Monday, March 6th, 2017

Time	Event
1:00p	How Linux Conquered the Data Center Some of the people who worked to create the original Linux operating system kernel remember this time with almost crystal clarity, as though a bright flashbulb indelibly etched its image on the canvasses of their minds. In 1998 Red Hat was continuing to gather together names of new allies and prospective supporters for its enterprise Linux.  Several more of the usual suspects had joined the party: Netscape, Informix, Borland’s Interbase, Computer Associates (now CA), Software AG.  These were the challengers in the Windows software market, the names to which the VARs attached extra discounts.  As a single glimpse of the Softsel Hot List or the Ingram Micro D sales chart would tell any CIO studying the market, none of these names were the leaders in their respective software categories, nor were they expected to become leaders. One Monday in July of that year, Oracle added its name to Red Hat’s list. “That was a seminal moment,” recalled Dirk Hohndel, now VMware’s Chief Open Source Officer.  He happened to be visiting the home of his good friend and colleague, Linus Torvalds — the man for whom Linux was named.  A colleague of theirs, Jon “Maddog” Hall, burst in to deliver the news: Their project was no longer a weekend hobby. “Linus and I looked at each other and said, ‘Wow!  That will change the world!’” “When Oracle started to support their database on Red Hat Linux,” related Mark Hinkle, the Linux Foundation’s vice president of marketing, “that was a signal to the industry that you could trust your financial data to a Linux operating system.” “It was an announcement of something that Oracle had planned to do,” noted Hohndel.  “We all have seen how these announcements often play out.” The advocates of free and open software, Hall among them, had long spoken about the inexorable march of progress and the alignment of free software with free expression.  But history will show that the progenitors of Linux first perceived victory not when some debate opponent conceded the merits of the GNU license, but rather when a major commercial database vendor gave Linux its tacit stamp of approval. The Facilitator The tech journalists of the day — veterans of the battle between MS-DOS and OS/2, and between Windows and Macintosh — tended to view any prospective platform battle in the enterprise market as political warfare, whose key supporters signed up in one camp or the other. Oracle’s news did not have a preassigned camp.  A casual admission that Oracle was testing the Linux support waters appeared in paragraph 13 of InfoWorld’s page-5 news of an announcement by Informix that it would support Linux. Maybe Informix’s move was prompted, the piece suggested, by rumors of Linux running in Oracle’s labs. The cover of Canada’s weekly Maclean’s magazine for July 20, featuring Red Hat founder Robert Young, was titled, “The War for the Desktop.”  Right scale, wrong geography. Oracle’s then CEO, Larry Ellison, delivers a keynote address at the 2006 Oracle OpenWorld conference October 25, 2006 in San Francisco. (Photo by Justin Sullivan/Getty Images) Critical mass for enterprise Linux had already been achieved.  Oracle was aware of this before most everyone else — even before the folks who created Linux.  Which is one reason they remember the event like a historical milestone.  But they weren’t entirely thinking of what it meant for Linux alone. “There was this sort of greenfield opportunity with the explosion of the Internet and connected technologies over a non-private network,” Hinkle said. “So the Web [HTTP], e-mail, and DNS became opportunities for Linux, the Apache Web Server, and BIND running on Linux, started to grow and really exploded, because of the need for these services exploded.” Together, these services formed a flexible, adaptable stack.  A technician could easily build a server, install Linux, install the stack, and deploy the server.  Later, an operator could instantiate a virtual server with just as few steps, but in a few minutes rather than a few hours. This was the breakthrough:  Organizations could build infrastructure — first physical, then virtual — when and where it was needed.  Linux was just a facilitator. The Tide Hohndel, one of the Linux kernel’s original midwives, has honed a theory over the years as to the true underpinnings of Linux’s success.  Though he cites events such as the December 1999 IPO of VA Linux Systems as critical to establishing Linux as a premier OS in the public mind, a more significant trend was running beneath the headlines. The ascent of open source services brought with it DNS and the ability for data centers to begin discriminating which servers were delegated which workloads, according to their internet domains.  Then MySQL brought along the ability to simply query information — especially server logs — without invoking some gargantuan data warehouse on a per-seat licensing basis.  