Tailored Fit Pricing for IBM Z: A Viable R4HA Alternative?

In a previous blog entry, I discussed the pros and cons of IBM Z Solution Consumption License Charges (SCLC): A Viable R4HA Alternative.  Recently on 14 May 2019 IBM announced Tailored Fit Pricing for IBM Z, introducing two comprehensive alternatives to the Rolling 4 Hour Average (R4HA) based pricing model, for both new and existing workloads, with a General Availability (GA) date of 21 June 2019.

To digress a little, for those of us in the Northern Hemisphere, June 21 is considered as the Summer Solstice, where the date might vary, one day before or after, namely June 20-22.  You can then further complicate things with confusing Midsummer’s Day with the Summer Solstice and Astronomical versus Meteorological seasons, but whatever, it’s a significant timeframe, with many traditions throughout Europe.  Once again, Midsummer’s Day can be any date between June 19 and June 24.  Having considered my previous review of SCLC and now the Tailored Fit Pricing announcement, I was reminded of a quotation from A Midsummer Night’s Dream by William Shakespeare, “so quick bright things come to confusion”…

The primary driver for Tailored Fit Pricing for IBM Z is to help mitigate unpredictable costs whilst continuing to deliver optimal business outcomes in the world of Digital Transformation & Hybrid Cloud.  Depending on the type of workload activity in your organisation, a tailored pricing model may be far more competitive when compared to pay-as-you-go schemes that have been typical on many x86 based cloud implementations.  Combining technology with cost competitive commercial models delivered through Tailored Fit Pricing strongly challenges the mindset that IT growth must be done on a public cloud in order to make economic sense.  Put another way, this is the IBM Marketing stance to compete with the ever-growing presence of the major 3 Public Cloud providers, namely Amazon Web Services (AWS), Microsoft Azure and Google Cloud, totalling ~60% of Public Cloud customer spend.

In essence a significant portion of The Tailored Fit Pricing for IBM Z announcement is a brand renaming activity, where the Container Pricing for IBM Z name changes to Tailored Fit Pricing for IBM Z.  The IBM Application Development and Test Solution and the IBM New Application Solution that were previously introduced under the Container Pricing for IBM Z name, are now offered under the Tailored Fit Pricing for IBM Z name.  Tailored Fit Pricing for IBM Z pricing introduces two new pricing solutions for IBM Z software running on the z/OS platform.  The Enterprise Consumption and Enterprise Capacity Solutions are both tailored to your environment and offer flexible deployment options:

  • Enterprise Consumption Solution: a tailored usage-based pricing model where compute power is measured on a per MSU basis.  MSU consumption is aggregated hourly, providing a measurement system better aligned with actual system utilization, when compared with R4HA.  Software charges are based on the total annual MSU usage, assisting users with seasonal workload pattern variations.  A total MSU used charging mechanism is designed to remove MSU capping, optimizing SLA and response time metrics accordingly.
  • Enterprise Capacity Solution: a tailored full-capacity licensing model, offering the maximum level of cost predictability.  Charges are based on the overall size of the physical hardware environment.  Charges are calculated based on the estimated mix of workloads running, while providing the flexibility to vary actual usage across workloads. Charges include increased capacity for development and test environments and reduced pricing for all types of workload growth.  An overall size charging mechanism is designed to remove MSU capping, optimizing SLA and response time metrics accordingly.

The high-level benefits associated with the Enterprise Consumption and Enterprise Capacity solutions can be summarized as:

  • Licensing models that eradicate cost control capping activities, enabling clients to fully exploit the CPU capacity installed
  • Increased CPU capacity for Development and Test (DevTest) environments, enabling clients to dramatically increase DevTest activities, without cost consideration
  • Optimized and potential lower pricing for all types of workload growth, without requiring additional IBM approvals, or additional tagging and tracking

Enterprise Solution License Charges (ESLC) are a new type of Monthly License Charge (MLC) pricing methodology for Enterprise Solutions, tailored for each individual and specific client environment and related requirements.  It was forever thus, whatever the pricing mechanism, the ubiquitous z/OS, CICS, Db2, MQ, IMS, WAS software products are the major considerations for MLC pricing mechanisms.  The Key prerequisites for Tailored Fit Pricing for IBM Z are IBM z14 Models M01-M05 or z14 Model ZR1, running the z/OS 2.2 and higher Operating System.

