Posts Tagged ‘Power Systems’

Are Quantum Computers Even Feasible

November 29, 2018

IBM has toned down its enthusiasm for quantum computing. Even last spring it already was backing off a bit at Think 2018. Now the company is believes that quantum computing will augment classical computing to potentially open doors that it once thought would remain locked indefinitely.

First IBM Q computation center

With its Bristlecone announcement Google trumped IBM with 72 qubits. Debating a few dozen qubits more or less may prove irrelevant. A number of quantum physics researchers have recently been publishing papers that suggest useful quantum computing may be decades away.

Mikhail Dyakonov writes in his piece titled: The Case Against Quantum Computing, which appeared last month in Spectrum IEEE.org. Dyakonov does research in theoretical physics at Charles Coulomb Laboratory at the University of Montpellier, in France.

As Dyakonov explains: In quantum computing, the classical two-state circuit element (the transistor) is replaced by a quantum element called a quantum bit, or qubit. Like the conventional bit, it also has two basic states. But you already know this because DancingDinosaur covered it here and several times since.

But this is what you might not know: With the quantum bit, those two states aren’t the only ones possible. That’s because the spin state of an electron is described as a quantum-mechanical wave function. And that function involves two complex numbers, α and β (called quantum amplitudes), which, being complex numbers, have real parts and imaginary parts. Those complex numbers, α and β, each have a certain magnitude, and, according to the rules of quantum mechanics, their squared magnitudes must add up to 1.

Dyakonov continues: In contrast to a classical bit a qubit can be in any of a continuum of possible states, as defined by the values of the quantum amplitudes α and β. This property is often described by the statement that a qubit can exist simultaneously in both of its ↑ and ↓ states. Yes, quantum mechanics often defies intuition.

So while IBM, Google, and other classical computer providers quibble about 50 qubits or 72 or even 500 qubits, to Dyakonov this is ridiculous. The real number of qubits will be astronomical as he explains: Experts estimate that the number of qubits needed for a useful quantum computer, one that could compete with your laptop in solving certain kinds of interesting problems, is between 1,000 and 100,000. So the number of continuous parameters describing the state of such a useful quantum computer at any given moment must be at least 21,000, which is to say about 10300. That’s a very big number indeed; much greater than the number of subatomic particles in the observable universe.

Just in case you missed the math, he repeats: A useful quantum computer [will] need to process a set of continuous parameters that is larger than the number of subatomic particles in the observable universe.

Before you run out to invest in a quantum computer with the most qubits you can buy you would be better served joining IBM’s Q Experience and experimenting with it on IBM’s nickel. Let them wrestle with the issues Dyakonov brings up.

Then, Dyakonov concludes: I believe that such experimental research is beneficial and may lead to a better understanding of complicated quantum systems.  I’m skeptical that these efforts will ever result in a practical quantum computer. Such a computer would have to be able to manipulate—on a microscopic level and with enormous precision—a physical system characterized by an unimaginably huge set of parameters, each of which can take on a continuous range of values. Could we ever learn to control the more than 10300 continuously variable parameters defining the quantum state of such a system? My answer is simple. No, never.

I hope my high school science teacher who enthusiastically introduced me to quantum physics has long since retired or, more likely, passed on. Meanwhile, DancingDinosaur expects to revisit quantum regularly in the coming months or even years.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

IBM Takes Red Hat for $34 Billion

November 2, 2018

“The acquisition of Red Hat is a game-changer. It changes everything about the cloud market,” declared Ginni Rometty, IBM Chairman. At a cost of $34 billion, 10x Red Hat’s gross revenue, it had better be a game changer. See IBM’s announcement earlier this week here.

IBM Multicloud Manager Dashboard

IBM has been hot on the tail of the top three cloud hyperscalers—AWS, Google, and Microsoft/Azure. Will this change the game? Your guess is as good as anyone’s.

The hybrid cloud market appears to be IBM’s primary target. As the company put it: “IBM will become the world’s #1 hybrid cloud provider, offering companies the only open cloud solution that will unlock the full value of the cloud for their businesses.” IBM projects the value of the hybrid cloud market at $1 trillion within a few years!

Most companies today are only 20 percent along their cloud journey, renting compute power to cut costs. The next chapter of the cloud, noted Rometty, requires shifting business applications to hybrid cloud, extracting more data, and optimizing every part of the business.

