Posts Tagged ‘mainframe’

IBM Resurrects Moore’s Law

June 23, 2017

Guess Moore’s Law ain’t as dead as we were led to believe. On Jun 5 IBM and Research Alliance partners GLOBALFOUNDRIES and Samsung, along with equipment suppliers announced the development of an industry-first process to build silicon nano sheet transistors that will enable 5nm chips. Previously, IBM announced a 7nm process using a silicon germanium (SiGe) alloy.

As DancingDinosaur wrote in early Oct. 2015, the last z System that conformed to the expectations of Moore’s Law was the zEC12, introduced Aug 2012. IBM could boast then it had the fastest commercial processor available.  The subsequent z13 didn’t match it in processor speed.  The z13 chip runs a 22 nm core at 5 GHz, one-half a GHz slower than the zEC12, which ran its 32nm core at 5.5 GHz. IBM compensated for the slower chip speed by adding more processors throughout the system to boost I/O and other functions and optimizing the box every way possible.

5nm silicon nano-sheet transistors delivers 40% performance gain

By 2015, the z13 delivered about a 10 percent performance bump per core thanks to the latest tweaks in the core design, such as better branch prediction and better pipelining. But even at one-half Ghz slower, the z13 was the first system to process 2.5 billion transactions a day.  Even more importantly for enterprise data centers, z13 transactions are persistent, protected, and auditable from end-to-end, adding assurance as mobile transactions grow to an estimated 40 trillion mobile transactions per day by 2025. The z13 also received and continues to receive praise for its industry leading security ratings as well as its scalability and flexibility.

Just recently Hitachi announced a partnership with IBM to develop a version of the z13 to run its own operating system, VOS3. The resulting z13 will run the next generation of Hitachi’s AP series.

But IBM isn’t back in pursuit of Moore’s Law just to deliver faster traditional mainframe workloads. Rather, the company is being driven by its strategic initiatives, mainly cognitive computing. As IBM explained in the announcement: The resulting increase in performance will help accelerate cognitive computing, the Internet of Things (IoT), and other data-intensive applications delivered in the cloud. The power savings could also mean that the batteries in smartphones and other mobile products could last two to three times longer than today’s devices, before needing to be charged.

Scientists working as part of the IBM-led Research Alliance at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering’s NanoTech Complex in Albany, NY achieved the breakthrough by using stacks of silicon nanosheets as the device structure of the transistor instead of the standard FinFET architecture, which is the blueprint for the semiconductor industry up through 7nm node technology. “For business and society to meet the demands of cognitive and cloud computing in the coming years, advancement in semiconductor technology is essential,” said Arvind Krishna, senior vice president, Hybrid Cloud, and director, IBM Research in the announcement. “That’s why IBM aggressively pursues new and different architectures and materials that push the limits of this industry, and brings them to market in technologies like mainframes and our cognitive systems.”

Compared to the leading edge 10nm technology available in the market, according to IBM, a nanosheet-based 5nm technology can deliver 40 percent performance enhancement at fixed power, or 75 percent power savings at matched performance. This improvement enables a significant boost to meeting the future demands of artificial intelligence (AI) systems, virtual reality, and mobile devices.

These may not sound like the workloads you are running on your mainframe now, but systems with these chips are not going to be shipped in the next mainframe either. So, you have a couple of years. The IBM team expects to make progress toward commercializing 7nm in 2018. By the time they start shipping 5nm systems you might be desperate for a machine to run such workloads and others like them.

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.

 

Syncsort Drives zSystem and Distributed Data Integration

June 8, 2017

IBM appears to be so busy pursuing its strategic imperatives—security, blockchain, quantum computing, and cognitive computing—that it seems to have forgotten the daily activities that make up the bread-and-butter of mainframe data centers. Stepping up to fill the gap have been mainframe ISVs like Compuware, Syncsort, Data Kinetics, and a few others.