For new Mission Critical workloads and existing or new Development and Test (DevTest) workloads, Tailored Fit Pricing for IBM Z is clearly a great fit.  The restriction of z14 hardware is a little disappointing, where Solution Consumption License Charges (SCLC) included support for the z13 and z13s server.  I’m guessing that IBM are relying upon a significant z14 field upgrade programme in the next few years, largely based upon the Pervasive Encryption (PE) functionality.  However, for those customers that have run the IBM Z platform for decades and might have invested in cost optimization activities, including but not limited to capping, the jump to these new Enterprise Solution License Charges (ESLC) might take a while…

We could review this isolated announcement to the nth degree, but I’m not sure how productive that might be.  For sure, there is always devil in the detail, but sometimes we need to consider the big picture…

As a baby boomer myself, I see my role as passing on my knowledge to the next generations, although still wanting and striving to learn each and every day.  At this time of year, where the weather is better and roads drier, I drive my classic car a lot more and I enjoy the ability to tune the engine with my ears, hands, eyes and a strobe; getting my hands dirty!  I wonder whether the future of the IBM Z platform ecosystem is somewhat analogous to that of the combustion engine.  Several decades ago, electronics and Engine Management Systems became common place for combustion engines and now the ubiquitous laptop is plugged into the engine bay, to retrieve codes to diagnose and in theory repair faults.  For the consumer, arguably a good thing from a vehicle reliability viewpoint, but from a mechanical engineer viewpoint, have these folks become deskilled?  If you truly want your modern vehicle fixed, you will probably need a baby boomer to do this, one that doesn’t rely on a laptop, but their experience.  Although a sweeping generalisation, as there are always exceptions to any rule, the same applies to the IBM Z environment, where it was forever thus, compute power (MSU/MIPS) optimization relies upon a tune, tune, tune approach.

Whether R4HA or Full Capacity based, software cost charges will only be truly optimized if the system and ultimately application code is tuned.  A possible potential downside of not paying close attention to MSU usage, especially when considering these Enterprise Solution License Charges, is a potential isolated activity to “fix” IBM Z software costs forevermore, based upon a high MSU baseline.  Just as the combustion engine management systems simplify fault or diagnostic data collection, they don’t necessarily highlight that the vehicle owner left their cargo carrier on the vehicle roof, harming fuel efficiency.  A crude analogy for sure, but experience counts for a lot.  We have all probably encountered the Old Engineer & The Hammer story before and ultimately it’s incumbent upon us all, to safeguard that we don’t enable a rapid “death of expertise”.  Once the skills are lost, they’re lost.  Whether iStrobe from Compuware, TurboTune from Critical Path Software Inc. or the myriad of other System Monitor options, engage the experienced engineer and safeguard MSU optimization.  At this point, deploy the latest IBM Z pricing mechanism, namely Tailored Fit Pricing for IBM Z, and you will have truly optimized software costs…

IBM Z Solution Consumption License Charges (SCLC): A Viable R4HA Alternative?

In the same timeframe as the recent IBM z14 and LinuxONE Enhanced Driver Maintenance (GA2) hardware announcements, there were modifications to the Container Pricing for IBM Z mechanism, namely Solution Consumption License Charges (SCLC) and the Application Development and Test Solution.  Neither of these new pricing models are dependent on the IBM z14 GA2 hardware announcement, but do require the latest IBM z13, IBM z13s, IBM z14 or IBM z14 ZR1 servers and z/OS V2.2 and upwards for collocated workloads and z/OS V2.1 and upwards for separate LPAR workloads.

