IBM Eager to Move Blockchain to Next Level

Blockchain, as IBM sees it, has moved past the days of proof of concept and is now focused on platform industrialization and best practices. As for industries, financial systems remain its primary target although areas like digital identity, provenance, and asset management are gaining interest fast. To that end the company is offering the IBM Blockchain Platform on a free trial basis. The Platform promises to make any developer a blockchain developer fast. According to IBM, hundreds of companies on dozens of blockchain networks are solving business challenges across varied industries, not just financial services.

Although IBM has been a blockchain pioneer, especially in the enterprise segment, which is getting more crowded.  Specifically, the Enterprise Ethereum Alliance (EEA), which lists over 500 members, recently made available to the public the release of its Enterprise Ethereum Architecture Stack. According to EEA, the stack defines the building blocks needed to drive the Web 3.0 era of decentralized, connective intelligence that will work anywhere and is capable of facilitating smart contracts without intermediaries. The stack, available as a free public document to download here, incorporates components developed by the EEA.

IBM is not saying much about EEA, preferring instead to focus on Hyperledger. This much, however, an IBMer was willing to say: Our main POV is that Hyperledger is the only blockchain platform designed for enterprises from the ground up.  Ethereum is better suited for crypto currencies and ICOs.  But we know they are working to move into the enterprise space. Interestingly, of the 500 companies listed as EEA members, including many of IBM’s consulting and vendor peers but not IBM.

The IBMer continued: we have had many clients start with Ethereum and discover that it does not meet their business needs … and then they move to Hyperledger, which as a permissioned blockchain network does meet enterprise business needs. Permissioned means you have to be approved to join the network, presumably making it more secure.

IBM clearly has locked onto the idea of a permissioned blockchain network as important, at least among enterprise customers. The IBMer continued: we state that you can have a public permissioned blockchain network—and you’ll start to see more of these around payments.

The IBMer noted: it’s unclear what Ethereum’s permissioning method is; what their governance model is.  There is lack of agreement around new types of consensus.

In case you have missed the blockchain buzz of the past few years blockchain is a shared, immutable ledger for recording the history of transactions. Immutable means that once a transaction has been accepted by the other nodes on the blockchain it cannot be removed or deleted. It sits there as a permanent record for the life of the blockchain. Of course, corrections can be made but they are added as another immutable record. As such, depending on the number of nodes in the blockchain, you can end up with a long chain of immutable records, sort of an auditor’s dream or nightmare.

A business blockchain, such as IBM Blockchain and the Linux Foundation’s Hyperledger Project, provides a permissioned network with known identities. And unlike Bitcoin, there is no need for cryptocurrency exchange. Where any payments are required, you make them as you would in any transaction.  Except with blockchain the transaction exists forever as an immutable record. From initial order to delivery acceptance to payment it all exists as a permanent record. Warning: sleazy business partners should avoid blockchain transactions since you won’t be able to weasel out so easily.

To make blockchain easy, IBM is developing what it describes as enterprise-ready solutions. For example, to address the provenance of diamonds and avoid buying what are called blood diamonds, IBM has backed TrustChain, which tracks six styles of diamond and gold engagement rings on a blockchain network, enabling dealers to know who handled the diamond from the mine to processor to the buyer’s finger.

Similarly, IBM’s Food Trust blockchain includes Dole, Driscoll’s, Golden State Foods, Kroger, McCormick and Company, McLane Company, Nestle, Tyson Foods, Unilever, and Walmart to address food safety. So the next time you eat something and get a stomach ache, just turn to Food Trust to track down the culprit.

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

High Cost of Ignoring Z’s Pervasive Encryption

That cost was spelled out at IBM’s Think this past spring.  Writes David Bruce, who leads IBM’s strategies for security on IBM Z and LinuxONE, data breaches are expensive, costing $3.6 million on average. And hoping to avoid one by doing business as usual is a bad bet. Bruce reports breaches are increasingly likely: an organization has a 28 percent chance of being breached in the next 24 months. You can find Bruce’s comments on security and pervasive encryption here.

9 million data records were compromised in 2015

Were any of those 9 million records from your organization? Did you end up on the front page of the newspaper? To stay out of the data breach headlines, organizations require security solutions that protect enterprise and customer data at minimal cost and effort, Bruce observes.

Encryption is the preferred solution, but it is costly, cumbersome, labor-intensive, and hit-or-miss. It is hit-or-miss because the overhead involved forces organizations to choose what to encrypt and what to skip. You have to painstakingly classify the data in terms of risk, which takes time and only adds to the costs. Outside of critical revenue transactions or key intellectual property—no brainers—you will invariably choose wrong and miss something you will regret when it shows up on the front page of the New York Times.