IBM’s Project DataWorks taps into unstructured data often missed

IBM hasn’t completely ignored this need. For instance, Project DataWorks uses Watson Analytics and natural language processing to analyze and create complex visualizations. Syncsort, on the other hand, latched onto open Apache technologies, starting in the fall of 2015. Back then it introduced a set of tools to facilitate data integration through Apache Kafka and Apache Spark, two of the most active Big Data open source projects for handling real-time, large-scale data processing, feeds, and analytics.

Syncsort’s primary integration vehicle then revolved around the Intelligent Execution capabilities of its DMX data integration product suite with Apache Spark. Intelligent Execution allows users to visually design data transformations once and then run them anywhere – across Hadoop, MapReduce, Spark, Linux, Windows, or Unix, both on premise or in the cloud.

Since then Syncsort, in March, announced another big data integration solution. This time its DMX-h, is now integrated with Cloudera Director, enabling organizations to easily deploy DMX-h along with Cloudera Enterprise on Amazon Web Services, Microsoft Azure, or Google Cloud. By deploying DMX-h with CDH, Syncsort explained, organizations can quickly pull data into new, ready-to-work clusters in the cloud. This accelerates how quickly they can take advantage of big data cloud benefits, including cost savings and Data-as-a-Service (DaaS) delivery.

A month before that, this past February, Syncsort introduced new enhancements in its Big Data integration solution by again deploying DMX-h to deliver integrated workflow capabilities and Spark 2.0 integration, which simplifies Hadoop and Spark application development, effectively enabling mainframe data centers to extract maximum value from their data assets.

In addition, Syncsort brought new integrated workflow capabilities and Spark 2.0 integration to simplify Hadoop and Spark application development. It lets data centers tap value from their enterprise data assets regardless of where it resides, whether on the mainframe, in distributed systems, or in the cloud.

Syncsort’s new integrated workflow capability also gives organizations a simpler, more flexible way to create and manage their data pipelines. This is done through the company’s design-once, deploy-anywhere architecture with support for Apache Spark 2.0, which makes it easy for organizations to take advantage of the benefits of Spark 2.0 and integrated workflow without spending time and resources redeveloping their jobs.

Assembling such an end-to-end data pipeline can be time-consuming and complicated, with various workloads executed on multiple platforms, all of which need to be orchestrated and kept up to date. Delays in such complicated development, however, can prevent organizations from getting the timely insights they need for effective decision-making.

Enter Syncsort’s Integrated Workflow, which helps organizations manage various workloads, such as batch ETL on large repositories of historical data. This can be done by referencing business rules during data ingest in a single workflow, in effect simplifying and speeding development of the entire data pipeline, from accessing critical enterprise data, to transforming that data, and ultimately analyzing it for business insights.

Finally, in October 2016 Syncsort announced new capabilities in its Ironstream software that allows organizations to access and integrate mainframe log data in real-time to Splunk IT Service Intelligence (ITSI). Further, the integration of Ironstream and Compuware’s Application Audit software deliver the audit data to Splunk Enterprise Security (ES) for Security Information and Event Management (SIEM). This integration improves an organization’s ability to detect threats against critical mainframe data, correlate them with related information and events, and satisfy compliance requirements.

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.

 

IBM Introduces Hitachi-Specific z13

May 30, 2017

Remember when rumors were flying that Hitachi planned to buy the mainframe z Systems business from IBM?  DancingDinosaur didn’t believe it at that time, and now we have an official announcement that IBM is working with Hitachi to deliver mainframe z System hardware for use with Hitachi customers.

Inside the IBM z13

DancingDinosaur couldn’t see Hitachi buying the z. The overhead would be too great. IBM has been sinking hundreds of millions of dollars into the z, adding new capabilities ranging from Hadoop and Spark natively on z to whatever comes out of the Open Mainframe Project.

The new Hitachi deal takes the z in a completely different direction. The plans calls for using Hitachi’s operating system, VOS3, running on the latest IBM z13 hardware to provide Hitachi users with better performance while sustaining their previous investments in business-critical Hitachi data and software, as IBM noted. VOS3 started as a fork of MVS and has been repeatedly modified since.

