Posts Tagged ‘Cloud’

IBM Launches New IoT Collaborative Initiative

February 23, 2017

Collaboration partners can pull hundreds of millions of dollars in new revenue from IoT, according to IBM’s recent IoT announcement. Having reached what it describes as a tipping point with IoT innovation the company now boasts of having over 6,000 clients and partners around the world, many of whom are now wanting to join in its new global Watson IoT center to co-innovate. Already Avnet, BNP Paribas, Capgemini, and Tech Mahindra will collocate development teams at the IBM Munich center to work on IoT collaborations.

new-ibm-watson-iot-center

IBM Opens New Global Center for Watson IoT

The IBM center also will act as an innovation space for the European IoT standards organization EEBus.  The plan, according to Harriet Green, General Manager, IBM Watson IoT, Cognitive Engagement and Education (pictured above left), calls for building a new global IoT innovation ecosystem that will explore how cognitive and IoT technologies will transform industries and our daily lives.

IoT and more recently cognitive are naturals for the z System, and POWER Systems have been the platform for natural language processing and cognitive since Watson won Jeopardy three years ago. With the latest enhancements IBM has brought to the z in the form of on-premises cognitive and machine learning the z should assume an important role as it gathers, stores, collects, and processes IoT data for cognitive analysis. DancingDinosaur first reported on this late in 2014 and again just last week. As IoT and cognitive workloads ramp up on z don’t be surprised to see monthly workload charges rise.

Late last year IBM announced that car maker BMW will collocate part of its research and development operations at IBM’s new Watson IoT center to help reimagine the driving experience. Now, IBM is announcing four more companies that have signed up to join its special industry “collaboratories” where clients and partners work together with 1,000 Munich-based IBM IoT experts to tap into the latest design thinking and push the boundaries of the possible with IoT.

Let’s look at the four newest participants starting with Avnet. According to IBM, an IT distributor and global IBM partner, Avnet will open a new joint IoT Lab within IBM’s Watson IoT HQ to develop, build, demonstrate and sell IoT solutions powered by IBM Watson. Working closely with IBM’s leading technologists and IoT experts, Avnet also plans to enhance its IoT technical expertise through hands-on training and on-the-job learning. Avnet’s team of IoT and analytics experts will also partner with IBM on joint business development opportunities across multiple industries including smart buildings, smart homes, industry, transportation, medical, and consumer.

As reported by BNP Paribas, Consorsbank, its retail digital bank in Germany, will partner with IBM´s new Watson IoT Center. The company will collocate a team of solution architects, developers and business development personnel at the Watson facility. Together with IBM’s experts, they will explore how IoT and cognitive technologies can drive transformation in the banking industry and help innovate new financial products and services, such as investment advice.

Similarly, global IT consulting and technology services provider Capgemini will collocate a team of cognitive IoT experts at the Watson center. Together they will help customers maximize the potential of Industry 4.0 and develop and take to market sector-specific cognitive IoT solutions. Capgemini plans a close link between its Munich Applied Innovation Exchange and IBM’s new Customer Experience zones to collaborate with clients in an interactive environment.

Finally, the Indian multinational provider of enterprise and communications IT and networking technology Tech Mahindra, is one of IBM’s Global System Integrators with over 3,000 specialists focused on IBM technology around the world. The company will locate a team of six developers and engineers within the Watson IoT HQ to help deliver on Tech Mahindra’s vision of generating substantial new revenue based on IBM’s Watson IoT platform. Tech Mahindra will use the center to co-create and showcase new solutions based on IBM’s Watson IoT platform for Industry 4.0 and Manufacturing, Precision Farming, Healthcare, Insurance and Banking, and automotive.

To facilitate connecting the z to IoT IBM offers a simple recipe. It requires 4 basic ingredients and 4 steps: Texas Instrument’s SensorTag, a Bluemix account, IBM z/OS Connect Enterprise Edition, and a back-end service like CICS.  Start by exposing an existing z Systems application as a RESTful AP. This is where the z/OS Connect Edition comes in.  Then enable your SensorTag device to Watson IoT Quick Start. From there connect the Cloud to your on-premises Hybrid Cloud.  Finally, enable the published IoT data to trigger a RESTful API. Sounds pretty straightforward but—full disclosure—Dancing Dinosaur has not tried it due to lacking the necessary pieces. If you try it, please tell DancingDinosaur how it works (info@radding.net). Good luck.

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 On-Premises Cognitive Means z Systems Only

February 16, 2017

Just in case you missed the incessant drumbeat coming out of IBM, the company committed to cognitive computing. But that works for z data centers since IBM’s cognitive system is available on-premises only for the z. Another z first: IBM just introduced Machine Learning (key for cognitive) for the private cloud starting with the z.

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There are three ways to get IBM cognitive computing solutions: the IBM Cloud, Watson, or the z System, notes Donna Dillenberger, IBM Fellow, IBM Enterprise Solutions. The z, however, is the only platform that IBM supports for cognitive computing on premises (sorry, no Power). As such, the z represents the apex of programmatic computing, at least as IBM sees it. It also is the only IBM platform that supports cognitive natively; mainly in the form of Hadoop and Spark, both of which are programmatic tools.