Adding to the cost is the compliance runaround. Auditors are scheduled to visit or maybe they aren’t even scheduled and just drop in; you now have to drop whatever your staff was hoping to do and gather the necessary documentation to prove your data is safe and secure.  Do you really need this? Life is too short as it is.

You really want to put an end to the entire security compliance runaround and all the headaches it entails. But more than that, you want protected, secure data; all data, all the time.  When someone from a ransomware operation calls asking for hundreds or thousands of dollars to get your data back you can laugh and hang up the phone. That’s what Bruce means when he talks about pervasive encryption. All your data is safely encrypted with its keys protected from the moment it is created until the moment it is destroyed by you. And you don’t have to lift a finger; the Z does it all.

That embarrassing news item about a data breach; it won’t happen to you either. Most importantly of all, customers will never see it and get upset.

In fact, at Think, Forrester discussed today’s customer-obsessed approach that leading organizations are adopting to spur growth. To obsess over customers, explained Bruce, means to take great care in protecting the customer’s sensitive data, which provides the cornerstone of a customer-obsessed Forrester zero trust security framework. The framework includes, among other security elements, encryption of all data across the enterprise. Enabling the Z’s built in pervasive encryption and automatic key protection you can ignore the rest of Forrester’s framework.

Pervasive encryption, unique to Z, addresses the security challenges while helping you thrive in this age of the customer. At Think, Michael Jordan, IBM Distinguished Engineer for IBM Z Security, detailed how pervasive encryption represents a paradigm shift in security, reported Bruce. Previously, selective field-level encryption was the only feasible way to secure data, but it was time-, cost-, and resource-intensive – and it left large portions of data unsecured.

Pervasive encryption, however, offers a solution capable of encrypting data in bulk, making it possible and practical to encrypt all data associated with an application, database, and cloud service – whether on premises or in the cloud, at-rest or in-flight. This approach also simplifies compliance by eliminating the need to demonstrate compliance at the field level. Multiple layers of encryption – from disk and tape up through applications – provide the strongest possible defense against security breaches. The high levels of security enabled by pervasive encryption help you promote customer confidence by protecting their data and privacy.

If you have a Z and have not enabled pervasive encryption, you are putting your customers and your organization at risk. Am curious, please drop me a note why.

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

 

Is Your Enterprise Ready for AI?

According to IBM’s gospel of AI “we are in the midst of a global transformation and it is touching every aspect of our world, our lives, and our businesses.”  IBM has been preaching its gospel of AI of the past year or longer, but most of its clients haven’t jumped fully aboard. “For most of our clients, AI will be a journey. This is demonstrated by the fact that most organizations are still in the early phases of AI adoption.”

AC922 with NIVIDIA Tesla V100 and Enhanced NVLink GPUs

The company’s latest announcements earlier this week focus POWER9 squarely on AI. Said Tim Burke, Engineering Vice President, Cloud and Operating System Infrastructure, at Red Hat. “POWER9-based servers, running Red Hat’s leading open technologies offer a more stable and performance optimized foundation for machine learning and AI frameworks, which is required for production deployments… including PowerAI, IBM’s software platform for deep learning with IBM Power Systems that includes popular frameworks like Tensorflow and Caffe, as the first commercially supported AI software offering for [the Red Hat] platform.”

IBM insists this is not just about POWER9 and they may have a point; GPUs and other assist processors are taking on more importance as companies try to emulate the hyperscalers in their efforts to drive server efficiency while boosting power in the wake of declines in Moore’s Law. ”GPUs are at the foundation of major advances in AI and deep learning around the world,” said Paresh Kharya, group product marketing manager of Accelerated Computing at NVIDIA. [Through] “the tight integration of IBM POWER9 processors and NVIDIA V100 GPUs made possible by NVIDIA NVLink, enterprises can experience incredible increases in performance for compute- intensive workloads.”

To create an AI-optimized infrastructure, IBM announced the latest additions to its POWER9 lineup, the IBM Power Systems LC922 and LC921. Characterized by IBM as balanced servers offering both compute capabilities and up to 120 terabytes of data storage and NVMe for rapid access to vast amounts of data. IBM included HDD in the announcement but any serious AI workload will choke without ample SSD.

Specifically, these new servers bring an updated version of the AC922 server, which now features recently announced 32GB NVIDIA V100 GPUs and larger system memory, which enables bigger deep learning models to improve the accuracy of AI workloads.

IBM has characterized the new models as data-intensive machines and AI-intensive systems, LC922 and LC921 Servers with POWER9 processors. The AC922, arrived last fall. It was designed for the what IBM calls the post-CPU era. The AC922 was the first to embed PCI-Express 4.0, next-generation NVIDIA NVLink, and OpenCAPI—3 interface accelerators—which together can accelerate data movement 9.5x faster than PCIe 3.0 based x86 systems. The AC922 was designed to drive demonstrable performance improvements across popular AI frameworks such as TensorFlow and Caffe.