Nobody has a lock on this market yet. Not IBM, not Red Hat, not VMware, but one thing seems clear; whoever wins will involve open source.  Red Hat, with $3 billion in open source revenue has proven that open source can pay. The only question is how quickly it can pay back IBM’s $34 billion bet.

What’s needed is something that promotes data portability and applications across multiple clouds, data security in a multi-cloud environment, and consistent cloud management. This is the Red Hat and IBM party line.  Both believe they will be well positioned to address these issues to accelerate hybrid multi-cloud adoption. To succeed at this, the new entity will have to tap their leadership in Linux, containers, Kubernetes, multi-cloud management, and automation.

IBM first brought Linux to the Z 20 years ago, making IBM an early advocate of open source, collaborating with Red Hat to help grow enterprise-class Linux.  More recently the two companies worked to bring enterprise Kubernetes and hybrid cloud solutions to the enterprise. These innovations have become core technologies within IBM’s $19 billion hybrid cloud business.

The initial announcement made the point Red Hat will join IBM’s Hybrid Cloud team as a distinct unit, as IBM described, preserving the independence and neutrality of Red Hat’s open source development heritage and commitment, current product portfolio, go-to-market strategy, and unique development culture. Also Red Hat will continue to be led by Jim Whitehurst and Red Hat’s current management team.

That camaraderie lasted until the Q&A following the announcement, when a couple of disagreements arose following different answers on relatively trivial points. Are you surprised? Let’s be clear, nobody spends $34 billion on a $3 billion asset and gives it a completely free hand. You can bet IBM will be calling the shots on everything it is feels is important. Would you do less?

Dharmesh Thakker, a contributor to Forbes, focused more on Red Hat’s OpenShift family of development software. These tools make software developers more productive and are helping transform how software is created and implemented across most enterprises today. So “OpenShift is likely the focus of IBM’s interest in Red Hat” he observes.

A few years ago, he continued, the pendulum seemed to shift from companies deploying more-traditional, on-premises datacenter infrastructure to using public cloud vendors, mostly Amazon. In the last few years, he continued, we’ve seen most mission-critical apps inside companies continue to run on a private cloud but modernized by agile tools and microservices to speed innovation. Private cloud represents 15-20% of datacenter spend, Thakker reports, but the combo of private plus one or more public clouds – hybrid cloud—is here to stay, especially for enterprises. Red Hat’s OpenShift technology enables on-premises, private cloud deployments, giving IBM the ability to play in the hybrid cloud.

IBM isn’t closing this deal until well into 2019; expect to hear more about this in the coming months.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

 

 

 

GAO Blames Z for Government Inefficiency

October 19, 2018

Check out the GAO report from May 2016 here.  The Feds spent more than 75 percent of the total amount budgeted for information technology (IT) for fiscal year 2015 on operations and maintenance (O&M). In a related report, the IRS reported it used assembly language code and COBOL, both developed in the 1950s, for IMF and IDRS. Unfortunately, the GAO conflates the word “mainframe” to refer to outdated UNISYS mainframes with the modern, supported, and actively developed IBM Z mainframes, notes Ross Mauri, IBM general manager, Z systems.

Mainframes-mobile in the cloud courtesy of Compuware

The GAO repeatedly used “mainframe” to refer to outdated UNISYS mainframes alongside the latest advanced IBM Z mainframes.  COBOL, too, maintains active skills and training programs at many institutions and receives investment across many industries. In addition to COBOL, the IBM z14 also runs Java, Swift, Go, Python and other open languages to enable modern application enhancement and development. Does the GAO know that?

The GAO uses the word “mainframe” to refer to outdated UNISYS mainframes as well as modern, supported, and actively developed IBM Z mainframes. In a recent report, the GAO recommends moving to supported modern hardware. IBM agrees. The Z, however, does not expose mainframe investments to a rise in procurement and operating costs, nor to skilled staff issues, Mauri continued.

Three investments the GAO reviewed in the operations and maintenance clearly appear as legacy investments facing significant risks due to their reliance on obsolete programming languages, outdated hardware, and a shortage of staff with critical skills. For example, IRS reported that it used assembly language code and COBOL (both developed in the 1950s) for IMF and IDRS. What are these bureaucrats smoking?

The GAO also seems confused over the Z and the cloud. IBM Cloud Private is designed to run on Linux-based Z systems to take full advantage of the cloud through open containers while retaining the inherent benefits of Z hardware—security, availability,  scalability, reliability; all the ities enterprises have long relied on the z for. The GAO seems unaware that the Z’s automatic pervasive encryption immediately encrypts everything at rest or in transit. Furthermore, the GAO routinely addresses COBOL as a deficiency while ISVs and other signatories of the Open Letter consider it a modern, optimized, and actively supported programming language.