According to IBM, Hitachi will exclusively adopt the IBM z Systems high-performance mainframe hardware technology as the only hardware for the next generation of Hitachi’s AP series. These systems primarily serve major organizations in Japan. This work expands Hitachi’s cooperation with IBM to make mainframe development more efficient through IBM’s global capabilities in developing and manufacturing mainframe systems. The Open Mainframe Project, BTW, is a Linux initiative.

The collaboration, noted IBM, reinforces its commitment to delivering new innovations in mainframe technology and fostering an open ecosystem for the mainframe to support a broad range of software and applications. IBM recently launched offerings for IBM z Systems that use the platform’s capabilities for speed, scale and security to deliver cloud-based blockchain services for building new transaction systems and machine learning for analyzing large amounts of data.

If you count VOS3, the mainframe now runs a variety of operating systems, including z/OS, z/TPF and z/VM operating systems as well as the Linux. Reportedly, Hitachi plans to integrate its new mainframe with its Lumada Internet of Things (IoT) offerings. With z scalability, security, massive I/O, and performance the z makes an ideal IoT platform, and IoT is a market IBM targets today. Now IBM is seeding a competitor with the z running whatever appealing capabilities Hitachi’s Lumada offers. Hope whatever revenue or royalties IBM gets is worth it.

IBM and Hitachi, as explained in the announcement, have a long history of cooperation and collaboration in enterprise computing technologies. Hitachi decided to expand this cooperation at this time to utilize IBM’s most advanced mainframe technologies. Hitachi will continue to provide its customers with a highly reliable, high-performance mainframe environment built around the Hitachi VOS3 operating system. Hitachi also continues to strengthen mainframe functionality and services which contributes to lower TCO, improved ease of system introduction and operation, and better serviceability.

Of course, the mainframe story is far from over. IBM has been hinting at a new mainframe coming later this year for months.  Since IBM stopped just automatically cranking up core processor speed to boost price/performance it will employ an array of assist processors and software optimizations to boost performance wherever it can, but particularly in the area of its current critical imperatives—security, cognitive computing, blockchain, and cloud. One thing DancingDinosaur doesn’t expect to see in the new z, however, will be cubits embedded, but who knows?

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.

 

IBM Insists Storage is Generating Positive Revenue

May 19, 2017

At a recent quarterly briefing on the company’s storage business, IBM managers crowed over its success: 2,000 new Spectrum Storage customers, 1,300 new DS8880 systems shipped, 1500 PB of capacity shipped, 7% revenue gain Q1’17. This appeared to contradict yet another consecutive losing quarter in which only IBM’s Cognitive Solutions (includes Solutions Software and Transaction Processing Software) posted positive revenue.

However, Martin Schroeter, Senior Vice President and Chief Financial Officer (1Q’17 financials here), sounded upbeat about IBM storage in the quarterly statement: Storage hardware was up seven percent this quarter, led by double-digit growth in our all-flash array offerings. Flash contributed to our Storage revenue growth in both midrange and high-end. In storage, we continue to see the shift in value towards software-defined environments, where we continue to lead the market. We again had double-digit revenue growth in Software-Defined Storage, which is not reported in our Systems segment. Storage software now represents more than 40 percent of our total storage revenue.

IBM Flash System A9000

Highly parallel all-flash storage for hyperscale and cloud data centers

Schroeter continued: Storage gross margins are down, as hardware continues to be impacted by price pressure. To summarize Systems, our revenue and gross profit performance were driven by expected cycle declines in z Systems and Power, mitigated by Storage revenue growth. We continue to expand our footprint and add new capabilities, which address changing workloads. While we are facing some shifting market dynamics and ongoing product transitions, our portfolio remains uniquely optimized for cognitive and cloud computing.

DancingDinosaur hopes he is right.  IBM has been signaling a new z System coming for months, along with enhancements to Power storage. Just two weeks ago IBM reported achievements with Power and Nvidia, as DancingDinosaur covered at that time.