In the post CPU era, where Moore’s Law no longer rules, you need to pay as much attention to the GPU and other assist processors as the CPU itself, maybe even more so. For example, the coherence and high-speed of the NVLink enables hash tables—critical for fast analytics—on GPUs. As IBM noted at the introduction of the new machines this week: Hash tables are fundamental data structure for analytics over large datasets. For this you need large memory: small GPU memory limits hash table size and analytic performance. The CPU-GPU NVLink2 solves 2 key problems: large memory and high-speed enables storing the full hash table in CPU memory and transferring pieces to GPU for fast operations; coherence enables new inserts in CPU memory to get updated in GPU memory. Otherwise, modifications on data in CPU memory do not get updated in GPU memory.

IBM has started referring to the LC922 and LC921 as big data crushers. The LC921 brings 2 POWER9 sockets in a 1U form factor; for I/O it comes with both PCIe 4.0 and CAPI 2.0.; and offers up to 40 cores (160 threads) and 2TB RAM, which is ideal for environments requiring dense computing.

The LC922 is considerably bigger. It offers balanced compute capabilities delivered with the P9 processor and up to 120TB of storage capacity, again advanced I/O through PCIe 4.0/CAPI 2.0, and up to 44 cores (176 threads) and 2TB RAM. The list price, notes IBM is ~30% less.

If your organization is not thinking about AI your organization is probably in the minority, according to IDC.

• 31 percent of organizations are in [AI] discovery/evaluation
• 22 percent of organizations plan to implement AI in next 1-2 years
• 22 percent of organizations are running AI trials
• 4 percent of organizations have already deployed AI

Underpinning both servers is the IBM POWER9 CPU. The POWER9 enjoys a nearly 5.6x improved CPU to GPU bandwidth vs x86, which can improve deep learning training times by nearly 4x. Even today companies are struggling to cobble together the different pieces and make them work. IBM learned that lesson and now offers a unified AI infrastructure in PowerAI and Power9 that you can use today.

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

Compuware Tackles Mainframe Workforce Attrition and Batch Processing

While IBM works furiously to deliver quantum computing and expand AI and blockchain into just about everything, many DancingDinosaur readers are still wrestling with the traditional headaches and boosting quality and efficiency or mainframe operations and optimizing the most traditional mainframe activities there are, batch processes. Would be nice if quantum computing could handle multiple batch operations simultaneously but that’s not high on IBM’s list of quantum priorities.

So Compuware is stepping up as it has been doing quarterly by delivering new systems to expedite and facilitate conventional mainframe processes.  Its zAdviser promises actionable analytic insight to continuously improve quality, velocity and efficiency on the mainframe. While Compuware’s ThruPut Manager enables next-gen ITstaff to optimize mainframe batch execution through new visually intuitive workload scheduling.

zAdviser captures data about developers’ behaviors

zAdviser uses machine learning to continuously measure and improve an organization’s mainframe DevOps processes and development outcomes. Based on key performance indicators (KPIs), zAdviser measures application quality, as well as development speed and the efficiency of a development team. The result: managers can now make evidence-based decisions in support of their continuous improvement efforts.

The new tool leverages a set of analytic models that uncover correlations between mainframe developer behaviors and mainframe DevOps KPIs. These correlations represent the best available empirical evidence regarding the impact of process, training and tooling decisions on digital business outcomes. Compuware is offering zAdviser free to customers on current maintenance.

zAdviser leverages a set of analytic models that uncover correlations between mainframe developer behaviors and mainframe DevOps KPIs. These correlations represent the best available empirical evidence regarding the impact of process, training and tooling decisions on digital business outcomes.

Long mainframe software backlogs are no longer acceptable. Improvements in mainframe DevOps has become an urgent imperative for large enterprises that find themselves even more dependent on mainframe applications—not less. According to a recent Forrester Consulting study commissioned by Compuware, 57 percent of enterprises with a mainframe run more than half of their business-critical workloads on the mainframe. That percentage is expected to increase to 64 percent by 2019, while at the same time enterprises are failing to replace the expert mainframe workforce they have lost by attrition. Hence the need for modern, automated, intelligent tools to speed the learning curve for workers groomed on Python or Node.js.

Meanwhile, IBM hasn’t exactly been twiddling its thumbs in regard to DevOps analytics for the Z. Its zAware delivers a self-contained firmware IT analytics offering that helps systems and operations professionals rapidly identify problematic messages and unusual system behavior in near real time, which systems administrators can use to take corrective actions.