The GAO apparently isn’t even aware of IBM Cloud Private. IBM Cloud Private is compatible with leading IT systems manufacturers and has been optimized for IBM Z. All that you need to get started with the cloud is the starter kit available for IBM OpenPOWER LC (Linux) servers, enterprise Power Systems, and Hyperconverged Systems powered by Nutanix. You don’t even need a Z; just buy a low cost OpenPOWER LC (Linux) server online and configure it as desired.

Here is part of the letter that Compuware sent to the GAO, Federal CIOs, and members of Congress. It’s endorsed by several dozen members of the IT industry. The full letter is here:

In light of a June 2018 GAO report to the Internal Revenue Service suggesting the agency’s mainframe- and COBOL-based systems present significant risks to tax processing, we the mainframe IT community—developers, scholars, influencers and inventors—urge the IRS and other federal agencies to:

  • Reinvest in and modernize the mainframe platform and the mission-critical applications which many have long relied upon.
  • Prudently consider the financial risks and opportunity costs associated with rewriting and replacing proven, highly dependable mainframe applications, for which no “off-the-shelf” replacement exists.
  • Understand the security and performance requirements of these mainframe applications and data and the risk of migrating to platforms that were never designed to meet such requirements.

The Compuware letter goes on to state: In 2018, the mainframe is still the world’s most reliable, performant and securable platform, providing the lowest cost high-transaction system of record. Regarding COBOL it notes that since 2017 IBM z14 supports COBOL V6.2, which is optimized bi-monthly.

Finally, about attracting new COBOL workers: COBOL is as easy to work with it as any other language. In fact, open source Zowe has demonstrated appeal to young techies, providing solutions for development and operations teams to securely manage, control, script, and develop on the mainframe like any other cloud platform. What don’t they get?

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at technologywriter.com.

Can IBM find a place for Watson?

September 7, 2018

After beating 2 human Jeopardy game champions three times in a row in 2011 IBM’s Watson has been hard pressed to come up with a comparable winning streak. Initially IBM appeared to expect its largest customers to buy richly configured Power Servers to run Watson on prem. When they didn’t get enough takers the company moved Watson to the cloud where companies could lease it for major knowledge-driven projects. When that didn’t catch on IBM started to lease Watson’s capabilities by the drink, promising to solve problems in onesies and twosies.

Jeopardy champs lose to Watson

Today Watson is promising to speed AI success through IBM’s Watson Knowledge Catalog. As IBM puts it: IBM Watson Knowledge Catalog powers intelligent, self-service discovery of data, models, and more; activating them for artificial intelligence, machine learning, and deep learning. Access, curate, categorize and share data, knowledge assets, and their relationships, wherever they reside.

DancingDinosaur has no doubt that Watson is stunning technology and has been rooting for its success since that first Jeopardy round seven years ago. Over that time, Watson and IBM have become a case study in how not to price, package, and market powerful yet expensive technology. The Watson Knowledge Catalog is yet another pricing and packaging experiment.

Based on the latest information online, Watson Knowledge Catalog is priced according to number of provisioned catalogs and discovery connections. There are two plans available: Lite and Professional. The Lite plan allows 1 catalog and 5 free discovery connections while the Professional plan provides unlimited of both. Huh? This statement begs for clarification and there probably is a lot of information and fine print required to answer the numerous questions the above description raises, but life is too short for DancingDinosaur to rummage around on the Watson Knowledge Catalog site to look for answers. Doesn’t this seem like something Watson itself should be able to clarify with a single click?

But no, that is too easy. Instead IBM takes the high road, which DancingDinosaur calls the education track.  Notes Jay Limburn, Senior Technical Staff Member and IBM Offering Manager: there are two main challenges that might impede you from realizing the true value of your data and slowing your journey to adopting artificial intelligence (AI). They are 1) inefficient data management and 2) finding the right tools for all data users.

Actually, the issues start even earlier. In attempting AI most established organizations start at a disadvantage, notes IBM. For example:

  • Most enterprises do not know what and where their data is
  • Data science and compliance teams are handicapped by the lack of data accessibility
  • Enterprises with legacy data are even more handicapped than digitally savvy startups
  • AI projects will expose problems with limited data and poor quality; many will simply fail just due to that.
  • The need to differentiate through monetization increases in importance with AI

These are not new. People have been whining about this since the most rudimentary data mining attempts were made decades ago. If there is a surprise it is that they have not been resolved by now.