The rise of MySQL’s stature — especially in the tech press — may have precipitated Oracle’s strategy. “The Apache Web server, and MySQL, and certainly PHP played a huge role in the success of open source, and Linux is part of this,” said Hohndel. Open source alternatives threatened the comfortable position of databases and productivity packages in the enterprise, not because they were attacking the desktop like MacLean’s suggested, but rather because they were rendering the desktop irrelevant.  Oracle was among the first to seriously respond.  That response garnered the attention of CIOs and IT managers. The moment the enterprise witnessed Oracle bestowing its blessing upon Linux as a legitimate option for hosting business databases, the CIO began taking Linux as legitimately as did the IT manager. “If it hadn’t been for the early free software movement and the open source movement in the late ‘90s, Linux by itself would not have changed the world,” Hohndel continued.  “Open source is a software development methodology.  It is fantastic at creating collaboration, innovation, and shared APIs.  But it actually has a very poor track record at creating enterprise, production-ready software.” When open source met virtualization, the resulting chemistry changed the world.  Infrastructure became virtual, and workloads became portable.  The cloud was born.  Enterprise software publishers couldn’t come up with anything competitive, because they had not yet fathomed what the cloud actually meant. “I often compare software development to biological processes,” explained Linus Torvalds, during a recent Open Source Leadership Summit, “where it really is evolution.  It is not intelligent design.  I’m there in the middle of the thing, and I can tell you, it is absolutely not intelligent design.” Software, as Torvalds perceives it, is a congealing, coalescing nebula of contributed ideas and trial-and-error functions, only some of which are inevitably successful.  He argued that perhaps as important to the ascent of open source in the enterprise as Linux, if not more so, was the creation of Git (which he also spearheaded).  This is the formal system for automating the contribution of bits and pieces of code upstream.  It is the guarantor of improvements and disentanglements, such as there are, not only in Linux but all the components of the stack which Linux facilitates. Linux creator Linus Torvalds speaking at the Open Source Leadership Summit, February 15, 2017 “All the really stressful times for me personally have been about process,” said Torvalds.  “They’ve never been about code.  When code doesn’t work, that can actually be exciting.  That can be fun!  You hit your head on a wall for a couple of weeks and that can be very frustrating; but then when you solve it, the feeling of accomplishment is huge.” Git enabled an ecosystem for software that enlisted the customer as a participant, not just a benefactor.  It spurred on the development of containerization platforms such as Docker and CoreOS, orchestration platforms such as Kubernetes, and schedulers such as Mesosphere DC/OS.  While Linux is the progenitor of Git, it is Git that may be responsible for the ultimate conquest of the data center. The Reformation Like Keurig with its encapsulated coffee and HP with its impregnable ink packets, Microsoft believed it had a permanent supply line to its own gravy train.  Virtualization blew up this train.  Up to this point, if an enterprise had a key business process or an application relying upon a critical database, it needed a Windows-based platform to run it.  Windows was tied to processors.  Thus there appeared to be an unbreakable chain linking databases to operating systems to processors. Once that chain was broken, the concept of composable infrastructure was born.  But the world still looked to Microsoft to provide it. Instead, in 2007, Microsoft began a campaign of reduced expectations for what Windows Server could deliver.  Incapable of replicating the live migration feature that VMware demonstrated with aplomb, Microsoft postponed deadlines into the stratosphere and in the interim tried convincing its customers (through the press) that such a feature wasn’t something anyone wanted anyway. Microsoft’s then CEO, Steve Ballmer, delivers the keynote address for the global launch of Windows Server 2003 April 24, 2003 in San Francisco. (Photo by Justin Sullivan/Getty Images) The coup de grace against Microsoft’s mountaintop position in enterprise operating systems had been delivered. . . but not by Linux.  Leveraging so much functionality upon a single design point — the OS/processor connection — ended up immobilizing Windows Server at the time it needed to evolve most.  This, in turn, forced the server industry to implement a massive work-around, paving the way for cloud platforms. Linux marched in, along with that conquering force.  Today, Microsoft is in the midst of “re-imaging,” if you will, Windows Server as a service provider — as a key component, but certainly one component among many.  For Microsoft, the platform has become Azure. “As a platform, specifically a virtualization platform, we need to ensure that we can host both Windows Server and Linux workloads equally well,” Erin Chapple, Microsoft’s Windows Server General Manager, said in a message to Data Center Knowledge.  “In retrospect, we should have started that work sooner for Linux.” Note Chapple’s relocation of “we” and “platform” to locations outside of Windows Server, which is no longer presented as the ecosystem in its totality. “We believe that customers look at the overall value proposition of data center products in their procurement decisions, from licensing to innovation roadmap and beyond,” she said.  “In the last couple decades, this approach has led to the mixed approach to operating systems in the data center that many organizations, including Microsoft, have today. . . As part of our approach to listening to customers and our learnings from running one of the two largest commercial clouds in the world, we’ve invested in an open and flexible platform that is all about choice and supporting customers to run all their workloads, including Linux, and in Windows Server 2016, a product designed to meet our customers’ unique needs in the modern data center.” Oracle’s stamp of approval, the rise of the stack, the triumph of virtualization, the easy success of Git, the empowerment of the cloud, and the collapse of the tower that Microsoft built — all these independent factors resulted in today’s state of affairs in the enterprise data center. And what is that state, exactly?  There’s a good argument to be made that Windows Server has not really been vanquished — indeed, that its place in the enterprise remains guaranteed, even as just the support platform for certain applications.  Comparing Windows Server to Linux in today’s server environment may be akin to comparing a piston engine to a square yard of asphalt in today’s highway environment.  The only folks who still see a need to pair them together may be the ones who publish headlines for a living. “I think that there’s always going to be — especially for coordinated, massive services — a need for an operating system and a scheduler,” remarked the Linux Foundation’s Hinkle.  “But I do think you are going to see in the future containers running directly on the chip without the operating systems that you see today.  With more IT, there’s just going to be more use cases.  I don’t know that any time soon you’re going to see a situation where the operating system isn’t important.  It’s just going to be more and more abstracted.” Hohndel and Torvalds were right that the world would change.  And in this new and altered realm, the part that scored the first blow in a triumphant battle may continue to exist.  But historians — if there are any — may have a difficult time identifying it. 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4:00p	JPMorgan Marshals an Army of Developers to Automate Finance By Hugh Son (Bloomberg) — At JPMorgan Chase & Co., a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours. The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers. The software reviews documents in seconds, is less error-prone and never asks for vacation. While the financial industry has long touted its technological innovations, a new era of automation is now in overdrive as cheap computing power converges with fears of losing customers to startups. Made possible by investments in machine learning and a new private cloud network, COIN is just the start for the biggest U.S. bank. The firm recently set up technology hubs for teams specializing in big data, robotics and cloud infrastructure to find new sources of revenue, while reducing expenses and risks. The push to automate mundane tasks and create new tools for bankers and clients — a growing part of the firm’s $9.6 billion technology budget — is a core theme as the company hosts its annual investor day on Tuesday. Behind the strategy, overseen by Chief Operating Officer Matt Zames and Chief Information Officer Dana Deasy, is an undercurrent of anxiety: Though JPMorgan emerged from the financial crisis as one of few big winners, its dominance is at risk unless it aggressively pursues new technologies, according to interviews with a half-dozen bank executives. Redundant Software That was the message Zames had for Deasy when he joined the firm from BP Plc in late 2013. The New York-based bank’s internal systems, an amalgam from decades of mergers, had too many redundant software programs that didn’t work together seamlessly. “Matt said, ‘Remember one thing above all else: We absolutely need to be the leaders in technology across financial services,’” Deasy said last week in an interview. “Everything we’ve done from that day forward stems from that meeting.” A QuickTake explainer on fintech After visiting companies including Apple Inc. and Facebook Inc. three years ago to understand how their developers worked, the bank set out to create its own computing cloud called Gaia that went online last year. Machine learning and big-data efforts now reside on the private platform, which effectively has limitless capacity to support their thirst for processing power. The system already is helping the bank automate some coding activities and making its 20,000 developers more