ThruPut Manager brings a new web interface that offers  visually intuitive insight for the mainframe staff, especially new staff, into how batch jobs are being initiated and executed—as well as the impact of those jobs on mainframe software licensing costs.

By implementing ThruPut Manager, Compuware explains, enterprises can better safeguard the performance of both batch and non-batch applications while avoiding the significant adverse economic impact of preventable spikes in utilization as measured by Rolling 4-Hour Averages (R4HA). Reducing the R4HA is a key way data centers can contain mainframe costs.

More importantly,  with the new ThruPut Manager, enterprises can successfully transfer batch management responsibilities to the next generation of IT staff with far less hands-on platform experience—without exposing themselves to related risks such as missed batch execution deadlines, missed SLAs, and excess costs.

With these new releases, Compuware is providing a way to reduce the mainframe software backlog—the long growing complaint that mainframe shops cannot deliver new requested functionality fast enough—while it offers a way to replace the attrition among aging mainframe staff with young staff who don’t have years of mainframe experience to fall back on. And if the new tools lower some mainframe costs however modestly in the process, no one but IBM will complain.

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 Grows Quantum Ecosystem

It is good that you aren’t dying to deploy quantum computing soon because IBM readily admits that it is not ready for enterprise production now or in several weeks or maybe several months. IBM, however, continues to assemble the building blocks you will eventually need when you finally feel the urge to deploy a quantum application that can address a real problem that you need to resolve.

cryostat with prototype of quantum processor

IBM is surprisingly frank about the state of quantum today. There is nothing you can do at this point that you can’t simulate on a conventional or classical computer system. This situation is unlikely to change anytime soon either. For years to come, we can expect hybrid quantum and conventional compute environments that will somehow work together to solve very demanding problems, although most aren’t sure exactly what those problems will be when the time comes. Still at Think earlier this year IBM predicted quantum computing will be mainstream in 5 years.

Of course, IBM has some ideas of where the likely problems to solve will be found:

• Chemistry—material design, oil and gas, drug discovery
• Artificial Intelligence—classification, machine learning, linear algebra
• Financial Services—portfolio optimization, scenario analysis, pricing

It has been some time since the computer systems industry had to build a radically different kind of compute discipline from scratch. Following the model of the current IT discipline IBM began by launching the IBM Q Network, a collaboration with leading Fortune 500 companies and research institutions with a shared mission. This will form the foundation of a quantum ecosystem.  The Q Network will be comprised of hubs, which are regional centers of quantum computing R&D and ecosystem; partners, who are pioneers of quantum computing in a specific industry or academic field; and most recently, startups, which are expected to rapidly advance early applications.

The most important of these to drive growth of quantum are the startups. To date, IBM reports eight startups and it is on the make for more. Early startups include QC Ware, Q-Ctrl, Cambridge Quantum Computing (UK), which is working on a compiler for quantum computing, 1Qbit based in Canada, Zapata Computing located at Harvard, Strangeworks, an Austin-based tool developer, QxBranch, which is trying to apply classical computing techniques to quantum, and Quantum Benchmark.

Startups get membership in the Q network and can run experiments and algorithms on IBM quantum computers via cloud-based access; provide deeper access to APIs and advanced quantum software tools, libraries, and applications; and have the opportunity to collaborate with IBM researchers and technical SMEs on potential applications, as well as with other IBM Q Network organizations. If it hasn’t become obvious yet, the payoff will come from developing applications that solve recognizable problems. Also check out QISKit, a software development kit for quantum applications available through GitHub.

The last problem to solve is the question around acquiring quantum talent. How many quantum scientists, engineers, or programmers do you have? Do you even know where to find them? The young people excited about computing today are primarily interested in technologies to build sexy apps using Node.js, Python, Jupyter, and such.

To find the people you need to build quantum computing systems you will need to scour the proverbial halls of MIT, Caltech, and other top schools that produce physicists and quantum scientists. A scan of salaries for these people reveals $135,000- $160,000, if they are available at all.

The best guidance from IBM on starting is to start small. The industry is still at the building block stage; not ready to throw specific application at real problems. In that case sign up for IBM’s Q Network and get some of your people engaged in the opportunities to get educated in quantum.

When DancingDinosaur first heard about quantum physics he was in a high school science class decades ago. It was intriguing but he never expected to even be alive to see quantum physics becoming real, but now it is. And he’s still here. Not quite ready to sign up for QISKit and take a small qubit machine for a spin in the cloud, but who knows…

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.