Or maybe they finally have with the IBM Watson Knowledge Catalog. As IBM puts it, the company will deliver what promises to be the ultimate data Catalog that actually illuminates data:

  • Knows what data your enterprise has
  • Where it resides
  • Where it came from
  • What it means
  • Provide quick access to it
  • Ensure protection of use
  • Exploit Machine Learning for intelligence and automation
  • Enable data scientists, data engineers, stewards and business analysts
  • Embeddable everywhere for free, with premium features available in paid editions

OK, after 7 years Watson may be poised to deliver and it has little to do with Jeopardy but with a rapidly growing data catalog market. According to a Research and Markets report, the data catalog market is expected to grow from $210 million in 2017 to $620 million by 2022. How many sales of the Professional version gets IBM a leading share.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.

Can Zowe Bring Young Developers to the Z

August 31, 2018

Are you ever frustrated by the Z? As powerful as it gets mainframes remain a difficult nut to crack, particularly for newcomers who have grown up with easier technologies. Even Linux on Z is not as simple or straightforward as on other platforms. This poses a problem for Z-based shops that are scrambling to replace retiring mainframers.

IBM – Jon Simon/Feature Photo Service

Shopping via smartphone

Certainly other organizations, mainly mainframe ISVs like Compuware and Syncsort, have succeeded in extending the GUI deeper into the Z but that alone is not enough. It remains too difficult for newcomers to take their newly acquired computer talents and readily apply them to the mainframe. Maybe Zowe can change this.

And here’s how:  Recent surveys show that flexibility, agility and speed are key.  Single platforms are out, multi-platforms, and multi-clouds are in. IBM’s reply: let’s bring things together with the announcement of Zowe, pronounced like joey starting with a z. Zowe represents the first open source framework for z/OS. As such it provides solutions for development and operations teams to securely manage, control, script, and develop on the mainframe like any other cloud platform. Launched with partners CA Technologies and Rocket Software along with the support of the Open Mainframe Project, the goal is to drive innovation for the community of next-generation mainframe developers and enable interoperability and scalability between products. Zowe promotes a faster team on-ramp to mainframe productivity, collaboration, knowledge sharing, and communication.

In short, IBM and partners are enabling users to access z/OS using a new open-source framework. Zowe, more than anything before, brings together generations of systems that were not designed to handle global networks of sensors and devices. Now, decades since IBM brought Linux to the mainframe IBM, CA, and Rocket Software are introducing Zowe, a new open-source software framework that bridges the divide between modern challenges like IoT and the mainframe.

Zowe has four components:

  1. Zowe APIs: z/OS has a set of Representational State Transfer (REST) operating system APIs. These are made available by the z/OS Management Facility (z/OSMF). Zowe uses these REST APIs to submit jobs, work with the Job Entry Subsystem (JES) queue, and manipulate data sets. Zowe Explorers are visual representations of these APIs that are wrapped in the Zowe web UI application. Zowe Explorers create an extensible z/OS framework that provides new z/OS REST services to enterprise tools and DevOps processes.
  2. Zowe API Mediation Layer: This layer has several key components, including that API Gateway built using Netflix Zuul and Spring Boot technology to forward API requests to the appropriate corresponding service through the micro-service endpoint UI and the REST API Catalog. This publishes APIs and their associated documentation in a service catalog. There also is a Discovery Service built on Eureka and Spring Boot technology, acting as the central point in the API Gateway. It accepts announcements of REST services while providing a repository for active services.
  3. Zowe Web UI: Named zLUX, the web UI modernizes and simplifies working on the mainframe and allows the user to create modern applications. This is what will enable non-mainframers to work productively on the mainframe. The UI works with the underlying REST APIs for data, jobs, and subsystems, and presents the information in a full-screen mode compared to the command-line interface.
  4. Zowe Command Line Interface (CLI): Allows users to interact with z/OS from a variety of other platforms, such as cloud or distributed systems, submit jobs, issue Time Sharing Option (TSO) and z/OS console commands, integrate z/OS actions into scripts, and produce responses as JSON documents. With this extensible and scriptable interface, you can tie in mainframes to the latest distributed DevOps pipelines and build in automation.

The point of all this is to enable any developer to manage, control, script, and develop on the mainframe like any other cloud platform. Additionally, Zowe allows teams to use the same familiar, industry-standard, open-source tools they already know to access mainframe resources and services too.