If there was any doubt, all-flash storage is the way IBM and most other storage providers are heading for the performance and competitive economics. In January IBM announced three all flash DS888* all flash products, which DancingDinosaur covered at the time here. Specifically:

  • DS8884 F (the F designates all flash)—described by IBM as performance delivered within a flexible and space-saving package
  • DS8886 F—combines performance, capacity, and cost to support a variety of workloads and applications
  • DS8888 F—promises performance and capacity designed to address the most demanding business workload requirements

The three products are intended to provide the speed and reliability needed for workloads ranging from enterprise resource planning (ERP) and financial transactions to cognitive applications like machine learning and natural language processing. Doubt that a lot of mainframe data centers are doing much with cognitive systems yet, but that will be coming.

Spectrum Storage also appears to be looming large in IBM’s storage plans. Spectrum Storage is IBM’s software defined storage (SDS) family of products. DancingDinosaur covered the latest refresh of the suite of products this past February.

The highlights of the recent announcement included the addition of Cloud Object Storage and a version of Spectrum Virtualize as software only.  Spectrum Control got a slew of enhancements, including new cloud-based storage analytics for Dell EMC VNX, VNXe, and VMAX; extended capacity planning views for external storage, and transparent cloud tiering for IBM Spectrum Scale.  The on-premises editions added consolidated chargeback/showback and support for Dell EMC VNXe file storage. This should make it clear that Spectrum Storage is not only for underlying IBM storage products.

Along the same lines, Spectrum Storage added VMware 6 support and the certified vSphere Web client. In the area of cloud object storage, IBM added native NFS access, enhance STaaS multi-tenancy, IPV6 support, and preconfigured bundles.

IBM also previewed enhancements coming in 2Q’17.   Of specific interest to DancingDinosaur readers will likely be  the likely updates to the FlashSystem and VeraStack portfolio.

The company is counting on these enhancements and more to help pull IBM out of its tailspin. As Schroeter wrote in the 1Q’17 report: New systems product introductions later in the year will drive improved second half performance as compared to the first. Hope so; already big investors are cashing out. Clients, however, appear to be staying for now.

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.

 

IBM Demonstrates Quantum Computing Advantage

May 12, 2017

In an announcement last week, IBM reported that scientists from IBM Research and Raytheon BBN have demonstrated one of the first proven examples of a quantum computer’s advantage over a conventional computer. By probing a black box containing an unknown string of bits, they showed that just a few superconducting qubits can discover the hidden string faster and more efficiently than today’s computers. Their research was published in a paper titled, “Demonstration of quantum advantage in machine learning” in nature.com.

With IBM’s current 5 qubit processor, the quantum algorithm consistently identified the sequence in up to 100x fewer computational steps and was more tolerant of noise than the conventional (non-quantum) algorithm. This is much larger than any previous head-to-head comparison between quantum and conventional processors.

Courtesy: IBM Research

The graphic above defines 3 types of quantum computers. At the top is the quantum annealer, described as the least powerful and most restrictive.  In the middle sits analog quantum, 50-100 qubits, a device able to simulate complex quantum interactions. This will probably be IBM’s next quantum machine; currently IBM offers a 5 qubit device. At the bottom sits the universal quantum. IBM suggests this will scale to over 100,000 qubits and be capable of handling machine learning, quantum chemistry, optimization problems, secure computing, and more. It will be exponentially faster than traditional computers and be able to handle just about all the things even the most powerful conventional supercomputers cannot do now.

The most powerful z System, regardless of how many cores or accelerators or memory or bandwidth, remains a traditional, conventional computer. It deals with problems as a series of basic bits, sequences of 0 or 1. That it runs through these sequences astoundingly fast fools us into thinking that there is something beyond the same old digital computing we have known for the last 50 years or more.

Digital computers see the world and the problems you trying to solve as sequences of 0 and 1. That’s it; there is nothing in-between. They store numbers as sequences of 0 and 1 in memory, and they process stored numbers using only the simplest mathematical operations, add and subtract. As a college student DancingDinosaur was given the most powerful TI programmable calculator then available and, with a few buddies, we tried to come up with things it couldn’t do. No matter how many beer-inspired tries, we never found something it couldn’t handle.  The TI was just another digital device.