IT Security Enters the Cooperative Era

Ever hear of the cybersecurity tech accord?  It was  announced on Tuesday. Microsoft, Facebook, and 32 other companies signed aboard.  Absent from the signing were Apple, Alphabet and Amazon. Also missing was IBM. Actually, IBM was already at the RSA Conference making its own security announcement of an effort to help cybersecurity teams collaborate just like the attackers they’re defending against do via the dark web by sharing information among themselves.

IBM security control center

Tuesday’s Cybersecurity Tech Accord amounted to a promise to work together on cybersecurity issues. Specifically, the companies promise to work against state sponsored cyberattacks. The companies also agreed to collaborate on stronger defense systems and protect against the tampering of their products, according to published reports.

Giving importance to the accord is the financial impact of cybersecurity attacks on businesses and organizations, which is projected to reach $8 trillion by 2022. Other technology leaders, including Cisco, HP, Nokia, Oracle also joined the accord.

A few highly visible and costly attacks were enough to galvanize the IT leaders. In May, WannaCry ransomware targeted more than 300,000 computers in 150 countries, including 48 UK medical facilities. In a bid to help, Microsoft issued patches for old Windows systems, even though it no longer supports them, because so many firms run old software that was vulnerable to the attack, according to published reports. The White House attributed the attack to North Korea.

In June, NotPetya ransomware, which initially targeted computers in Ukraine before spreading, infected computers, locked down their hard drives, and demanded a $300 ransom to be paid in bitcoin. Even victims that paid weren’t able to recover their files, according to reports. The British government said Russia was behind the global cyberattack.

The Cybersecurity Tech Accord is modeled after a digital Geneva Convention, with a long-term goal of updating international law to protect people in times of peace from malicious cyberattacks, according to Microsoft president Brad Smith.

Github’s chief strategy officer Julio Avalos wrote in a separate blog post that “protecting the Internet is becoming more urgent every day as more fundamental vulnerabilities in infrastructure are discovered—and in some cases used by government organizations for cyberattacks that threaten to make the Internet a theater of war.” He continued: “Reaching industry-wide agreement on security principles and collaborating with global technology companies is a crucial step toward securing our future.”

Added Sridhar Muppidi, Co-CTO of IBM Security about the company’s efforts to help cybersecurity teams collaborate like the attackers they’re working against, in a recent published interview: The good guys have to collaborate with each other so that we can provide a better and more secure and robust systems. So we talk about how we share the good intelligence. We also talk about sharing good practices, so that we can then build more robust systems, which are a lot more secure.

It’s the same concept of open source model, where you provide some level of intellectual capital with an opportunity to bring in a bigger community together so that we can take the problem and solve it better and faster. And learn from each other’s mistakes and each other’s advancement so that it can help, individually, each of our offerings. So, end of the day, for a topic like AI, the algorithm is going to be an algorithm. It’s the data, it’s the models, it’s the set of things which go around it which make it very robust and reliable, Muppidi continued.

IBM appears to be practicing what it preaches by facilitating the collaboration of people and machines in defense of cyberspace. Last year at RSA, IBM introduced Watson to the cybersecurity industry to augment the skills of analysts in their security investigations. This year investments and artificial intelligence (AI), according to IBM, were made with a larger vision in mind: a move toward “automation of response” in cybersecurity.

At RSA, IBM also announced the next-generation IBM Resilient Incident Response Platform (IRP) with Intelligent Orchestration. The new platform promises to accelerate and sharpen incident response by seamlessly combining incident case management, orchestration, automation, AI, and deep two-way partner integrations into a single platform.

Maybe DancingDinosaur, which has spent decades acting as an IT-organization-of-one, can finally turn over some of the security chores to an intelligent system, which hopefully will do it better and faster.

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 Skinny Z Systems

Early this week IBM unveiled two miniaturized mainframe models, dubbed skinny mainframes, it said are easier to deploy in a public or private cloud facility than their more traditional, much bulkier predecessors. Relying on all their design tricks, IBM engineers managed to pack each machine into a standard 19-inch rack with space to spare, which can be used for additional components.

Z14 LinuxONE Rockhopper II, 19-inch rack

The first new mainframe introduced this week, also in a 19-inch rack, is the Z14 model ZR1. You can expect subsequent models to increment the model numbering.  The second new machine is the LinuxONE Rockhopper II, also in a 19-inch rack.

In the past, about a year after IBM introduced a new mainframe, say the z10, it was introduced what it called a Business Class (BC) version. The BC machines were less richly configured, less expandable but delivered comparable performance with lower capacity and a distinctly lower price.

In a Q&A analyst session IBM insisted the new machines would be priced noticeably lower, as were the BC-class machines of the past. These are not comparable to the old BC machines. Instead, they are intended to attract a new group of users who face new challenges. As such, they come cloud-ready. The 19-inch industry standard, single-frame design is intended for easy placement into existing cloud data centers alongside other components and private cloud environments.

