit margins are closely linked, and at scale like Google’s, data center efficiency is everything. Designing for better efficiency is a never-ending process for Google’s infrastructure team, and since cooling is the biggest source of inefficiency in data centers, it has always gotten special attention. “Since I’ve been at Google, we have redesigned our fundamental cooling technology on average every 12 to 18 months,” said Joe Kava, who’s overseen the company’s data center operations for the last eight years. This efficiency chase has produced innovations in water and air-based data center cooling. The company has developed ways to use sea water and industrial canal water for cooling; it has devised systems for reclaiming and recycling grey water and for harvesting rain water. Google has also pushed the envelope in using outside air for cooling, or airside economization. “We’ve designed data centers that don’t use water cooling at all,” Kava said. Like other web-scale data center operators and companies in the business of providing data center capacity, data center design at Google is something that gets improved with every new facility that comes online. “There’s no one-size-fits-all model at Google,” he said. “Each data center is designed for highest performance and efficiency for that specific location. We don’t rest on our laurels.” Data Centers Central to Google’s Cloud Pitch Kava spoke about Google’s data center best practices this week at the company’s first cloud user conference in San Francisco, called GCP Next. The event was a big effort to send the message that Google is a serious competitor to Amazon Web Services and Microsoft Azure in the enterprise cloud market. Google CEO Sundar Pichai and the company’s former CEO and chairman Eric Schmidt delivered keynotes at the event; and so did Urs Hölzle, its senior VP of technical infrastructure and its eighth employee, and Diane Greene, the founder of VMware, who recently joined to lead Google’s enterprise cloud business. See more: Go on a Virtual 360-Degree Google Data Center Tour Kava’s keynote at GCP Next was no filler. The might of Google data centers is a key part of its cloud pitch, on par with the low cost of its cloud services and all the sexy features, like machine learning and container orchestration. The message boils down to something like this: Look, we design and build the best data centers in the world, and now you can use them too. It’s a message Google has been using to sell its cloud for several years now. Testing claims like this is difficult, since ultimately the only customer of Google data centers is Google itself, and, as Kava put it, 99 percent of Googlers themselves aren’t allowed to set foot in the company’s data centers for security purposes. But the overall strength of engineering at Google is hard to argue with. “World’s Largest Data Center Campus” Kava showed off a video of the company’s data center in Iowa, which he said was the largest data center campus in the world: Note the construction trucks in this screenshot from the video for scale. Each building pad is more than one-third of a mile long and houses a multi-story data center, he said. Data center scale is another important message for Google to send as it ratchets up its cloud business. Today, its biggest rivals are far ahead in terms of the number of locations their cloud services are available in, and Google has to catch up. The company announced this week it was bringing two additional cloud availability regions online this year – in Oregon and Japan – and 10 more locations next year. Machine Learning Helps Fine-Tune for Efficiency Google execs spent a lot of time talking about machine learning at the conference. The company is increasingly using machine learning technologies for its web services and this week launched first machine learning services as cloud offerings. One way it is using machine learning internally is to optimize data center efficiency, as Data Center Knowledge reported earlier. Data centers are complex systems working together to get the best performance, Kava said. It is impossible for humans to understand how to optimize these systems because of the sheer number of interactions and operating parameters involved. “However, it is pretty trivial for computers to crunch through those scenarios and find the optimal settings,” he said. “Over the past couple of years we’ve developed these algorithms and we’ve trained them with billions of data points from all of our data centers all over the world.” Read more: Google Using Machine Learning to Boost Data Center Efficiency Data visualization generated using analysis of this data helps operations teams decide how to set up electrical and mechanical plants in Google data centers: Data visualization helps the team see inflection points in curves that may not be intuitive otherwise. Using this process, Kava’s team has realized that there can be as many as 19 independent variables that affect data center performance. Perhaps the biggest operating principle in Google’s infrastructure approach is end-to-end ownership. Designing everything in-house, from servers to data centers, and relying exclusively on internal staff for data center operations, the company exercises full control of its infrastructure, its performance and efficiency. (Comment on this)

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