Quantum computers can digest 0 and 1 but have a broader array of tricks. For example, contradictory things can exist concurrently. Quantum geeks often cite a riddle dubbed Schrödinger’s cat. In this riddle the cat can be alive and dead at the same time because quantum system can handle multiple, contradictory states. If we had known of Schrödinger’s cat my buddies and I might have stumped that TI calculator.

In an article on supercomputing in Explain That Stuff by Chris Woodford he shows the thinking behind Schrödinger’s cat, called superposition.  This is where two waves, representing a live cat and a dead one, combine to make a third that contains both cats or maybe hundreds of cats. The wave inside the pipe contains all these waves simultaneously: they’re added to make a combined wave that includes them all. Qubits use superposition to represent multiple states (multiple numeric values) simultaneously.

In its latest quantum achievement IBM with only a 5 cubit the quantum algorithm consistently identified the sequence in up to a 100x fewer computational steps and was more tolerant of noise than the conventional (non-quantum) algorithm. This is much larger than any previous head-to-head comparison between quantum and conventional processors.

In effect, the IBM-Raytheon team programmed a black box such that, with the push of a button, it produces a string of bits with a hidden a pattern (such as 0010) for both a conventional computation and a quantum computation. The conventional computer examines the bits one by one. Each result gives a little information about the hidden string. By forcing the conventional computer to query the black box many times it can determine the full answer.

The quantum computer employs a quantum algorithm that extracts the information hidden in the quantum phase — information to which a conventional algorithm is completely blind. The bits are then measured as usual and, in about half the time, the hidden string can be fully revealed.

Most z data centers can’t use quantum capabilities for their daily work, at least not yet. As Woodford noted: It’s very early for the whole field—and most researchers agree that we’re unlikely to see practical quantum computers appearing for many years—perhaps even decades. Don’t bet on it; at the rate IBM is driving this, you’ll probably see useful things much sooner. Maybe tomorrow.

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.

 

No letup by IBM on Blockchain

April 27, 2017

IBM continues to push blockchain. Its latest announcement on the subject, Three Blockchain Adoption Principles Essential for Every CEO, came early this week. The basic pitch: in certain market segments blockchain could potentially help save billions of dollars annually and significantly reduce delays and spoilage. Citing the World Economic Forum, the company adds: “reducing barriers within the international supply chain could increase worldwide GDP by almost five percent and total trade volume by 15 percent.”  That should be sweet music to any C-suite exec.

Blockchain enables transparent food chain

In a related announcement also this week, IBM Japan, Mizuho Financial Group, and Mizuho Bank are building a blockchain-based trade financing platform for trade financing. With the platform, Mizuho is aiming to streamline trading operations and improve supply chain efficiency. The resulting timely and highly secure exchange of trade documents turns out to be essential for stakeholders in the supply chain ecosystem. Digitizing trade information on a blockchain can help alter the way information is shared, infusing greater trust into transactions, making it easier for parties involved in the supply chain, including exporters, importers, shippers, insurance companies, port operators, and port authorities to share critical shipment data in near real-time.

IBM is emerging as a leader in secure open-source blockchain solutions built for the enterprise. An early member of the Linux Foundation’s Hyperledger Project, the company has worked with more than 400 clients across multiple business segments to implement blockchain applications delivered via the IBM Cloud.

DancingDinosaur has its own 3 reasons enterprise data center execs should be excited by blockchain. They are different and more z-centric than IBM’s. First, you probably already have a z System, and the z’s legendary security, availability, and scalability make it a natural for blockchain. Second, the z already comes optimized to handle transactions and most of your transaction data already lives on the z, making it very efficient from a processing standpoint.  Third, until or unless your blockchain grows to some enormous size, it will barely consume any system resources or generate overhead. In that sense, blockchain on your z comes virtually free.