The company, said Ross Mauri, General Manager IBM Z, is targeting the new machines toward clients seeking robust security with pervasive encryption, cloud capabilities and powerful analytics through machine learning. Not only, he continued, does this increase security and capability in on-premises and hybrid cloud environments for clients, IBM will also deploy the new systems in IBM public cloud data centers as the company focuses on enhancing security and performance for increasingly intensive data loads.

In terms of security, the new machines will be hard to beat. IBM reports the new machines capable of processing over 850 million fully encrypted transactions a day on a single system. Along the same lines, the new mainframes do not require special space, cooling or energy. They do, however, still provide IBM’s pervasive encryption and Secure Service Container technology, which secures data serving at a massive scale.

Ross continued: The new IBM Z and IBM LinuxONE offerings also bring significant increases in capacity, performance, memory and cache across nearly all aspects of the system. A complete system redesign delivers this capacity growth in 40 percent less space and is standardized to be deployed in any data center. The z14 ZR1 can be the foundation for an IBM Cloud Private solution, creating a data-center-in-a-box by co-locating storage, networking and other elements in the same physical frame as the mainframe server.  This is where you can utilize that extra space, which was included in the 19-inch rack.

The LinuxONE Rockhopper II can also accommodate a Docker-certified infrastructure for Docker EE with integrated management and scale tested up to 330,000 Docker containers –allowing developers to build high-performance applications and embrace a micro-services architecture.

The 19-inch rack, however, comes with tradeoffs, notes Timothy Green writing in The Motley Fool. Yes, it takes up 40% less floor space than the full-size Z14, but accommodates only 30 processor cores, far below the 170 cores supported by a full size Z14, , which fills a 24-inch rack. Both new systems can handle around 850 million fully encrypted transactions per day, a fraction of the Z14’s full capacity. But not every company needs the full performance and capacity of the traditional mainframe. For companies that don’t need the full power of a Z14 mainframe, notes Green, or that have previously balked at the high price or massive footprint of full mainframe systems, these smaller mainframes may be just what it takes to bring them to the Z. Now IBM needs to come through with the advantageous pricing they insisted they would offer.

The new skinny mainframe are just the latest in IBM’s continuing efforts to keep the mainframe relevant. It began over a decade ago with porting Linux to the mainframe. It continued with Hadoop, blockchain, and containers. Machine learning and deep learning are coming right along.  The only question for DancingDinosaur is when IBM engineers will figure out how to put quantum computing on the Z and squeeze it into customers’ public or private cloud environments.

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 Shouldn’t Forget Its Server Platforms

The word coming out of IBM brings a steady patter about cognitive, Watson, and quantum computing, for which IBM predicted quantum would be going mainstream within five years. Most DancingDinosaur readers aren’t worrying about what’s coming in 2023 although maybe they should. They have data centers to run now and are wondering where they are going to get the system horsepower they will need to deliver IoT or Blockchain or any number of business initiatives clamoring for system resources today or tomorrow and all they’ve got are the z14 and the latest LinuxONE. As powerful as they were when first announced, do you think that will be enough tomorrow?

IBM’s latest server, the Z

Timothy Prickett Morgan, analyst at The Next Platform, apparently isn’t so sure. He writes in a recent piece how Google and the other hyperscalers need to add serious power to today’s server options. The solution involves “putting systems based on IBM’s Power9 processor into production.” This shouldn’t take anybody by surprise; almost as soon as IBM set up the Open Power consortium Rackspace, Google, and a handful of others started making noises about using Open POWER for a new type of data center server. The most recent announcements around Power9, covered here back in Feb., promise some new options with even more coming.

Writes Morgan: “Google now has seven applications that have more than 1 billion users – adding Android, Maps, Chrome, and Play to the mix – and as the company told us years ago, it is looking for any compute, storage, and networking edge that will allow it to beat Moore’s Law.” Notice that this isn’t about using POWER9 to drive down Intel’s server prices; Google faces a more important nemesis, the constraints of Moore’s Law.

Google has not been secretive about this, at least not recently. To its credit Google is making its frustrations known at appropriate industry events:  “With a technology trend slowdown and growing demand and changing demand, we have a pretty challenging situation, what we call a supply-demand gap, which means the supply on the technology side is not keeping up with this phenomenal demand growth,” explained Maire Mahony, systems hardware engineer at Google and its key representative at the OpenPower Foundation that is steering the Power ecosystem. “That makes it hard to for us to balance that curve we call performance per TCO dollar. This problem is not unique to Google. This is an industry-wide problem.” True, but the majority of data centers, even the biggest ones, don’t face looming multi-billion user performance and scalability demands.