What if your z told you that a given strategy had a 92% of success. It couldn’t do that until now with IBM’s recently released cognitive system for z.

Your z system today represents the peak of programmatic computing. That’s what everyone working in computers grew up with, going all the way back to Assembler, COBOL, and FORTRAN. Newer languages and operating systems have arrived since; today your mainframe can respond to Java or Linux and now Python and Anaconda. Still, all are based on the programmatic computing model.

IBM believes the future lies in cognitive computing. Cognitive has become the company’s latest strategic imperative, apparently trumping its previous strategic imperatives: cloud, analytics, big data, and mobile. Maybe only security, which quietly slipped in as a strategic imperative sometime 2016, can rival cognitive, at least for now.

Similarly, IBM describes itself as a cognitive solutions and cloud platform company. IBM’s infatuation with cognitive starts with data. Only cognitive computing will enable organizations to understand the flood of myriad data pouring in—consisting of structured, local data but going beyond to unlock the world of global unstructured data; and then to decision tree-driven, deterministic applications, and eventually, probabilistic systems that co-evolve with their users by learning along with them.

You need cognitive computing. It is the only way, as IBM puts it: to move beyond the constraints of programmatic computing. In the process, cognitive can take you past keyword-based search that provides a list of locations where an answer might be located to an intuitive, conversational means to discover a set of confidence-ranked possibilities.

Dillenberger suggests it won’t be difficult to get to the IBM cognitive system on z . You don’t even program a cognitive system. At most, you train it, and even then the cognitive system will do the heavy lifting by finding the most appropriate training models. If you don’t have preexisting training models, “just use what the cognitive system thinks is best,” she adds. Then the cognitive system will see what happens and learn from it, tweaking the models as necessary based on the results and new data it encounters. This also is where machine learning comes in.

IBM has yet to document payback and ROI data. Dillenberger, however, has spoken with early adopters.  The big promised payback, of course, will come from the new insights uncovered and the payback will be as astronomical or meager as you are in executing on those insights.

But there also is the promise of a quick technical payback for z data centers managers. When the data resides on z—a huge advantage for the z—you just run analytics where the data is. In such cases you can realize up to 3x the performance, Dillenberger noted.  Even if you have to pull data from some other location too you still run faster, maybe 2x faster. Other z advantages include large amounts of memory, multiple levels of cache, and multiple I/O processors get at data without impacting CPU performance.

When the data and IBM’s cognitive system resides on the z you can save significant money. “ETL consumed huge amounts of MIPS. But when the client did it all on the z, it completely avoided the costly ETL process,” Dillenberger noted. As a result, that client reported savings of $7-8 million dollars a year by completely bypassing the x-86 layer and ETL and running Spark natively on the z.

As Dillenberger describes it, cognitive computing on the z is here now, able to deliver a payback fast, and an even bigger payback going forward as you execute on the insights it reveals. And you already have a z, the only on-premises way to IBM’s Cognitive System.

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 Unveils Enhanced Repackaged Spectrum Storage

February 9, 2017

IBM appears to be gaining traction with its growing Spectrum SDS family of storage products re-introduced this week. According to the company, 87 of the Fortune Global 100 use IBM Spectrum Storage. That breaks down to all 10 of the top 10 telecommunications companies and all 20 of the top 20 banks. In addition, 18 of the top 20 energy companies, 9 of the top 10 global healthcare companies, and 8 of the top 10 automobile manufacturers adopted Spectrum storage. In addition, IBM notes, 80 organizations pick IBM Spectrum storage every week.

Of course, that hasn’t been enough to turn incessant red ink into black. According to IBM’s 2016 year-end financials, systems (systems hardware—including storage—and operating systems software), posted revenues of $2.5 billion, down 12.5 percent. You can see DancingDinosaur’s report on the latest IBM financials here. Although IBM called out the z for gross profit margins improvements driven by z Systems performance there was nary a word about storage. Will follow upcoming quarterly reports to see if this increased traction translates into actual positive revenue. Stay tuned.

Over the shoulder shot of a group of business colleagues in a meeting around a conference table

IBM introduces Spectrum Computing, 6/16

The announcements this week included:

  • IBM Spectrum Storage Suite
  • IBM Spectrum Virtualize
  • IBM Spectrum Control
  • IBM Spectrum Accelerate
  • IBM Cloud Object Storage

IBM Spectrum Storage isn’t completely new. DancingDinosaur first covered the Spectrum storage introduction in mid-February, 2015. Actually IBM began offering SDS products in 2014 and gained some kudos for it from IDC. The latest announcement really amounts to a repackaging of the products as the IBM Spectrum Storage Suite along with a variety of enhancements, some of which are quite interesting.