The following blockchain principles are based on IBM’s customer experience:

  1. Blockchain has the potential to transform trade, transactions and business processes: The two concepts underpinning blockchain are “business network” and “ledger.” Taken together, these are what make blockchain a smart, tamper-resistant way to conduct trade, transactions and business processes. Network members exchange assets through a ledger that all members can access and share. The ledger syncs across the network with all members needing to confirm a transaction of tangible or intangible assets before it is approved and stored on the blockchain. This shared view helps establish legitimacy and transparency, even when parties are not familiar with one another.
  2. The value, it turns out, resides in the ecosystem as the blockchain network grows: This should be no surprise to an exec who saw the growth, first of LANs and WANs, and later the Internet and Web. So too, as a business network blockchain can include several different types of participants. Depending on the number of participants in a blockchain network, the value of assets being exchanged, and the need to authorize members with varying credentials adopters should observe the difference between “permissioned” and “permission-less” blockchain networks. The real value for blockchain is achieved when these business networks grow. With a strong ecosystem, blockchain networks can more easily reach critical mass, allowing the users to build new business models and reinvent and automate transaction processes.
  3. Blockchain can significantly improve visibility and trust across business: Block chains can reduce transaction settlement times from days or weeks to seconds by providing immediate visibility to all participants. The technology can also be used to cut excess costs by removing intermediary third-parties, those typically required to verify transactions. Because blockchain is built on the concept of trust, it can help reduce risks of illicit practices carried out over payment networks, helping to mitigate fraud and cybercrimes. Speed, cost efficiency, and transparency are among blockchain’s most significant benefits in the enterprise and within ecosystems of companies conducting trade. IBM, Walmart and Tsinghua University, for example, are exploring the use of blockchain to help address the challenges surrounding food safety [see graphic above]. By allowing members within the supply chain to see the same records, blockchain helps narrow down the source of a contamination

“Critical success factors in blockchain engagements require top-down executive support for innovative use cases and bringing key network participants into the dialogue from the start,” according to Marie Wieck, general manager, IBM Blockchain.

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.

IBM Gets Serious About Open Data Science (ODS) with Anaconda

April 21, 2017

As IBM rapidly ramps up cognitive systems in various forms, its two remaining platforms, z System and POWER, get more and more interesting. This week IBM announced it was bringing the Anaconda Open Data Science (ODS) platform to its Cognitive Systems and PowerAI.

Anaconda, Courtesy Pinterest

Specifically, Anaconda will integrate with the PowerAI software distribution for machine learning (ML) and deep learning (DL). The goal: make it simple and fast to take advantage of Power performance and GPU optimization for data-intensive cognitive workloads.

“Anaconda on IBM Cognitive Systems empowers developers and data scientists to build and deploy deep learning applications that are ready to scale,” said Bob Picciano, senior vice president of IBM Cognitive Systems. Added Travis Oliphant, co-founder and chief data scientist, Continuum Analytics, which introduced the Anaconda platform: “By optimizing Anaconda on Power, developers will also gain access to the libraries in the PowerAI Platform for exploration and deployment in Anaconda Enterprise.”

With more than 16 million downloads to date, Anaconda has emerged as the Open Data Science platform leader. It is empowering leading businesses across industries worldwide with tools to identify patterns in data, uncover key insights, and transform basic data into the intelligence required to solve the world’s most challenging problems.

As one of the fastest growing fields of AI, DL makes it possible to process enormous datasets with millions or even billions of elements and extract useful predictive models. DL is transforming the businesses of leading consumer Web and mobile application companies, and it is catching on with more traditional business.

IBM developed PowerAI to accelerate enterprise adoption of open-source ML and DL frameworks used to build cognitive applications. PowerAI promises to reduce the complexity and risk of deploying these open source frameworks for enterprises on the Power architecture and is tuned for high performance, according to IBM. With PowerAI, organizations also can realize the benefit of enterprise support on IBM Cognitive Systems HPC platforms used in the most demanding commercial, academic, and hyperscale environments

For POWER shops getting into Anaconda, which is based on Python, is straightforward. You need a Power8 with IBM GPU hardware or a Power8 combined with a Nvidia GPU, in effect a Minsky machine. It’s essentially a developer’s tool although ODS proponents see it more broadly, bridging the gap between traditional IT and lines of business, shifting traditional roles, and creating new roles. In short, they envision scientists, mathematicians, engineers, business people, and more getting involved in ODS.