Morgan continued: “Google has absolutely no choice but to look for every edge. The benefits of homogeneity, which have been paramount for the first decade of hyperscaling, no longer outweigh the need to have hardware that better supports the software companies like Google use in production.”

This isn’t Intel’s problem alone although it introduced a new generation of systems, dubbed Skylake, to address some of these concerns. As Morgan noted recently, “various ARM chips –especially ThunderX2 from Cavium and Centriq 2400 from Qualcomm –can boost non-X86 numbers.” So can AMD’s Epyc X86 processors. Similarly, the Open Power consortium offers an alternative in POWER9.

Morgan went on: IBM differentiated the hardware with its NVLink versions and, depending on the workload and the competition, with its most aggressive pricing and a leaner and cheaper microcode and hypervisor stack reserved for the Linux workloads that the company is chasing. IBM very much wants to sell its Power-Linux combo against Intel’s Xeon-Linux and also keep AMD’s Epyc-Linux at bay. Still, it is not apparent to Morgan how POWER9 will compete.

Success may come down to a battle of vendor ecosystems. As Morgan points out: aside from the POWER9 system that Google co-engineered with Rackspace Hosting, the most important contributions that Google has made to the OpenPower effort is to work with IBM to create the OPAL firmware, the OpenKVM hypervisor, and the OpenBMC baseboard management controller, which are all crafted to support little endian Linux, as is common on x86.

Guess this is the time wade into the endian morass. Endian refers to the byte ordering that is used, and IBM chips and a few others do them in reverse of the x86 and Arm architectures. The Power8 chip and its POWER9 follow-on support either mode, big or little endian. By making all of these changes, IBM has made the Power platform more palatable to the hyperscalers, which is why Google, Tencent, Alibaba, Uber, and PayPal all talk about how they make use of Power machinery, particularly to accelerate machine learning and generic back-end workloads. But as quickly as IBM jumped on the problem recently after letting it linger for years, it remains one more complication that must be considered. Keep that in mind when a hyperscaler like Google talks about performance per TCO dollar.

Where is all this going? Your guess is as good as any. The hyperscalers and the consortia eventually should resolve this and DancingDinosaur will keep watching. Stay tuned.

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.

Mainframe ISVs Advance the Mainframe While IBM Focuses on Think

Last week IBM reveled in the attention of upwards of 30,000 visitors to its Think conference, reportedly a record for an IBM conference. Meanwhile Syncsort and Compuware stayed home pushing new mainframe initiatives. Specifically, Syncsort introduced innovations to deliver mainframe log and application data in real-time directly to Elastic for deeper next generation analytics through like Splunk, Hadoop and the Elastic Stack.

Syncsort Ironstone for next-gen analytics

Compuware reported that the percentage of organizations running at least half their business-critical applications on the mainframe expected to increase next year, although the loss of skilled mainframe staff, and the failure to subsequently fill those positions pose significant threats to application quality, velocity and efficiency. Compuware has been taking the lead in modernizing the mainframe developer experience to make it compatible with the familiar x86 experience.

According to David Hodgson, Syncsort’s chief product officer, many organizations are using Elastic’s Kibana to visualize Elasticsearch data and navigate the Elastic Stack. These organizations, like others, are turning to tools like Hadoop and Splunk to get a 360-degree view of their mainframe data enterprise-wide. “In keeping with our proven track record of enabling our customers to quickly extract value from their critical data anytime, anywhere, we are empowering enterprises to make better decisions by making mission-critical mainframe data available in another popular analytics platform,” he adds.

For cost management, Syncsort now offers Ironstream with the flexibility of MSU-based (capacity) or Ingestion-based pricing.

Compuware took a more global view of the mainframe. The mainframe, the company notes, is becoming more important to large enterprises as the percentage of organizations running at least half their business-critical applications on that platform expected to increase next year. However, the loss of skilled mainframe staff, and the failure to subsequently fill those positions, pose significant threats to application quality, velocity and efficiency.

These are among the findings of research and analysis conducted by Forrester Consulting on behalf of Compuware.  According to the study, “As mainframe workload increases—driven by modern analytics, blockchain and more mobile activity hitting the platform—customer-obsessed companies should seek to modernize application delivery and remove roadblocks to innovation.”

The survey of mainframe decision-makers and developers in the US and Europe also revealed the growing mainframe importance–64 percent of enterprises will run more than half of their critical applications on the platform within the next year, up from 57 percent this year. And just to ratchet up the pressure a few notches, 72 percent of customer-facing applications at these enterprises are completely or very reliant on mainframe processing.