For example, IBM Cloud Object Storage software allows new use cases and enables a standalone object store managed by IBM Spectrum Control. It also adds a new storage tier behind IBM Spectrum Scale and a primary pool target behind IBM Spectrum Protect in the form of a cloud container. IBM also continues its innovative licensing arrangement by which you pay for your storage capacity and then can allocate and re-allocate that capacity freely.

Spectrum Cloud Object storage also introduces unified NFS/Object access. This allows companies to store data in a file system structure on object storage using NFS access capability and access data stored as files via either a file or object interface. It has been optimized for scalability and file-to-object migration as well as being able to scale to millions of users and buckets/containers. Finally, it now supports IPv6 management of devices and all nodes in configuration.

IBM Spectrum Virtualize Software also is interesting. For example, it now supports Supermicro SuperServer 2028U-TRTP+ in addition to existing support for Lenovo System x3650 M5. IBM envisions service providers and enterprises deploying Supermicro servers to build new services based on IBM Spectrum Virtualize software to deliver virtualized storage services at a lower price point. Take note: both of these are 2u x86 boxes. They can also offer disaster recovery as a service for clients with SVC.

Finally, IBM has enhanced Spectrum Control in V5.2.13. Among the new capabilities: improved storage insights through new cloud-based storage analytics for Dell EMC VNX, VNXe, and VMAX. This should enable users to improve application performance and reduce storage costs. It also will extend capacity planning views include external storage for IBM Spectrum Scale’s transparent cloud tiering. For on-premises software the latest Spectrum Control offers new support for Dell EMC VNXe file storage.

Overall, the new Spectrum Control should simplify the life of storage managers. “IBM Spectrum Control gives me one pane of glass to manage spinning disk, file system clusters, and object storage, “said Bob Oesterlin, Sr. Principal Storage Engineer, Nuance, as reported by IBM. The ability to span IBM storage as well as that of other vendors should prove a winner.

Combined with other capabilities, such as Spectrum Accelerate V11.5.4’s data-at-rest encryption, ability to flexibly encrypt existing hot data in minutes without disruption, and support for standard key management tools (IBM Security Key Lifecycle Manager and SafeNet KeySecure) will add to the appeal of the enhanced IBM Spectrum Storage Suite. Will it be enough to turn IBM Systems’ red ink to black? We’ll all just have to watch the next few quarterly reports to know.

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.

 

Arcati 2017 Mainframe Survey—Cognitive a No-Show

February 2, 2017

DancingDinosaur checks into Arcati’s annual mainframe survey every few years. You can access a copy of the 2017 report here.  Some of the data doesn’t change much, a few percentage points here or there. For example, 75% of the respondents consider the mainframe too expensive. OK, people have been saying that for years.

On the other hand, 65% of the respondents’ mainframes are involved with web services. Half also run Java-based mainframe apps, up from 30% last year, while 17% more are planning to run Java with their mainframe this year. Similarly, 35% of respondents report running Linux on the mainframe, up from 22% last year. Again, 13% of the respondents expect to add Linux this year.  Driving this is the advantageous cost and management benefits that result from consolidating distributed Linux workloads on the z. Yes, things are changing.

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The biggest surprise for DancingDinosaur, however, revolved around IBM’s latest strategic initiatives, especially cognitive computing and blockchain.  Other strategic initiatives may include, depending on who is briefing you at the moment—security, data analytics, cloud, hybrid cloud, and mobile. These strategic imperatives, especially cognitive computing, are expected to drive IBM’s revenue. In the latest statement, reported last week in DancingDinosaur, strategic imperatives amounted to 41% of revenue.  Cloud revenue and Cloud-as-a-service also rose considerably, 35% and 61% respectively.

When DancingDinosaur searched the accompanying Arcati vendor report (over 120 vendors with brief descriptions) for cognitive only GT Software came up. IBM didn’t even mention cognitive in its vendor listing, which admittedly was skimpy. The case was the same with Blockchain; only one vendor, Atos, mentioned it and nothing about blockchain in the IBM listing. More vendors, however, noted supporting one or some of the other supposed strategic initiatives.

Overall, the Arcati survey is quite positive about the mainframe. The survey found that 50 percent of sites viewed their mainframe as a legacy system (down from last year’s 62 percent). However, 22 percent (up from 16 percent last year) viewed mainframe as strategic, with 28 percent (up from 22 percent) viewing mainframes as both strategic and legacy.

Reinforcing the value of the mainframe, the survey found 78 percent of sites experienced some kind of increase in capacity. With increased demand for mainframe resources (data and processing), it should not be surprising that respondents report an 81 percent an increase in technology costs. Yet, 38 percent of sites report their people costs have decreased or stayed the same.

Unfortunately, the survey also found that 70 percent of respondents thought there were a cultural barrier between mainframe and other IT professionals. That did not discourage respondents from pointing out the mainframe advantages: 100 percent highlighted the benefit of the mainframe’s availability, 83 percent highlighted security, 75 percent identified scalability, and 71 percent picked manageability as a mainframe benefit.