The mainframe may be older than many of the programmers IBM hopes Zowe will attract. But it opens new possibilities for next generation applications and for mainframe shops desperately needing new mission-critical applications for which customers are clamoring. This should radically reduce the learning curve for the next generation while making experienced professionals more efficient. Start your free Zowe trial here. BTW, Zowe’s code will be made available under the open-source Eclipse Public License 2.0.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.

 

Travelport and IBM launch industry AI travel platform

August 24, 2018

Uh oh, if you have been a little sloppy with travel expenses, it’s time to clean up your travel act before AI starts monitoring your reimbursed travel. IBM and Travelport are teaming up to offer the industry’s first AI-based travel platform to intelligently manage corporate travel spend while leveraging IBM Watson capabilities to unlock previously unavailable data insights.

As IBM explains it, the new travel platform will be delivered via the IBM Cloud and exploits IBM Watson capabilities to intelligently track, manage, predict and analyze travel costs to fundamentally change how companies manage and optimize their travel programs. Typically, each work group submits its own travel expenses and reconciliation and reimbursement can be handled by different groups.

With annual global business travel spend estimated to reach a record $1.2 trillion this year, as projected by the Global Business Travel Association, corporate travel managers need new ways to reduce costs. That requires consolidating and normalizing all the information. Currently for businesses to get a full picture of travel patterns a travel manager might have to sift through data silos from travel agencies, cards, expense systems, and suppliers for end-to-end visibility of spend and compliance across all travel subcategories.  This, however, is usually undertaken from an historical view rather than in real time, which is one reason why reimbursement can take so long. As an independent contractor, DancingDinosaur generally has to submit travel expenses at the end of the project and wait forever for payment.

IBM continues: The new platform, dubbed Travel Manager,  features advanced artificial intelligence, and provides cognitive computing and predictive data analytics using what-if type scenarios, while integrated with travel and expense data to help travel management teams, procurement category managers, business units, finance, and human resource departments optimize their travel program, control spend, and enhance the end-traveler experience.  Maybe they will even squeeze independent contractors into the workflow.

The special sauce in all of this results from how IBM combines data with Travelport, a travel commerce platform on its own, to produce IBM Travel Manager as an AI platform that oversees corporate travel expenses. In the process, IBM Travel Manager gives users complete, unified access to previously siloed information, which, when combined with travel data from the Travelport global distribution system (GDS), can then be used to create real-time predictive analytics recommending how, say, adjustments in travel booking behavior patterns can positively impact a company’s travel budget.

Travelport, itself, is a heavyweight in the travel industry. It relies on technology to make the experience of buying and managing travel better. Through its travel commerce platform it provides distribution, technology, payment and other capabilities for the $7 trillion global travel and tourism industry. The platform facilitates travel commerce by connecting the world’s leading travel providers with online and offline travel buyers in a proprietary (B2B) travel marketplace.

The company helps with all aspects of the travel supply chain from airline merchandising, hotel content and distribution, mobile commerce to B2B payments. Last year its platform processed over $83 billion of travel spend, helping its customers maximize the value of every trip.

IBM Travel Manager combines and normalizes data from diverse sources, allowing for more robust insights and benchmarking than other reporting solutions. It also taps AI to unlock previously unavailable insights from multiple internal and external data sources. The product is expected to be commercially available to customers through both IBM and Travelport.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.

IBM AI Reference Architecture Promises a Fast Start

August 10, 2018

Maybe somebody in your organization has already fooled around with a PoC for an AI project. Maybe you already want to build it out and even put it into production. Great! According to IBM:  By 2020, organizations across a wide array of different industries that don’t deploy AI will be in trouble. So those folks already fooling around with an AI PoC will probably be just in time.

To help organization pull the complicated pieces of AI together, IBM, with the help of IDC, put together its AI Infrastrucure Reference Architecture. This AI reference architecture, as IBM explains, is intended to be used by data scientists and IT professionals who are defining, deploying and integrating AI solutions into an organization. It describes an architecture that will support a promising proof of concept (PoC), experimental application, and sustain growth into production as a multitenant system that can continue to scale to serve a larger organization, while integrating into the organization’s existing IT infrastructure. If this sounds like you check it out. The document runs short, less than 30 pages, and free.

In truth, AI, for all the wonderful things you’d like to do with it, is more a system vendor’s dream than yours.  AI applications, and especially deep learning systems, which parse exponentially greater amounts of data, are extremely demanding and require powerful parallel processing capabilities. Standard CPUs, like those populating racks of servers in your data center, cannot sufficiently execute AI tasks. At some point, AI users will have to overhaul their infrastructure to deliver the required performance if they want to achieve their AI dreams and expectations.