productive, saving money, Zames said. When needed, the firm can also tap into outside cloud services from Amazon.com Inc., Microsoft Corp. and International Business Machines Corp. Tech Spending JPMorgan will make some of its cloud-backed technology available to institutional clients later this year, allowing firms like BlackRock Inc. to access balances, research and trading tools. The move, which lets clients bypass salespeople and support staff for routine information, is similar to one Goldman Sachs Group Inc. announced in 2015. JPMorgan’s total technology budget for this year amounts to 9 percent of its projected revenue — double the industry average, according to Morgan Stanley analyst Betsy Graseck. The dollar figure has inched higher as JPMorgan bolsters cyber defenses after a 2014 data breach, which exposed the information of 83 million customers. “We have invested heavily in technology and marketing — and we are seeing strong returns,” JPMorgan said in a presentation Tuesday ahead of its investor day, noting that technology spending in its consumer bank totaled about $1 billion over the past two years. One-third of the company’s budget is for new initiatives, a figure Zames wants to take to 40 percent in a few years. He expects savings from automation and retiring old technology will let him plow even more money into new innovations. Not all of those bets, which include several projects based on a distributed ledger, like blockchain, will pay off, which JPMorgan says is OK. One example executives are fond of mentioning: The firm built an electronic platform to help trade credit-default swaps that sits unused. ‘Can’t Wait’ “We’re willing to invest to stay ahead of the curve, even if in the final analysis some of that money will go to product or a service that wasn’t needed,” Marianne Lake, the lender’s finance chief, told a conference audience in June. That’s “because we can’t wait to know what the outcome, the endgame, really looks like, because the environment is moving so fast.” As for COIN, the program has helped JPMorgan cut down on loan-servicing mistakes, most of which stemmed from human error in interpreting 12,000 new wholesale contracts per year, according to its designers. JPMorgan is scouring for more ways to deploy the technology, which learns by ingesting data to identify patterns and relationships. The bank plans to use it for other types of complex legal filings like credit-default swaps and custody agreements. Someday, the firm may use it to help interpret regulations and analyze corporate communications. Another program called X-Connect, which went into use in January, examines e-mails to help employees find colleagues who have the closest relationships with potential prospects and can arrange introductions. Creating Bots For simpler tasks, the bank has created bots to perform functions like granting access to software systems and responding to IT requests, such as resetting an employee’s password, Zames said. Bots are expected to handle 1.7 million access requests this year, doing the work of 140 people. While growing numbers of people in the industry worry such advancements might someday take their jobs, many Wall Street personnel are more focused on benefits. A survey of more than 3,200 financial professionals by recruiting firm Options Group last year found a majority expect new technology will improve their careers, for example by improving workplace performance. Click here for Options Group’s survey on technology’s impact “Anything where you have back-office operations and humans kind of moving information from point A to point B that’s not automated is ripe for that,” Deasy said. “People always talk about this stuff as displacement. I talk about it as freeing people to work on higher-value things, which is why it’s such a terrific opportunity for the firm.” To help spur internal disruption, the company keeps tabs on 2,000 technology ventures, using about 100 in pilot programs that will eventually join the firm’s growing ecosystem of partners. For instance, the bank’s machine-learning software was built with Cloudera Inc., a software firm that JPMorgan first encountered in 2009. “We’re starting to see the real fruits of our labor,” Zames said. “This is not pie-in-the-sky stuff.” (Comment on this)
4:30p	The Right Approach to Database Monitoring Can Eliminate Poor App Performance and Availability Kelsey Uebelhor is Director of Product Marketing at VividCortex. Editor’s Note: In this three-part series of articles, we look at various approaches to database monitoring that can improve app performance and availability, online customer experience, and engineering team productivity. In this article, we address poor app performance. Many components contribute to an app’s performance, but the database is at the foundation of these technology stacks. When an app is functioning poorly, it is often due to problems at the database level that include: server stalls, bad query performance or poor latency. These are just three of the common database-related challenges engineering teams encounter every day. Good database