Also, social media runs on the mainframe. Respondents found social media (Facebook, Twitter, YouTube) useful for their work on the mainframe. Twenty-seven percent report using social (up slightly from 25 percent last year) with the rest not using it at all despite IBM offering Facebook pages dedicated to IMS, CICS, and DB2. DancingDinosaur, only an occasional FB visitor, will check it out and report.

In terms of how mainframes are being used, the Arcati survey found that 25 percent of sites are planning to use Big Data; five percent of sites have adopted it for DevOps while 48 percent are planning to use mainframe DevOps going forward. Similarly, 14 percent of respondents already are reusing APIs while another 41 percent are planning to.

Arcati points out another interesting thought: The survey showed a 55:45 percent split in favor of distributed systems. So, you might expect the spend on the two types of platform to be similar. Yet, the survey found that 87 percent of an organization’s IT spend was going to distributed systems! Apparently mainframes aren’t as expensive as people think. Or put it another way, the cost of owning and operating distributed systems with mainframe-caliber QoS amounts to a lot more than people are admitting.

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 Cheers Beating Estimates But Losing Streak Continues

January 26, 2017

It has been 19 quarters since IBM reported positive revenue in its quarterly reports but the noises coming out of IBM with the latest 4Q16 and full year 2016 financials are upbeat due to the company beating analyst consensus revenue estimates and its strategic initiatives are starting to generate serious revenue.   Although systems revenues were down again (12%) the accountants at least had something positive to say about the z: “gross profit margins improved driven by z Systems performance.”

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EZSource: Dashboard visualizes changes to mainframe code

IBM doesn’t detail which z models were contributing but you can guess they would be the LinuxONE models (Emperor and Rock Hopper) and the z13. DancingDinosaur expects z performance to improve significantly in 2017 when a new z, which had been heavily hinted in the 3Q2016 results reported here, is expected to ship.

With it latest financials IBM is outright crowing about its strategic initiatives: Fourth-quarter cloud revenues increased 33 percent.  The annual exit run rate for cloud as-a-service revenue increased to $8.6 billion from $5.3 billion at year-end 2015.  Revenues from analytics increased 9 percent.  Revenues from mobile increased 16 percent and revenues from security increased 7 percent.

For the full year, revenues from strategic imperatives increased 13 percent.  Cloud revenues increased 35 percent to $13.7 billion.  The annual exit run rate for cloud as-a-service revenue increased 61 percent year to year.  Revenues from analytics increased 9 percent.  Revenues from mobile increased 34 percent and from security increased 13 percent.

Of course, cognitive computing is IBM’s strategic imperative darling for the moment, followed by blockchain. Cognitive, for which IBM appears to use an expansive definition, is primarily a cloud play as far as IBM is concerned.  There is, however, a specific role for the z, which DancingDinosaur will get into in a later post. Blockchain, on the other hand, should be a natural z play.  It is, essentially, extremely secure OLTP on steroids.  As blockchain scales up it is a natural to drive z workloads.

As far as IBM’s financials go the strategic imperatives indeed are doing well. Other business units, however, continue to struggle.  For instance:

  • Global Business Services (includes consulting, global process services and application management) — revenues of $4.1 billion, down 4.1 percent.
  • Systems (includes systems hardware and operating systems software), remember, this is where z and Power platforms reside — revenues of $2.5 billion, down 12.5 percent. But as noted above, gross profit margins improved, driven by z Systems performance.
  • Global Financing (includes financing and used equipment sales) — revenues of $447 million, down 1.5 percent.

A couple of decades ago, when this blogger first started covering IBM and the mainframe as a freelancer writing for any technology publication that would pay real money IBM was struggling (if $100 billion behemoths can be thought to be struggling). The buzz among the financial analysts who followed the company was that IBM should be broken up into its parts and sold off.  IBM didn’t take that advice, at least not exactly, but it did begin a rebound that included laying off tons of people and the sale of some assets. Since then it invested heavily in things like Linux on z and open systems.

In December IBM SVP Tom Rosamilia talked about new investments in z/OS and z software like DB2 and CICS and IMS, and the best your blogger can tell he is still there. (Rumors suggest Rosamilia is angling for Rometty’s job in two years.)  If the new z does actually arrive in 2017 and key z software is refreshed then z shops can rest easy, at least for another few quarters.  But whatever happens, you can follow it here.

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 Continues Mainframe Software Renaissance

January 19, 2017

While IBM focuses on its strategic imperatives, especially cognitive computing (which are doing quite well according to the latest statement that came out today–will take up next week), Compuware is fueling a mainframe software renaissance on its own. It’s latest two announcements brings Java-like unit testing to COBOL code via its Topaz product set and automate and intelligently optimize the processing of batch jobs through its acquisition of MVS Solutions. Both modernize and simplify the processes around legacy mainframe coding thus the reference to mainframe software renaissance.

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Let’s start with Compuware’s Topaz set of graphical tools. Since they are GUI-based even novice developers can immediately validate and troubleshoot whatever changes, either intended or inadvertent, they made to the existing COBOL applications.  Compuware’s aim for Topaz for Total Test is to eliminate any notion that such applications are legacy code and therefore cannot be updated as frequently and with the same confidence as other types of applications. Basically, mainframe DevOps.

