IBM Continues Cranking Up Blockchain

 

 

Somehow between quantum computing, AI, and hybrid clouds IBM is managing to squeeze in blockchain as an active, growing business. For instance, a previously unnamed collaborative effort between the world’s largest shipping company, Maersk, and IBM has now grown to 92 participants and been dubbed TradeLens.

IBM has 92 participants in the TradeLens blockchain network

DancingDinosaur long considered blockchain as a natural for the Z due to its zero downtime reliability and high certified levels of security (EAL4+). The most recent models include IBM’s automated pervasive encryption. No more wasting time making decisions about what to encrypt. The Z just encrypts it all with minimal overhead penalty. Your applications and workloads won’t even notice and compliance audits become a breeze.

TradeLens is emerging from its beta to accept early-adopter applications and announced a new custom contract service for executing complex shipping orders with fewer middlemen. “We have seen a lot of skeptics talk about the validity of blockchain solutions,” said Marie Wieck, IBM general manager and head of blockchain. “And I think with over 90 organizations and more than 150 million events captured on the system, “we really are seeing the proof,” she adds.

The initiative now includes Germany-based Hamburg Sud, which Maersk bought last year for $4 billion, and U.S.-based Pacific International Lines, along with numerous customs authorities, cargo owners and freight forwarders. Collectively, the shipping companies account for more than 20% of the global supply chain market share, with 20 port and terminal operators in Singapore, the U.S., Holland, and more serving 235 marine gateways around the world.

TradeLens, in practice, gives users access to their own blockchain node similar to those on the bitcoin blockchain that lets users send money without the need of banks. In the case of TradeLens a shipper can cut out as many as five middlemen, even for simple queries such as identifying the location of a shipping container.

At stake is what Transparency Market Research expects will be a $32.9 billion global supply-chain software business by 2026. As far back as 2015, the World Trade Organization estimated that simplifying the global supply chain could reduce costs among users by as much as 17.5%, with developing nations expected to see as much as a 35% increase in exports as they leapfrog over legacy technology platforms.

The cooperative effort between Maersk and IBM still needs to make money. To do so, the two companies have shifted the business model from a stand-alone joint-venture to the intellectual property that comprises TradeLens being co-owned and jointly developed.

But the new cooperative structure could unnerve some potential customers. To offset concerns, the CEO of Maersk’s New Jersey-based TradeLens operation, Mike White, says a number of barriers have been put in place, including contractual restrictions on sharing data and technical barriers in the form of the independently managed blockchain nodes.

If successful, TradeLens might literally embody the common refrain among blockchain users that “all ships will rise” when they use a shared, distributed ledger. Facing decreasing global freight rates, Maersk last quarter became just the latest container shipper to cut profit forecasts.

Among competitors aiming to cut those costs and increase profits is the former head of blockchain at accounting firm Deloitte, who earlier this year announced he was raising $100 million to launch a supply chain platform using the ethereum blockchain. Similarly, blockchain startup Fr8 is preparing to raise $60 million via an initial coin offering to build its own blockchain logistics platform.

“The value proposition is for all ecosystem participants,” said White. “The ability to get better access to more real-time data, to have better visibility end-to-end, and to be able to connect one-to-many in a more efficient and effective way, makes the cost of getting that information lower, makes the ability to manage your own business better, and makes the ability to service your customers that much stronger.”

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

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.

Compuware Acquisition Boosts Mainframe DevOps

The acquisition of XaTester, new enhancements, and a partnership with Parasoft moves Compuware Topaz for Total Test toward leadership in the automated unit testing that has become essential for Agile and DevOps on the mainframe.  Compuware clearly has picked up its steady but languid quarterly pace of delivering new mainframe software. This comes on top of Topaz for Enterprise Data announced just a few weeks ago here.

Especially for mainframe shops, automated mainframe unit testing may present the biggest obstacle to speedy new code delivery.  The testing must not just be automated but continuous. As such, it serves as the centerpiece of the entire agile downstream process, which also includes continuous integration and continuous delivery. Only by delivering continuous automated testing can the mainframe shop deliver the no-fail quality of service for which it is heralded. Continuous automated testing is essential for controlling business risk, especially given the increased complexity and pace of modern application delivery.

To put it another way: building and integrating code changes is certainly important. However, if the automated delivery process cannot identify how changes impact business risk or disrupt the end-user experience continuous automated testing then increased frequency and speed of continuous Integration and continuous delivery becomes more of a problem than an advantage.