The technology is designed to run on the user’s desktop but is packaged and priced as a cloud subscription with a base package of 20 users. User licenses range from $500 per year to $30,000 per year depending on which bells and whistles you include. The number of options is pretty extensive.

According to IBM, this started with PowerAI to accelerate enterprise adoption of open-source ML/DL learning frameworks used to build cognitive applications. Overall, the open Anaconda platform brings capabilities for large-scale data processing, predictive analytics, and scientific computing to simplify package management and deployment. Developers using open source ML/DL components can use Power as the deployment platform and take advantage of Power optimization and GPU differentiation for NVIDIA.

Not to be left out, IBM noted growing support for the OpenPOWER Foundation, which recently announced the OpenPOWER Machine Learning Work Group (OPMLWG). The new OPMLWG includes members like Google, NVIDIA and Mellanox to provide a forum for collaboration that will help define frameworks for the productive development and deployment of ML solutions using OpenPOWER ecosystem technology. The foundation has also surpassed 300-members, with new participants such as Kinetica, Red Hat, and Toshiba. For traditional enterprise data centers, the future increasingly is pointing toward cognitive in one form or another.

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-Syncsort-Splunk to Boost Mainframe Security

April 6, 2017

The mainframe has proven to be remarkably secure over the years, racking up the highest security certifications available. But there is still room for improvement. Earlier this week Compuware announced Application Audit, a software tool that aims to transform mainframe cybersecurity and compliance through real-time capture of user behavior.

Capturing user behavior, especially in real-time, is seemingly impossible if you have to rely on the data your collect from the various logs and SMF data.  Compuware’s solution, Application Audit, in conjunction with Syncsort and Splunk, fully captures and analyzes start-to-finish mainframe application user behavior.

As Compuware explains: Most enterprises still rely on disparate logs and SMF data from security products such as RACF, CA-ACF2 and CA-Top Secret to piece together user behavior.  This is too slow if you want to capture bad behavior while it’s going on. Some organization try to apply analytics to these logs but that also is too slow. By the time you have collected enough logs to deduce who did what and when the damage may have been done.  Throw in the escalating demands of cross-platform enterprise cybersecurity and increasingly burdensome global compliance mandates you haven’t a chance without an automated tool optimized for this.

Fortunately, the mainframe provides rich and comprehensive session data you can run through and analyze with Application Audit and in conjunction with the organization’s security information and event management (SIEM) systems to more quickly and effectively see what really is happening. Specifically, it can:

  • Detect, investigate, and respond to inappropriate behavior by internal users with access
  • Detect, investigate, and respond to hacked or illegally accessed user accounts
  • Support criminal/legal investigations with complete and credible forensics
  • Fulfill compliance mandates regarding protection of sensitive data

IBM, by the way, is not ignoring the advantages of analytics for z security.  Back in February you read about IBM bringing its cognitive system to the z on DancingDinosaur.  IBM continues to flog cognitive on z for real-time analytics and security; promising to enable faster customer insights, business insights, and systems insights with decisions based on real-time analysis of both current and historical data delivered on an analytics platform designed for availability, optimized for flexibility, and engineered with the highest levels of security. Check out IBM’s full cognitive for z pitch.

The data Compuware and Syncsort collect with Application Audit is particularly valuable for maintaining control of privileged mainframe user accounts. Both private- and public-sector organizations are increasingly concerned about insider threats to both mainframe and non-mainframe systems. Privileged user accounts can be misused by their rightful owners, motivated by everything from financial gain to personal grievances, as well as by malicious outsiders who have illegally acquired the credentials for those accounts. You can imagine what havoc they could wreak.