By bringing fast, developer-friendly unit testing to COBOL applications, the new test tool also enables enterprises to deliver better customer experiences—since to create those experiences, IT needs its Agile/DevOps processes to encompass all platforms, from the mainframe to the cloud.  As a result z shops can gain increased digital agility along with higher quality, lower costs, and dramatically reduced dependency on the specialized knowledge of mainframe veterans aging out of the active IT workforce. In fact, the design of the Topaz tools enables z data centers to rapidly introduce the z to novice mainframe staff, which become productive virtually from the start—another cost saver.

Today in 2017 does management still need to be reminded of the importance of the mainframe. Probably, even though many organizations—among them the world’s largest banks, insurance companies, retailers and airlines—continue run their business on mainframe applications, and recent surveys clearly indicate that situation is unlikely to change anytime soon. However, as Compuware points out, the ability of enterprises to quickly update those applications in response to ever-changing business imperatives is daily being hampered by manual, antiquated development and testing processes; the ongoing loss of specialized COBOL programming knowledge; and the risk and associated fear of introducing even the slightest defect into core mainframe systems of record. The entire Topaz design approach from the very first tool, was to make mainframe code accessible to novices. That has continued every quarter for the past two years.

This is not just a DancingDinosaur rant. IT analyst Rich Ptak from Ptak Associates also noted: “By eliminating a long-standing constraint to COBOL Compuware provides enterprise IT the ability to deliver more digital capabilities to the business at greater speed and with less risk.”

Gartner in its latest Predicts 2017, chimes in with its DevOps equivalent of your mother’s reminder to brush your teeth after each meal: Application leaders in IT organizations should adopt a continuous quality culture that includes practices to manage technical debt and automate tests focused on unit and API testing. It should also automate test lab operations to provide access to production-like environments, and enable testing of deployment through the use of DevOps pipeline tools.” OK mom; everybody got the message.

The acquisition of MVS Solutions, Compuware’s fourth in the last year, adds to the company’s collection of mainframe software tools that promise agile, DevOps and millennial-friendly management of the IBM z platform—a continuation of its efforts to make the mainframe accessible to novices. DancingDinosaur covered these acquisition in early December here.

Batch processing accounts for the majority of peak mainframe workloads at large enterprises, providing essential back-end digital capabilities for customer-, employee- and partner-facing mobile, cloud, and web applications. As demands on these back-end mainframe batch processes intensify in terms of scale and performance, enterprises are under increasing pressure to ensure compliance with SLAs and control costs.

These challenges are exacerbated by the fact that responsibility for batch management is rapidly being shifted from platform veterans with decades of experience in mainframe operations to millennial ops staff who are unfamiliar with batch management. They also find native IBM z Systems management tools arcane and impractical, which increases the risk of critical batch operations being delayed or even failing. Run incorrectly, the batch workloads risk generating excessive peak utilization costs.

The solution, notes Compuware, lies in its new ThruPut Manager, which promises automatic, intelligent optimized batch processing. In the process it:

  • Provides immediate, intuitive insight into batch processing that even inexperienced operators can readily understand
  • Makes it easy to prioritize batch processing based on business policies and goals
  • Ensures proper batch execution by verifying that jobs have all the resources they need and proactively managing resource contention between jobs
  • Reduces the organizations’ IBM Monthly Licensing Charges (MLC) by minimizing rolling four-hour average (R4HA) processing peaks while avoiding counter-productive soft capping

Run in conjunction with Strobe, Compuware’s mainframe application performance management tool, ThruPut Manager also makes it easier to optimize batch workload and application performance as part of everyday mainframe DevOps tasks. ThruPut promises to lead to more efficiency and greater throughput resulting in a shorter batch workload and reduced processing capacity. These benefits also support better cross-platform DevOps, since distributed and cloud applications often depend on back-end mainframe batch processing.

Now, go out an hire some millenials and bring fresh blood into the mainframe. (Watch for DancingDinosaur’s upcoming post on why the mainframe is cool again.)

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.

 

z System-Power-Storage Still Live at IBM

January 5, 2017

A mid-December briefing by Tom Rosamilia, SVP, IBM Systems, reassured some that IBM wasn’t putting its systems and platforms on the backburner after racking up financial quarterly losses for years. Expect new IBM systems in 2017. A few days later IBM announced that Japan-based APLUS Co., Ltd., which operates credit card and settlement service businesses, selected IBM LinuxONE as its mission-critical system for credit card payment processing. Hooray!

linuxone-emperor-2

LinuxONE’s security and industry-leading performance will ensure APLUS achieves its operational objectives as online commerce heats up and companies rely on cloud applications to draw and retain customers. Especially in Japan, where online and mobile shopping has become increasingly popular, the use of credit cards has grown, with more than 66 percent of consumers choosing that method for conducting online transactions. And with 80 percent enterprise hybrid cloud adoption predicted by 2017, APLUS is well positioned to connect cloud transactions leveraging LinuxONE. Throw in IBM’s expansion of blockchain capabilities and the APLUS move looks even smarter.