Therefore, IDC recommends businesses developing AI capabilities or scaling existing AI capabilities, should plan to deliberately hit this wall in a controlled fashion. Do it knowingly and in full possession of the details to make the next infrastructure move. Also, IDC recommends you do it in close collaboration with a server vendor—guess who wants to be that vendor—who can guide them from early stage to advanced production to full exploitation of AI capabilities throughout the business.

IBM assumes everything is going to AI as quickly as it can, but that may not be the case for you. AI workloads include applications based on machine learning and deep learning, using unstructured data and information as the fuel to drive the next results. Some businesses are well on their way with deploying AI workloads, others are experimenting, and a third group is still evaluating what AI applications can mean for their organization. At all three stages the variables that, if addressed properly, together make up a well-working and business-advancing solution are numerous.

To get a handle on these variables, executives from IT and LOB managers often form a special committee to actively consider their organization’s approach to the AI. Nobody wants to invest in AI for the sake of AI; the vendors will get rich enough as it is. Also, there is no need to reinvent the wheel; many well-defined use cases exist that are applicable across industries. Many already are noted in the AI reference guide.

Here is a sampling:

  • Fraud analysis and investigation (banking, other industries)
  • Regulatory intelligence (multiple industries)
  • Automated threat intelligence and prevention systems (many industries)
  • IT automation, a sure winner (most industries)
  • Sales process recommendation and automation
  • Diagnosis and treatment (healthcare)
  • Quality management investigation and recommendation (manufacturing)
  • Supply and logistics (manufacturing)
  • Asset/fleet management, another sure winner (multiple industries)
  • Freight management (transportation)
  • Expert shopping/buying advisory or guide

Notes IDC: Many can be developed in-house, are available as commercial software, or via SaaS in the cloud.

Whatever you think of AI, you can’t avoid it. AI will penetrate your company embedded in the new products and services you buy.

So where does IBM hope your AI effort end up? Power9 System, hundreds of GPUs, and PowerAI. Are you surprised?

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.

New Syncsort Tools Boost IBMi

July 25, 2018

Earlier this week Syncsort announced new additions to its family of products that can be used to help address top-of-mind compliance challenges faced by IT leaders, especially IBMi shops. Specifically, Syncsort’s IBMi security products can help IBMi shops comply with the EU’s General Data Protection Regulation (GDPR) and strengthen security with multi-factor authentication.

The new innovations in the Syncsort Assure products follow the recent acquisition of IBMi data privacy products from Townsend Security. The Alliance Encryption and Security Suite can be used to address protection of sensitive information and compliance with multi-factor authentication, encryption, tokenization, secure file transfer, and system log collection.

Syncsort’s Cilasoft Compliance and Security Suite for IBMi and Syncsort’s Enforcive Enterprise Security Suite provide unique tools that can help organizations comply with regulatory requirements and address security auditing and control policies. New releases of both security suites deliver technology that can be used to help accelerate and maintain compliance with GDPR.

As the bad guys get more effective, multi-factor authentication is required in many compliance regulations; such as PCI-DSS 3.2, NYDFS Cybersecurity Regulation, Swift Alliance Access, and HIPAA. Multi-factor authentication strengthens login security by requiring something more than a password or passphrase; only granting access after two or more authentication factors have been verified.

To help organizations fulfill regulatory requirements and improve the security of their IBMi systems and applications, Syncsort has delivered the new, RSA-certified Cilasoft Reinforced Authentication Manager for IBMi (RAMi). RAMi’s rules engine facilitates the set-up of multi-factor authentication screens for users or situations that require it, based on specific criteria. RAMi’s authentication features also enable self-service user profile re-enablement and password changes and support of the four eyes principle of supervised changes to sensitive data. Four eyes principle requires that any requested action must be approved by at least two people.

Syncsort expects 30% of its revenue to come from IBMi products. It also plans to integrate its Assure products with Ironstream to offer capacity management for IBMi.

In one sense, Syncsort is joining a handful of vendors, led by IBM, who continue to expand and enhance IBMi. DancingDinosaur has been writing about the IBMi even before it became the AS400, which recently celebrated its 30th birthday this week, writes Timothy Prickett Morgan, a leading analyst at the Next Platform. The predecessors to the AS/400 that your blogger wrote about back then were the System 36 and System 38, but they didn’t survive.  In those 30+ years, however, the IBMi platform has continued to evolve to meet customer needs, most recently by running on Power Systems, where it still remains a viable business, Morgan noted.