To deliver on its vision of Topaz for Total Test as the defacto standard for automating mainframe unit testing across all major mainframe environments and programming languages, Compuware has:

• Acquired XaTester from Xact Consulting A/S, enabling developers to quickly create unit tests for both batch and CICS-based programs written in COBOL, PL/I and Assembler

• Enhanced Topaz for Total Test to provide automated unit testing for IMS batch and transactional applications. Testing for IMS is especially important given that newer developers often have little or no hands-on experience with IMS code. This presents a challenge since more than 95 percent of the top Fortune 1000 companies use IMS to process more than 50 billion transactions a day and manage 15 million gigabytes of critical business data. Fortunately, IBM continues to add new features to IMS that help adjust to the changing IT world. These enhancements complement Topaz for Total Test’s existing support for batch applications written in COBOL.

• Partnered with Parasoft, a leading innovator in end-to-end test automation for software development. The first deliverable from the partnership is integration between Parasoft SOAtest and Topaz for Total Test. This integration enables developers working on mainframe applications to quickly and easily test API calls between mainframe and non-mainframe systems, an increasingly critical aspect of DevOps.

Topaz for Total Test transforms mainframe development by giving developers the same type of unit testing capabilities on the mainframe that distributed platform teams have become accustomed to on other platforms. Unit testing enables developers to find potential problems in their code as early as possible to more quickly and frequently deliver incremental changes in software functionality while more granularly documenting code for the benefit of other developers.

DevOps, also presents complications for the mainframe that come from its reputation for slow, painstaking, methodical release cycles. DevOps is about making sure the way an application is deployed in production is the same way it was deployed in test and development.

According to IBM writing in piece titled DevOps for the mainframe, notes DevOps also includes the notion of applying software management to the scripts and processes used for the actual deployment and monitoring and taking the monitoring capabilities from Operations into development and test to get an early understanding of how the system will actually perform.

As the IBM writers continue: In the z/OS environment, organizations are generally building only the changes, the deltas, to the application and deploying them into the environment.  It is very common to find that some parts of an application have not been rebuilt in decades. Worse yet, there are generally few z/OS test environments that are shared across application development teams.  The tools also are rarely the same tools used by the distributed teams.  These differences increase the difficultly of achieving an-end-to-end DevOps process.

This is where Compuware comes in. Topaz for Total Test fundamentally transforms mainframe development by giving developers the same type of unit testing capabilities on the mainframe they’ve become accustomed to on other platforms, mainly x86.

The result for large enterprises, Compuware continues, is a unified DevOps toolchain that accelerates development across all platforms so a multi-platform shop can more effectively compete in today’s rapidly-changing markets. “The new rules of the digital economy are putting pressure on our customers to achieve the utmost speed with the utmost quality,” said Luke Tuddenham, Vice President at CPT, a global IT consulting services firm with a significant testing practice. The new Topaz tools should The acquisition of XaTester, new enhancements, and a partnership with Parasoft moves Compuware Topaz for Total Test toward leadership in the automated unit testing that has become essential for Agile and DevOps on the mainframe. .

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

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.

FlashSystem 9100 Includes NVMe and Spectrum Software

The new IBM FlashSystem 9100 comes with all the bells and whistles included, especially NVMe and Spectrum Software.  For software, IBM includes its full suite of software-defined capabilities for your data both on-premises and in the cloud and across public and private clouds. It also aims to modernize your infrastructure with new capabilities for private and hybrid clouds as well as optimize operations.

FlashSystem 9100 with new capabilities built-in end-to-end

It also includes AI-assisted, next-generation technology for multi-cloud environments. This should allow you to optimize business critical workloads in an effort to optimize your technology infrastructure and prepare for the era of multi-cloud digitized business now emerging.

The IT infrastructure market is changing so quickly and so radically that technology that might have been still under consideration can no longer make it to the short list. DancingDinosuar, for example, won’t even attempt to create an ROI analysis of hard disk for primary storage. Other than straight-out falsification the numbers couldn’t work.

The driver behind this, besides the advances in technology price/performance and what seems like return to Moore’s Law levels of gains, lies the success of the big hyperscalers, who are able to sustain amazing price and performance levels. DancingDinosaur readers are no hyperscalers but they are capitalizing on hyperscaler gains in the cloud and they can emulate hyperscaler strategies in their data centers wherever possible.

IBM puts it a little more conventionally: As more and more organizations move on to a multi-cloud strategy they are having more data-driven needs such as artificial intelligence (AI), machine learning (ML), and containers, it writes. All of these new needs require a storage solution that is powerful enough to address all the needs while being built on proven technology and support both the existing and evolving data centers. IBM’s response to these issues is the expansion of its FlashSystem to include the new 9100 NVMe end-to-end solution while piling on the software.