With the growth of international visitors spending money, IBM notes, and the emergence of FinTech firms in Japan have led to a diversification of payment methods the local financial industry struggles to respond. APLUS, which issues well-known credit cards such as T Card Plus, plans to offer leading-edge financial services by merging groups to achieve lean operations and improved productivity and efficiency. Choosing to update its credit card payment system with LinuxONE infrastructure, APLUS will benefit from an advanced IT environment to support its business growth by helping provide near-constant uptime. In addition to updating its server architecture, APLUS has deployed IBM storage to manage mission-critical data, the IBM DS8880 mainframe-attached storage that delivers integration with IBM z Systems and LinuxONE environments.

LinuxONE, however, was one part of the IBM Systems story Rosamilia set out to tell.  There also is the z13s, for encrypted hybrid clouds and the z/OS platform for Apache Spark data analytics and even more secure cloud services via blockchain on LinuxONE, by way of Bluemix or on premises.

z/OS will get attention in 2017 too. “z/OS is the best damn OLTP system in the world,” declared Rosamilia. He went on to imply that enhancements and upgrades to key z systems were coming in 2017, especially CICS, IMS, and a new release of DB2. Watch for new announcements coming soon as IBM tries to push z platform performance and capacity for z/OS and OLTP.

Rosamilia also talked up the POWER story. Specifically, Google and Rackspace have been developing OpenPOWER systems for the Open Compute Project.  New POWER LC servers running POWER8 and the NVIDIA NVLink accelerator, more innovations through the OpenCAPI Consortium, and the team of IBM and Nvidia to deliver PowerAI, part of IBM’s cognitive efforts.

As much as Rosamilia may have wanted to talk about platforms and systems IBM continues to avoid using terms like systems and platforms. So Rosamilia’s real intent was to discuss z and Power in conjunction with IBM’s strategic initiatives.  Remember these: cloud, big data, mobile, analytics. Lately, it seems, those initiatives have been culled down to cloud, hybrid cloud, and cognitive systems.

IBM’s current message is that IT innovation no longer comes from just the processor. Instead, it comes through scaling performance by workload and sustaining leadership through ecosystem partnerships.  We’ve already seen some of the fruits of that innovation through the Power community. Would be nice to see some of that coming to the z too, maybe through the open mainframe project. But that isn’t about z/0S. Any boost in CICS, DB2, and IMS will have to come from the core z team. The open mainframe project is about Linux on z.

The first glimpse we had of this came last spring in a system dubbed Minsky, which was described back then by commentator Timothy Prickett Morgan. With the Minsky machine, IBM is using NVLink ports on the updated Power8 CPU, which was shown in April at the OpenPower Summit and is making its debut in systems actually manufactured by ODM Wistron and rebadged, sold, and supported by IBM. The NVLink ports are bundled up in a quad to deliver 80 GB/sec bandwidth between a pair of GPUs and between each GPU and the updated Power8 CPU.

The IBM version, Morgan describes, aims to create a very brawny node with very tight coupling of GPUs and CPUs so they can better share memory, have fewer overall GPUs, and more bandwidth between the compute elements. IBM is aiming Minsky at HPC workloads, according to Morgan, but there is no reason it cannot be used for deep learning or even accelerated databases.

Is this where today’s z data center managers want to go?  No one is likely to spurn more performance, especially if it is accompanied with a price/performance improvement.  Whether rank-and-file z data centers are queueing up for AI or cognitive workloads will have to be seen. The sheer volume and scale of expected activity, however, will require some form of automated intelligent assist.

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

Happy Holidays and Best Wishes for 2017

December 21, 2016

DancingDinosaur is taking the rest of the year off. The next posting will be Jan. 5. In the meantime, best wishes for delightful holidays and a peaceful and prosperous New Year. Good time to read a new book (below).

iot-book-cover-2

Until then, based on comments IBM has hinted at we can expect a new z in 2017, might be the z14 as some suggest or something else. Expect it to be optimized for cognitive computing and the other strategic imperatives IBM has been touting for the past two years. But it also will need to satisfy the installed mainframe data center base so expect more I/O, faster performance, and improved price/performance.

Was nice to see LinuxONE come into its own late this year.  Expect to see much more from this z-based machine in 2017. Probably a new LinuxONE machine in the New Year as well.

And we can expect the new POWER9 this year.  That should perk things up a bit, but realistically, it appears IBM considers platform a dirty word. They really want to be a cloud player doing cognitive computing across a slew of vertical industries.

FYI, an important new book on IoT, Building the Internet of Things, by Maciej Kranz was published late in Nov. (See graphic above. It hit third place on the NY Times non-fiction best seller list in mid December. Not bad for a business tech book. You can find it on Amazon.com here. Kranz is a Cisco executive so if you have a relationship with a Cisco rep see if they’ll give you a free copy. Full disclosure: your blogger was the ghostwriter for the book and was thanked in the acknowledgements at the end of the book.  Like movies, Kranz and I have already started on the sequel, The Co-Economy (although the title may change). The new book is briefly described in the IoT book (pg. 161).