The many rivals of the OS/400 platform and its follow-ons since that initial launch of the AS/400 are now gone. You may recall a few of them: DEC’s VMS for the VAX and Alpha systems, Hewlett Packard’s MPE for the HP 3000, HP-UX for the HP 9000s, and Sun Microsystems’ Solaris for the Sparc systems.  DancingDinosaur once tried to cheerlead an effort to port Solaris/Sparc to the mainframe but IBM didn’t buy into that.

Among all of these and other platforms, IBMi is still out there, with probably around 125,000 unique customers and maybe between 250,000 and 300,000 systems. Morgan estimates.

He adds: As much as computing and automation has exploded on the scene since the first AS/400 arrived, one thing continues: Good old fashioned online transaction processing is something that every business still has to do, and even the biggest hyperscalers use traditional applications to keep the books and run the payroll.

The IBMi platform operates as more than an OLTP machine, evolving within the constantly changing environment of modern datacenters. This is a testament, Morgan believes, to the ingenuity and continuing investment by IBM in its Power chips, Power Systems servers, and the IBMi and AIX operating systems. Yes, Linux came along two decades ago and has bolstered the Power platforms, but not to the same extent that Linux bolstered the mainframe. The mainframe had much higher costs and lower priced Linux engines on mainframes exhibited a kind of elasticity of demand that IBM wishes it could get for IBMi and z/OS. Morgan is right about a lot but DancingDinosaur still wishes IBM had backed Solaris/Sparc on the z alongside Linux. Oh well.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.

IBM Introduces a Reference Architecture for On-Premise AI

June 22, 2018

This week IBM announced an AI infrastructure Reference Architecture for on-premises AI deployments. The architecture promises to address the challenges organizations face experimenting with AI PoCs, growing into multi-tenant production systems, and then expanding to enterprise scale while integrating into an organization’s existing IT infrastructure.

The reference architecture includes, according to IBM, a set of integrated software tools built on optimized, accelerated hardware for the purpose of enabling organizations to jump start. AI and Deep Learning projects, speed time to model accuracy, and provide enterprise-grade security, interoperability, and support.  IBM’s graphic above should give you the general picture.

Specifically, IBM’s AI reference architecture should support iterative, multi-stage, data-driven processes or workflows that entail specialized knowledge, skills, and, usually, a new compute and storage infrastructure. Still, these projects have many attributes that are familiar to traditional CIOs and IT departments.

The first of these is that the results are only as good as the data going into it, and model development is dependent upon having a lot of data and the data being in the format expected by the deep learning framework. Surprised? You have been hearing this for decades as GIGO (Garbage In Garbage Out).  The AI process also is iterative; repeatedly looping through data sets and tunings to develop more accurate models and then comparing new data in the model to the original business or technical requirements to refine the approach.  In this sense, AI reference model is no different than IT 101, an intro course for wannabe IT folks.

But AI doesn’t stay simplistic for long. As the reference architecture puts it, AI is a sophisticated, complex process that requires specialized software and infrastructure. That’s where IBM’s PowerAI Platform comes in. Most organizations start with small pilot projects bound to a few systems and data sets but grow from there.

As projects grow beyond the first test systems, however, it is time to bulk up an appropriate storage and networking infrastructure. This will allow it to sustain growth and eventually support a larger organization.

The trickiest part of AI and the part that takes inspired genius to conceive, test, and train is the model. The accuracy and quality of a trained AI model are directly affected by the quality and quantity of data used for training. The data scientist needs to understand the problem they are trying to solve and then find the data needed to build a model that solves the problem.

Data for AI is separated into a few broad sets; the data used to train and test the models and data that is analyzed by the models and the archived data that may be reused. This data can come from many different sources such as traditional organizational data from ERP systems, databases, data lakes, sensors, collaborators and partners, public data, mobile apps, social media, and legacy data. It may be structured or unstructured in many formats such as file, block, object, Hadoop Distributed File Systems (HDFS), or something else.

Many AI projects begin as a big data problem. Regardless of how it starts, a large volume of data is needed, and it inevitably needs preparation, transformation, and manipulation. But it doesn’t stop there.