Aside from being an all NVMe storage solution, IBM is leveraging several IBM technologies such as IBM Spectrum Virtualize and IBM FlashCore as well as software from IBM’s Spectrum family. This combination of software and technology helps the 9100 store up to 2PB of data in a 2U space (32PB in a larger rack). FlashCore also enables consistent microsecond latency, with IBM quoting performance of 2.5 million IOPS, 34GB/s, and 100μs latency for a single 2U array. For storage, the FlashSystem 9100 uses FlashCore modules with an NVMe interface. These 2.5” drives come in 4.8TB, 9.6TB, and 19.2TB capacities with up to 5:1 compression. The drives leverage 64-Layer 3D TLC NAND and can be configured with as little as four drives per system.   You might not be a hyperscaler but this is the kind of stuff you need if you hope to emulate one.

To do this, IBM packs in the goodies. For starters it is NVMe-accelerated and Multi-Cloud Enabled.  And it goes beyond the usual flash array. This is an NVMe-accelerated Enterprise Flash Array – 100% NVMe end-to-end and includes NVMe IBM FlashCore modules and NVMe industry standard SSD. It also supports physical, virtual and Docker environments.

In addition, the system includes IBM Storage Insights for AI-empowered predictive analytics, storage resource management, and support delivered over the cloud. Also, it offers Spectrum Storage Software for array management, data reuse, modern data protection, disaster recovery, and containerization (how it handles Docker). Plus, IBM adds:

• IBM Spectrum Virtualize
• IBM Spectrum Copy Data Management
• IBM Spectrum Protect Plus
• IBM Spectrum Virtualize for Public Cloud
• IBM Spectrum Connect
• FlashSystem 9100 Multi-Cloud Solutions

And just in case you think you are getting ahead of yourself, IBM is adding what it calls blueprints. As IBM explains them: the blueprints take the form of three pre-validated, cloud-focused solution plans.

1. Data Reuse, Protection and Efficiency solution leverages the capabilities of IBM Spectrum Protect Plus and IBM Spectrum Copy Data Management (CDM) to provide enhanced data protection features for virtual applications with powerful data copy management and reuse functionality both on premises and in the cloud.
2. Business Continuity and Data Reuse solution leverages IBM Spectrum Virtualize for Public Cloud to extend data protection and disaster recovery capabilities into the IBM Cloud, as well as all the copy management and data reuse features of IBM Spectrum CDM.
3. Private Cloud Flexibility and Data Protection solution enables simplified deployment of private clouds, including the technology needed to implement container environments, and all of the capabilities of IBM Spectrum CDM to manage copy sprawl and provide data protection for containerized applications.

The blueprints may be little more than an IBM shopping list that leaves you as confused as before and a little poorer. Still, the FlashSystem 9100, along with all of IBM’s storage solutions, comes with Storage Insights, the company’s enterprise, AI-based predictive analytics, storage resource management, and support platform delivered over the cloud. If you try any blueprint, let me know how it works, anonymously of course.

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.

 

Compuware Expedites DevOps on Z

Compuware continues its quarterly introduction of new capabilities for the mainframe, a process that has been going on for several years by now. The latest advance, Topaz for Enterprise Data, promises to expedite the way DevOps teams can access the data they need while reducing the complexity, labor, and risk through extraction, masking, and visualization of the mainframe. The result: the ability to leverage all available data sources to deliver high-value apps and analytics fast.

Topaz for Enterprise Data expedites data access for DevOps

The days when mainframe shops could take a methodical and deliberate approach—painstakingly slow—to accessing enterprise data have long passed. Your DevOps teams need to dig the value out of that data and put it into the hands of managers and LOB teams fast, in hours, maybe just minutes so they can jump on even the most fleeting opportunities.

Fast, streamlined access to high-value data has become an urgent concern as businesses seek competitive advantages in a digital economy while fulfilling increasingly stringent compliance requirements. Topaz for Enterprise Data enables developers, QA staff, operations teams, and data scientists at all skill and experience levels to ensure they have immediate, secure access to the data they need, when they need it, in any format required.

It starts with data masking, which in just the last few months has become a critical concern with the rollout of GDPR across the EU. GDPR grants considerable protections and options to the people whose data your systems have been collecting. Now you need to protect personally identifiable information (PII) and comply with regulatory mandates like GDPR and whatever similar regs will come here.

Regs like these don’t apply just to your primary transaction data. You need data masking with all your data, especially when large, diverse datasets of high business value residing on the mainframe contain sensitive business or personal information.