BTW, if you’ve always wanted to author a book but didn’t know how to start or finish or proceed, feel welcome to contact me through Technologywriter.com at the bottom of this post. We’ll figure out how to get it done.

Again, best wishes for the holidays. See you in the New Year.

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

AI and IBM Watson Fuel Interest in App Dev among Mainframe Shops

December 1, 2016

BMC’s 2016 mainframe survey, covered by DancingDinosaur here, both directly and indirectly pointed to increased activity in regard to data center applications. Mainly this took the form of increased interest in Java on the z as a platform for new applications. Specifically, 72% of overall respondents reported using Java today while 88% reported plans to increase their use Java. At the same time, the use of Linux on the z has been steadily growing year over year; 41% in 2014, 48% in 2015, 52% in 2016. This growth of both point to a heightened interest in application development, management, and change.

ibm-project-dataworks-visualization-1

IBM’s Project DataWorks uses Watson Analytics to create complex visualizations with one line of code

IBM has been feeding this kind of AppDev interest with its continued enhancement of Bluemix and the rollout of the Bluemix Garage method.  More recently, it recently announced a partnership with Topcoder, a global software development community comprised of more than one million designers, developers, data scientists, and competitive programmers with the aim of stimulating developers looking to harness the power of Watson to create the next generation AI apps, APIs, and solutions.

According to Forrester VP and Principal Analyst JP Gownder in the IBM announcement, by 2019, automation will change every job category by at least 25%. Additionally, IDC predicts that 75% of developer teams will include cognitive/AI functionality in one or more applications by 2018. The industry is driving toward a new level of computing potential not witnessed since the introduction of Big Data

To further drive the cultivation of this new style of developer, IBM is encouraging participation in Topcoder-run hackathons and coding competitions. Here developers can easily access a range of Watson services – such as Conversation, Sentiment Analysis, or speech APIs – to build powerful new tools with the help of cognitive computing and artificial intelligence. Topcoder hosts 7,000 code challenges a year and has awarded $80 million to its community. In addition, now developers will have the opportunity to showcase and monetize their solutions on the IBM Marketplace, while businesses will be able to access a new pipeline of talent experienced with Watson and AI.

In addition to a variety of academic partnerships, IBM recently announced the introduction of an AI Nano degree program with Udacity to help developers establish a foundational understanding of artificial intelligence.  Plus, IBM offers the IBM Learning Lab, which features more than 100 curated online courses and cognitive uses cases from providers like Codeacademy, Coursera, Big Data University, and Udacity. Don’t forget, IBM DeveloperWorks, which offers how-to tutorials and courses on IBM tools and open standard technologies for all phases of the app dev lifecycle.

To keep the AI development push going, recently IBM unveiled the experimental release of Project Intu, a new system-agnostic platform designed to enable embodied cognition. The new platform allows developers to embed Watson functions into various end-user devices, offering a next generation architecture for building cognitive-enabled experiences.

Project Intu is accessible via the Watson Developer Cloud and also available on Intu Gateway and GitHub. The initiative simplifies the process for developers wanting to create cognitive experiences in various form factors such as spaces, avatars, robots, or IoT devices. In effect, it extends cognitive technology into the physical world. The platform enables devices to interact more naturally with users, triggering different emotions and behaviors and creating more meaningful and immersive experiences for users.

Developers can simplify and integrate Watson services, such as Conversation, Language, and Visual Recognition with the capabilities of the device to act out the interaction with the user. Instead of a developer needing to program each individual movement of a device or avatar, Project Intu makes it easy to combine movements that are appropriate for performing specific tasks like assisting a customer in a retail setting or greeting a visitor in a hotel in a way that is natural for the visitor.

Project Intu is changing how developers make architectural decisions about integrating different cognitive services into an end-user experience – such as what actions the systems will take and what will trigger a device’s particular functionality. Project Intu offers developers a ready-made environment on which to build cognitive experiences running on a wide variety of operating systems – from Raspberry PI to MacOS, Windows to Linux machines.

With initiatives like these, the growth of cognitive-enabled applications will likely accelerate. As IBM reports, IDC estimates that “by 2018, 75% of developer teams will include Cognitive/AI functionality in one or more applications/services.”  This is a noticeable jump from last year’s prediction that 50% of developers would leverage cognitive/AI functionality by 2018

For those z data centers surveyed by BMC that worried about keeping up with Java and big data, AI adds yet an entirely new level of complexity. Fortunately, the tools to work with it are rapidly falling into place.