That means the loss of essential mainframe staff hurts, putting critical business processes at risk. Overall, enterprises reported losing an average of 23 percent of specialized mainframe staff in the last five years while 63 percent of those positions have not been filled.

There is more to the study, but these findings alone suggest that mainframe investments, culture, and management practices need to evolve fast in light of the changing market realities. As Forrester puts it: “IT decision makers cannot afford to treat their mainframe applications as static environments bound by long release cycles, nor can they fail to respond to their critical dependence with a retiring workforce. Instead, firms must implement the modern tools necessary to accelerate not only the quality, but the speed and efficiency of their mainframe, as well as draw [new] people to work on the platform.”

Nobody has 10 years or even three years to cultivate a new mainframer. You need to attract and cultivate talented x86 or ARM people now, equip each—him or her—with the sexiest, most efficient tools, and get them working on the most urgent items at the top of your backlog.

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 Boosts AI at Think

Enterprise system vendors are racing to AI along with all the others. Writes Jeffrey Burt, an analyst at The Next Platform, “There continues to be an ongoing push among tech vendors to bring artificial intelligence (AI) and its various components – including deep learning and machine learning – to the enterprise. The technologies are being rapidly adopted by hyperscalers and in the HPC space, and enterprises stand to reap significant benefits by also embracing them.” Exactly what those benefits are still need to be specifically articulated and, if possible, quantified.

IBM Think Conference this week

For enterprise data centers running the Z or Power Systems, the most obvious quick payoff will be fast, deeper, more insightful data analytics along with more targeted guidance on actions to take in response. After that there still remains the possibility of more automation of operations but the Z already is pretty thoroughly automated and optimized. Just give it your operational and performance parameters and it will handle the rest.  In addition, vendors like Compuware and Syncsort have been making the mainframe more graphical and intuitive. The days of needing deep mainframe experience or expertise have passed. Even x86 admins can quickly pick up a modern mainframe today.

In a late 2016 study by Accenture that modeled the impact of AI for 12 developed economies. The research compared the size of each country’s economy in 2035 in a baseline scenario, which shows expected economic growth under current assumptions and an AI scenario reflecting expected growth once the impact of AI has been absorbed into the economy. AI was found to yield the highest economic benefits for the United States, increasing its annual growth rate from 2.6 percent to 4.6 percent by 2035, translating to an additional USD $8.3 trillion in gross value added (GVA). In the United Kingdom, AI could add an additional USD $814 billion to the economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9 percent. Japan has the potential to more than triple its annual rate of GVA growth by 2035, and Finland, Sweden, the Netherlands, Germany and Austria could see their growth rates double. You can still find the study here.

Also coming out of Think this week was the announcement of an expanded Apple-IBM partnership around AI and machine learning (ML). The resulting AI service is intended for corporate developers to build apps themselves. The new service, Watson Services for Core ML, links Apple’s Core ML tools for developers that it unveiled last year with IBM’s Watson data crunching service. Core ML helps coders build machine learning-powered apps that more efficiently perform calculations on smartphones instead of processing those calculations in external data centers. It’s similar to other smartphone-based machine learning tools like Google’s TensorFlow Lite.

The goal is to help enterprises reimagine the way they work through a combination of Core ML and Watson Services to stimulate the next generation of intelligent mobile enterprise apps. Take the example of field technicians who inspect power lines or machinery. The new AI field app could feed images of electrical equipment to Watson to train it to recognize the machinery. The result would enable field technicians to scan the electrical equipment they are inspecting on their iPhones or iPads and automatically detect any anomalies. The app would eliminate the need to send that data to IBM’s cloud computing data centers for processing, thus reducing the amount of time it takes to detect equipment issues to near real-time.

Apple’s Core ML toolkit could already be used to connect with competing cloud-based machine learning services from Google, Amazon, and Microsoft to create developer tools that more easily link the Core ML service with Watson. For example, Coca-Cola already is testing Watson Services for Core ML to see if it helps its field technicians better inspect vending machines. If you want try it in your shop, the service will be free to developers to use now. Eventually, developers will have to pay.

Such new roll-your-own AI services represent a shift for IBM. Previously you had to work with IBM consulting teams. Now the new Watson developer services are intended to be bought in an “accessible and bite size” way, according to IBM, and sold in a “pay as you go” model without consultants.  In a related announcement at Think, IBM announced it is contributing the core of Watson Studio’s Deep Learning Service as an open source project called Fabric for Deep Learning. This will enable developers and data scientists to work together on furthering the democratization of deep learning.

Ultimately, the democratization of AI is the only way to go. When intelligent systems speak together and share insights everyone’s work will be faster, smarter. Yes, there will need to be ways to compensate distinctively valuable contributions but with over two decades of open source experience, the industry should be able to pretty easily figure that out.

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.