This isn’t going to go away anytime soon so large enterprises must start transferring responsibility for the stewardship of this data to the next generation of DevOps folks who will be stuck with it. You can bet somebody will surely step forward and say “you have to change every instance of my data that contains this or that.” Even the most expensive lawyers will not be able to blunt such requests. Better to have the tools in place to respond to this quickly and easily.

The newest tool, according to Compuware, is Topaz for Enterprise Data. It will enable even a mainframe- inexperienced DevOps team to:

• Readily understand relationships between data even when they lack direct familiarity with specific data types or applications, to ensure data integrity and resulting code quality.
• Quickly generate data for testing, training, or business analytics purposes that properly and accurately represents actual production data.
• Ensure that any sensitive business or personal data extracted from production is properly masked for privacy and compliance purposes, while preserving essential data relationships and characteristics.
• Convert file types as required.

Topaz users can access all these capabilities from within Topaz’s familiar Eclipse development environment, eliminating the need to learn yet another new and complicated tool.

Those who experience it apparently like what they find. Noted Lynn Farley, Manager of Data Management at TCF Bank: “Testing with production-like obfuscated data helps us develop and deliver better quality applications, as well as remain compliant with data privacy requirements, and Topaz provides our developers with a way to implement data privacy rules to mask multiple data types across platforms and with consistent results.”

Rich Ptak, principal of IT analyst firm Ptak Associates similarly observed: “Leveraging a modern interface for fast, simple access to data for testing and other purposes is critical to digital agility,” adding it “resolves the long-standing challenge of rapidly getting value from the reams of data in disparate sources and formats that are critical to DevOps and continuous improvement.”

“The wealth of data that should give large enterprises a major competitive advantage in the digital economy often instead becomes a hindrance due to the complexity of sourcing across platforms, databases, and formats,” said Chris O’Malley,Comp CEO of Compuware. As DancingDinosaur sees it, by removing such obstacles Compuware reduces the friction between enterprise data and business advantage.

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.

Hybrid Cloud to Streamline IBM Z

2020 is the year, according to IDC,  when combined IT infrastructure spending on private and public clouds will eclipse spending on traditional data centers. The researcher predicts the public cloud will account for 31.68 percent of IT infrastructure spending in 2020, while private clouds will take a 19.82 percent slice of the spending pie, totaling more than half (51.5 percent) of all infrastructure spending for the first time, with the rest going to traditional data centers.

Source: courtesy of IBM

There is no going back. By 2021 IDC expects the balance to continue tilting further toward the cloud, with combined public and private cloud dollars making up 53.15 percent of infrastructure spending. Enterprise spending on cloud, according to IDC, will grow over $530 billion as over 90 percent of enterprises will be using a mix of multiple cloud services and platforms, both on and off premises.

Technology customers want choices. They want to choose their access device, interface, deployment options, cost and even their speed of change. Luckily, today’s hybrid age enables choices. Hybrid clouds and multi-cloud IT offer the most efficient way of delivering the widest range of customer choices.

For Z shops, this shouldn’t come as a complete surprise. IBM has been preaching the hybrid gospel for years, at least since x86 machines began making significant inroads into its platform business. The basic message has always been the same: Center the core of your business on the mainframe and then build around it— using x86 if you must but now try LinuxONE and hybrid clouds, both public and on-premises.

For many organizations a multi-cloud strategy using two or more different clouds, public or on-premise, offers the fastest and most efficient way of delivering the maximum in choice, regardless of your particular strategy. For example one might prefer a compute cloud while the other a storage cloud. Or, an organization might use different clouds—a cloud for finance, another for R&D, and yet another for DevOps.

The reasoning behind a multi-cloud strategy can also vary. Reasons can range from risk mitigation, to the need for specialized functionality, to cost management, analytics, security, flexible access, and more.

Another reason for a hybrid cloud strategy, which should resonate with DancingDinosaur readers, is modernizing legacy systems. According to Gartner, by 2020, every dollar invested in digital business innovation will require enterprises to spend at least three times that to continuously modernize the legacy application portfolio. In the past, such legacy application portfolios have often been viewed as a problem subjected to large-scale rip-and-replace efforts in desperate, often unsuccessful attempts to salvage them.

With the growth of hybrid clouds, data center managers instead can manage their legacy portfolio as an asset by mixing and matching capabilities from various cloud offerings to execute business-driven modernization. This will typically include microservices, containers, and APIs to leverage maximum value from the legacy apps, which will no longer be an albatross but a valuable asset.

While the advent of multi-clouds or hybrid clouds may appear to complicate an already muddled situation, they actually provide more options and choices as organizations seek the best solution for their needs at their price and terms.