performance monitoring can help DBAs, developers, and engineers quickly diagnose and resolve these issues. But what do you need to know so you can choose the right approach? To answer this question, you need to start by understanding where application monitoring ends and database monitoring begins. Application Monitoring is Not Enough Today, businesses build modern apps by deliberately making their multi-tier architecture mostly stateless, which makes those apps easy to manage and scale. But this also makes them highly demanding—sending countless, and sometimes arbitrary, queries against the databases and assuming they will perform well. Because they are “stateful,” all the heavy lifting is delegated to the databases. While the primary concern for most businesses is application performance, that does not mean you can focus only on monitoring the application using Application Performance Monitoring (APM) tools. APM tools can help you identify slow application transactions, but they typically cannot help to diagnose or resolve the problem. Issue identification is obviously important, but to quickly diagnose and fix the problem you need to drill down into the database. Database Monitoring Essentials Database monitoring involves much more than graphing counters and CPU utilization trends over time. In a complex, modern architecture, databases can be a leading cause of system performance problems, and getting to the source of issues requires digging a bit deeper. It starts with query monitoring and workload analysis, deeper drill down, and anomaly detection. Query Monitoring and Workload Analytics — The database’s sole reason for existing is to run a workload—to execute queries. Therefore, the only way to know if the database is healthy and performing well is to measure what matters: whether its workload is completing successfully, quickly, and consistently. Queries need to be measured overall as well as individually, and they need to be deeply analyzed. Query workloads are huge and complex datasets. DBAs and engineering teams need to be able to: drill down to individual statement executions to examine the specifics; automate capture and analysis of query execution plans (EXPLAIN plans); aggregate and segment queries to find the big problems fast; and compare workload changes over separate time periods. Drill Down and Zoom-in— To monitor large, distributed, hybrid systems effectively, monitoring tools must present an aggregate view and enable rapid zoom-in and drill-down to the finest level of detail. Without the high-level view, monitoring isn’t scalable; but without the deep dive, you can’t solve problems effectively. You need rapid drill-down and zoom-in by multiple dimensions that include: hosts, tags, users, roles and time ranges. For high volume, highly scalable environments you need at least one-second granularity and microsecond precision, as query performance is often sub-millisecond. You must also be able to drill down to individual queries, disks, or processes. Anomaly Detection— For database monitoring, the volume of data is typically several orders of magnitude greater than that generated by basic system monitoring. Humans cannot begin to process this volume and complexity of data. Traditional monitoring tools let you generate alerts on static thresholds, such as “CPU exceeded 90 percent.” But this approach does not scale. Systems are constantly changing; what’s normal at 2 a.m. is very different from what you should expect at 2 p.m., and what was once a meaningful threshold can become inconsequential in another context. That’s why all modern monitoring tools offer some form of anomaly detection. However, for database monitoring this capability is particularly important, due to the variability and volume of the data being transmitted. With anomaly detection, you can do things like automatic baselining and seasonality prediction using “lateral logic” metrics that measure “time-to-disk-full” instead of simply alerting when the disk is already full. As the engine of high-performance systems, databases need instrumentation that will enable fast discovery of issues and the fine tuning necessary to maintain uptime and scalability.  Facing constant growth and complexity, the job for developers, engineers, and DBAs is only getting tougher. Having complete visibility into performance metrics helps users better understand the many ways databases affect overall app performance and availability and what to optimize to keep apps running at peak performance. In part two of the series, “How Database Monitoring Can Eliminate Problems Before Customers Notice,” we will discuss how you can use proactive monitoring to prevent issues before they ever occur. Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Penton. Industry Perspectives is a content channel at Data Center Knowledge highlighting thought leadership in the data center arena. See our guidelines and submission process for information on participating. View previously published Industry Perspectives in our Knowledge Library. (Comment on this)

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