AI models require the training data to be in a specific format; each model has its own and usually different format. Invariably the initial data is nowhere near those formats. Preparing the data is often one of the largest organizational challenges, not only in complexity but also in the amount of time it takes to transform the data into a format that can be analyzed. Many data scientists, notes IBM, claim that over 80% of their time is spent in this phase and only 20% on the actual process of data science. Data transformation and preparation is typically a highly manual, serial set of steps: identifying and connecting to data sources, extracting to a staging server, tagging the data, using tools and scripts to manipulate the data. Hadoop is often a significant source of this raw data, and Spark typically provides the analytics and transformation engines used along with advanced AI data matching and traditional SQL scripts.

There are two other considerations in this phase: 1) data storage and access and the speed of execution. For this—don’t be shocked—IBM recommends Spectrum Scale to provide multi-protocol support with a native HDFS connector, which can centralize and analyze data in place rather than wasting time copying and moving data. But you may have your preferred platform.

IBM’s reference architecture provides a place to start. A skilled IT group will eventually tweak IBM’s reference architecture, making it their own.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.

IBM Expands and Enhances its Cloud Offerings

June 15, 2018

IBM announced 18 new availability zones in North America, Europe, and Asia Pacific to bolster its IBM Cloud business and try to keep pace with AWS, the public cloud leader, and Microsoft. The new availability zones are located in Europe (Germany and UK), Asia-Pacific (Tokyo and Sydney), and North America (Washington, DC and Dallas).

IBM cloud availability zone, Dallas

In addition, organizations will be able to deploy multi-zone Kubernetes clusters across the availability zones via the IBM Cloud Kubernetes Service. This will simplify how they deploy and manage containerized applications and add further consistency to their cloud experience. Furthermore, deploying multi-zone clusters will have minimal impact on performance, about 2 ms latency between availability zones.

An availability zone, according to IBM, is an isolated instance of a cloud inside a data center region. Each zone brings independent power, cooling, and networking to strengthen fault tolerance. While IBM Cloud already operates in nearly 60 locations, the new zones add even more capacity and capability in these key centers. This global cloud footprint becomes especially critical as clients look to gain greater control of their data in the face of tightening data regulations, such as the European Union’s new General Data Protection Regulation (GDPR). See DancingDinosaur June 1, IBM preps z world for GDPR.

In its Q1 earnings IBM reported cloud revenue of $17.7bn over the past year, up 22 percent over the previous year, but that includes two quarters of outstanding Z revenue that is unlikely to be sustained,  at least until the next Z comes out, which is at least a few quarters away.  AWS meanwhile reported quarterly revenues up 49 percent to $5.4 billion, while Microsoft recently reported 93 percent growth for Azure revenues.

That leaves IBM trying to catch up the old fashioned way by adding new cloud capabilities, enhancing existing cloud capabilities, and attracting more clients to its cloud capabilities however they may be delivered. For example, IBM announced it is the first cloud provider to let developers run managed Kubernetes containers directly on bare metal servers with direct access to GPUs to improve the performance of machine-learning applications, which is critical to any AI effort.  Along the same lines, IBM will extend its IBM Cloud Private and IBM Cloud Private for Data and middleware to Red Hat’s OpenShift Container Platform and Certified Containers. Red Hat already is a leading provider of enterprise Linux to Z shops.

IBM has also expanded its cloud offerings to support the widest range of platforms. Not just Z, LinuxONE, and Power9 for Watson, but also x86 and a variety of non-IBM architectures and platforms. Similarly, notes IBM, users have gotten accustomed to accessing corporate databases wherever they reside, but proximity to cloud data centers still remains important. Distance to data centers can have an impact on network performance, resulting in slow uploads or downloads.

Contrary to simplifying things, the propagation of more and different types of clouds and cloud strategies complicate an organization’s cloud approach. Already, today companies are managing complex, hybrid public-private cloud environments. At the same time, eighty percent of the world’s data is sitting on private servers. It just is not practical or even permissible in some cases to move all the data to the public cloud. Other organizations are run very traditional workloads that they’re looking to modernize over time as they acquire new cloud-native skills. The new IBM cloud centers can host data in multiple formats and databases including DB2, SQLBase, PostreSQL, or NoSQL, all exposed as cloud services, if desired.

The IBM cloud centers, the company continues, also promise common logging and services between the on-prem environment and IBM’s public cloud environment. In fact, IBM will make all its cloud services, including the Watson AI service, consistent across all its availability zones, and offer multi-cluster support, in effect enabling the ability to run workloads and do backups across availability zones.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Follow DancingDinosaur on Twitter, @mainframeblog. See more of his work at technologywriter.com and here.


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