In addition, with Application Audit Compuware is orchestrating a number of players to deliver the full security picture. Specifically, through collaboration with CorreLog, Syncsort and Splunk, Compuware is enabling enterprise customers to integrate Application Audit’s mainframe intelligence with popular SIEM solutions such as Splunk, IBM QRadar, and HPE Security ArcSight ESM. Additionally, Application Audit provides an out-of-the-box Splunk-based dashboard that delivers value from the start. As Compuware explains, these integrations are particularly useful for discovering and addressing security issues associated with today’s increasingly common composite applications, which have components running on both mainframe and non-mainframe platforms. SIEM integration also ensures that security, compliance and other risk management staff can easily access mainframe-related data in the same manner as they access data from other platforms.

“Effective IT management requires effective monitoring of what is happening for security, cost reduction, capacity planning, service level agreements, compliance, and other purposes,” noted Stu Henderson, Founder and President of the Henderson Group in the Compuware announcement. “This is a major need in an environment where security, technology, budget, and regulatory pressures continue to escalate.”

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.

 

 

IBM Changes the Economics of Cloud Storage

March 31, 2017

Storage tiering used to be simple: active data went to your best high performance storage, inactive data went to low cost archival storage, and cloud storage filled in for one or whatever else was needed. Unfortunately, today’s emphasis on continuous data analytics, near real-time predictive analytics, and now cognitive has complicated this picture and the corresponding economics of storage.

In response, last week IBM unveiled new additions to the IBM Cloud Object Storage family. The company is offering clients new choices for archival data and a new pricing model to more easily apply intelligence to unpredictable data patterns using analytics and cognitive tools.

Analytics drive new IBM cloud storage pricing

By now, line of business (LOB) managers, having been exhorted to leverage big data and analytics for years, are listening. More recently, the analytics drumbeat has expanded to include not just big data but sexy IoT, predictive analytics, machine learning, and finally cognitive science. The idea of keeping data around for a few months and parking it in a long term archive to never be looked at again until it is finally deleted permanently just isn’t happening as it was supposed to (if it ever did). The failure to permanently remove expired data can become costly from a storage standpoint as well as risky from an e-discovery standpoint.

IBM puts it this way: Businesses typically have to manage across three types of data workloads: “hot” for data that’s frequently accessed and used; “cool” for data that’s infrequently accessed and used; and “cold” for archival data. Cold storage is often defined as cheaper but slower. For example, if a business uses cold storage, it typically has to wait to retrieve and access that data, limiting the ability to rapidly derive analytical or cognitive insights. As a result, there is a tendency to store data in more expensive hot storage.

IBM’s new cloud storage offering, IBM Cloud Object Storage Flex (Flex), uses a “pay as you use” model of storage tiers potentially lowering the price by 53 percent compared to AWS S3 IA1 and 75 percent compared to Azure GRS Cool Tier.2 (See footnotes at the bottom of the IBM press release linked to above. However IBM is not publishing the actual Flex storage prices.) Flex, IBM’s new cloud storage service, promises simplified pricing for clients whose data usage patterns are difficult to predict. Flex promises organizations will benefit from the cost savings of cold storage for rarely accessed data, while maintaining high accessibility to all data.

Of course, you could just lower the cost of storage by permanently removing unneeded data.  Simply insist that the data owners specify an expiration date when you set up the storage initially. When the date arrives in 5, 10, 15 years automatically delete the data. At least that’s how I was taught eons ago. Of course storage costs orders of magnitude less now although storage volumes are orders of magnitude greater and near real-time analytics weren’t in the picture.

Without the actual rates for the different storage tiers you cannot determine how much Storage Flex may save you.  What it will do, however, is make it more convenient to perform analytics on archived data you might otherwise not bother with.  Expect this issue to come up increasingly as IoT ramps up and you are handling more data that doesn’t need hot storage beyond the first few minutes of its arrival.

Finally, the IBM Cloud Object Storage Cold Vault (Cold Vault) service gives clients access to cold storage data on the IBM Cloud and is intended to lead the category for cold data recovery times among its major competitors. Cold Vault joins its existing Standard and Vault tiers to complete a range of IBM cloud storage tiers that are available with expanded expertise and methods via Bluemix and through the IBM Bluemix Garages.

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.

 


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