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

 

 

 

IBM Power System S822LC for HPC Beat Sort Record by 3.3x

November 17, 2016

The new IBM Power System S822LC for High Performance Computing servers set a new benchmark for sorting by taking less than 99 seconds (98.8 seconds) to finish sorting 100 terabytes of data in the Indy GraySort category, improving on last year’s best result, 329 seconds, by a factor of 3.3. The win proved a victory not only for the S822LC but for the entire OpenPOWER community. The team of Tencent, IBM, and Mellanox has been named the Winner of the Sort Benchmark annual global computing competition for 2016.

rack-of-new-ibm-power-systems-s822lc-for-high-performance-computing-servers-1Power System S822LC for HPC

Specifically, the machine, an IBM Power S822LC for High Performance Computing (HPC), features NVIDIA NVLink technology optimized for the Power architecture and NVIDIA’s latest GPU technology. The new system supports emerging computing methods of artificial intelligence, particularly deep learning. The combination, newly dubbed IBM PowerAI, provides a continued path for Watson, IBM’s cognitive solutions platform, to extend its artificial intelligence expertise in the enterprise by using several deep learning methods to train Watson.

Actually Tencent Cloud Data Intelligence (the distributed computing platform of Tencent Cloud) won each category in both the GraySort and MinuteSort benchmarks, establishing four new world records with its performance, outperforming the 2015 best speeds by 2-5x. Said Zeus Jiang, Vice President of Tencent Cloud and General Manager of Tencent’s Data Platform Department: “In the future, the ability to manage big data will be the foundation of successful Internet businesses.”

To get this level of performance Tencent runs 512 IBM OpenPOWER LC servers and Mellanox’100Gb interconnect technology, improving the performance of Tencent Cloud big data products with the infrastructure. Online prices for the S822LC starts at about $9600 for 2-socket, 2U with up to 20 cores (2.9-3.3Ghz), 1 TB memory (32 DIMMs), 230 GB/sec sustained memory bandwidth, 2x SFF (HDD/SSD), 2 TB storage, 5 PCIe slots, 4 CAPI enabled, up to 2 NVidia K80 GPU. Be sure to shop for volume discounts.

The 2016 Sort Benchmark Results below (apologies in advance if this table breaks apart)

Sort Benchmark Competition 20 Records (Tencent Cloud ) 2015 World Records 2016 Improvement
Daytona GraySort 44.8 TB/min 15.9 TB/min 2.8X greater performance
Indy GraySort 60.7 TB/min 18.2 TB/min 3.3X greater performance
Daytona MinuteSort 37 TB/min 7.7 TB/min 4.8X greater performance
Indy MinuteSort 55 TB/min 11 TB/min 5X greater performance

Pretty impressive, huh. As IBM explains it: Tencent Cloud used 512 IBM OpenPOWER servers and Mellanox’100Gb interconnect technology, improving the performance of Tencent Cloud big data products with the infrastructure. Then Tom Rosamilia, IBM Senior VP weighed in: “Industry leaders like Tencent are helping IBM and our OpenPOWER partners push performance boundaries for a cognitive era defined by big data and advanced analytics.” The computing record achieved by Tencent Cloud on OpenPOWER turned out to be an important milestone for the OpenPOWER Foundation too.

Added Amir Prescher, Sr. Vice President, Business Development, at Mellanox Technologies: “Real-time-analytics and big data environments are extremely demanding, and the network is critical in linking together the extra high performance of IBM POWER-based servers and Tencent Cloud’s massive amounts of data,” In effect, Tencent Cloud developed an optimized hardware/software platform to achieve new computing records while demonstrating that Mellanox’s 100Gb/s Ethernet technology can deliver total infrastructure efficiency and improve application performance, which should make it a favorite for big data applications.

Behind all of this was the new IBM Power System S822LC for High Performance Computing servers. Currently the servers feature a new IBM POWER8 chip designed for demanding workloads including artificial intelligence, deep learning and advanced analytics.  However, a new POWER9 chips has already been previewed and is expected next year.  Whatever the S822LC can do running POWER8 just imagine how much more it will do running POWER9, which IBM describes as a premier acceleration platform. DancingDinosaur covered POWER9 in early Sept. here.

To capitalize on the hardware, IBM is making a new deep learning software toolkit available, PowerAI, which runs on the recently announced IBM Power S822LC server built for artificial intelligence that features NVIDIA NVLink interconnect technology optimized for IBM’s Power architecture. The hardware-software combination provides more than 2X performance over comparable servers with 4 GPUs running AlexNet with Caffe. The same 4-GPU Power-based configuration running AlexNet with BVLC Caffe can also outperform 8 M40 GPU-based x86 configurations, making it the world’s fastest commercially available enterprise systems platform on two versions of a key deep learning framework.

Deep learning is a fast growing, machine learning method that extracts information by crunching through millions of pieces of data to detect and ranks the most important aspects of the data. Publicly supported among leading consumer web and mobile application companies, deep learning is quickly being adopted by more traditional enterprises across a wide range of industry sectors; in banking to advance fraud detection through facial recognition; in automotive for self-driving automobiles; and in retail for fully automated call centers with computers that can better understand speech and answer questions. Is your data center ready for deep learning?

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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