With the Z this may be easier done than it initially sounds. “Companies have lots of records on Z, and the way to get to these records is through APIs, particularly REST APIs,” explains Juliet Candee, IBM Systems Business Continuity Architecture. Start with the IBM Z Hybrid Cloud Architecture. Then, begin assembling catalogs of APIs and leverage z/OS Connect to access popular IBM middleware like CICS. By using z/OS Connect and APIs through microservices, you can break monolithic systems into smaller, more composable and flexible pieces that contain business functions.

Don’t forget LinuxONE, another Z but optimized for Linux and available at a lower cost. With the LinuxONE Rockhopper II, the latest slimmed down model, you can run 240 concurrent MongoDB databases executing a total of 58 billion database transactions per day on a single server. Accelerate delivery of your new applications through containers and cloud-native development tools, with up to 330,000 Docker containers on a single Rockhopper II server. Similarly, lower TCO and achieve a faster ROI with up to 65 percent cost savings over x86. And the new Rockhopper II’s industry-standard 19-inch rack uses 40 percent less space than the previous Rockhopper while delivering up to 60 percent more Linux capacity.

This results in what Candee describes as a new style of building IT that involves much smaller components, which are easier to monitor and debug. Then, connect it all to IBM Cloud on Z using secure Linux containers. This could be a hybrid cloud combining IBM Cloud Private and an assortment of public clouds along with secure zLinux containers as desired.

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.

Please note: DancingDinosaur will be away for the first 2 weeks of July. The next piece should appear the week of July 16 unless the weather is unusually bad.

IBM Introduces a Reference Architecture for On-Premise AI

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

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.

IBM Continues Quantum Push

IBM continued building out its Q Network ecosystem in May with the announcement of North Carolina State University, which is the first university-based IBM Q Hub in North America. As a hub. NC State will focus on accelerating industry collaborations, learning, skills development, and the implementation of quantum computing.

Scientists inside an open dilution fridge

NC State will work directly with IBM to advance quantum computing and industry collaborations, as part of the IBM Q Network’s growing quantum computing ecosystem. The school is the latest Q Network member. The network consists of individuals and organizations, including scientists, engineers, and business leaders, along with forward thinking companies, academic institutions, and national research labs enabled by IBM Q. Its mission: advancing quantum computing and launching the first commercial applications.

This past Nov. IBM announced a 50 qubit system. Shortly after Google announced Bristlecone, which claims to top that. With Bristlecone Google topped IBM for now with 72 qubits. However, that may not be the most important metric to focus on.

Stability rather than the number of qubits should be the most important metric. The big challenge today revolves around the instability of qubits. To maintain qubit machines stable enough the systems need to keep their processors extremely cold (Kelvin levels of cold) and protect them from external shocks. This is not something you want to build into a laptop or even a desktop. Instability leads to inaccuracy, which defeats the whole purpose.  Even accidental sounds can cause the computer to make mistakes. For minimally acceptable error rates, quantum systems need to have an error rate of less than 0.5 percent for every two qubits. To drop the error rate for any qubit processor, engineers must figure out how software, control electronics, and the processor itself can work alongside one another without causing errors.

50 cubits currently is considered the minimum number for serious business work. IBM’s November announcement, however, was quick to point out that “does not mean quantum computing is ready for common use.” The system IBM developed remains extremely finicky and challenging to use, as are those being built by others. In its 50-qubit system, the quantum state is preserved for 90 microseconds—record length for the industry but still an extremely short period of time.

Nonetheless, 50 qubits have emerged as the minimum number for a (relatively) stable system to perform practical quantum computing. According to IBM, a 50-qubit machine can do things that are extremely difficult to even simulate with the fastest conventional system.

Today, IBM offers the public IBM Q Experience, which provides access to 5- and 16-qubit systems; and the open quantum software development kit, QISKit, maybe the first quantum SDK. To date, more than 80,000 users of the IBM Q Experience, have run more than 4 million experiments and generated more than 65 third-party research articles.

Still, don’t expect to pop a couple of quantum systems into your data center. For the immediate future, the way to access and run qubit systems is through the cloud. IBM has put qubit systems in the cloud, where they are available to participants in its Q Network and Q Experience.

IBM has also put some of its conventional systems, like the Z, in the cloud. This raises some interesting possibilities. If IBM has both quantum and conventional systems in the cloud, can the results of one be accessed or somehow shared with the other. Hmm, DancingDinosaur posed that question to IBM managers earlier this week at a meeting in North Carolina (NC State, are you listening?).

The IBMers acknowledged the possibility although in what form and what timeframe wasn’t even at the point of being discussed. Quantum is a topic DancingDinosaur expects to revisit regularly in the coming months or even years. 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.