For many years, IBM themselves have attempted to introduce new sub-capacity software pricing models to encourage new workloads to the IBM Z server and associated z/OS operating system.  Some iterations include z Systems New Application License Charges (zNALC), Integrated Workload Pricing (IWP) and z Systems Collocated Application Pricing (zCAP), naming but a few.  The latest iteration appears to be Container Pricing for IBM Z, announced in July 2017, with three options, namely the aforementioned Application Development and Test Solution, the New Application Solution and Payments Pricing Solution.  This recent October 2018 announcement adapts the New Application Solution option, classifying it as the Solution Consumption License Charges (SCLC) mechanism.  For the purposes of this blog, we will concentrate on the SCLC mechanism, although the potential benefits of the Application Development and Test Solution for non-Production workloads should not be under estimated…

From a big picture viewpoint, z/OS, CICS, Db2, IMS and MQ are the most expensive IBM Z software products and of course, IBM Mainframe users have designed their environments to reduce software costs accordingly, initially with sub-capacity and then Workload Licence Charging (WLC) and the associated Rolling 4 Hour Average (R4HA).  Arguably CPU MSU management is a specialized capacity and performance management discipline in itself, with several 3rd party ISV options for optimized soft-capping (I.E. AutoSoftCapping, iCap, zDynaCap/Dynamic Capacity Intelligence).  IBM thinks that this MSU management discipline has thwarted new workloads being added to the IBM Z ecosystem, unless there was a mandatory requirement for CICS, Db2, IMS or MQ.  Hence this recent approach of adding new and qualified workloads, outside of the traditional R4HA mechanism.  These things take time and with a few tweaks and repairs, maybe the realm of possibility exists and perhaps the Solution Consumption License Charges (SCLC) is a viable and eminently usable option?

SCLC offers a new pricing metric when calculating MLC software costs for qualified Container Pricing workloads.  SCLC is based on actual MSU consumption, as opposed to the traditional R4HA WLC metric.  SCLC delivers a pure and consistent metered usage model, where the MSU resource used is charged at the same flat rate, regardless of hourly workload peaks, delivering pricing predictability.  Therefore, SCLC directly reflects the total workload cost, regardless of consumption, on a predictable “pay for what you use” basis.  This is particularly beneficial for volatile workloads, which can significantly impact WLC costs associated with the R4HA.  There are two variations of SCLC for qualified and IBM verified New Applications (NewApp):

  • The SCLC pay-as-you-go option offers a low priced, per-MSU model for software programs within the NewApp Solution, with no minimum financial commitment.
  • The SCLC-committed MSU option offers a saving of 20% over the pay-as-you-go price points, with a monthly minimum MSU commitment of just 25,000 MSUs.

SCLC costs are calculated and charged per MSU on an hourly basis, aggregated over an entire (SCRT) month.  For example, if a NewApp solution utilized 50 MSU in hour #1, 100 MSU in hour #2 and 50 MSU in hour #3, the total chargeable MSU for the 3-hour period would be 200 MSU.  Hourly periods continue to be calculated this way over the entire month, providing a true, usage-based cost model.  We previously reviewed Container Pricing in a previous blog entry from August 2017.  At first glance, the opportunity for a predictable workload cost seems evident, but what about the monthly MSU commitment of 25,000 MSU?

Let’s try and break this down at the simplest level, using the SCLC hourly MSU base metric.  In a fixed 24-hour day and an arbitrary 30-day month, there would be 720 single MSU hours.  To qualify for the 25,000 MSU commitment, the hourly workload would need to average ~35 MSU (~300 MIPS) in size.  For the medium and large sized business, generating a 35 MSU workload isn’t a consideration, but probably is for the smaller IBM Mainframe user.  The monthly commitment also becomes somewhat of a challenge, as a calendar month is 28/29 days, once per year, 30 days, four times per year and 31 days, seven times per year.  This doesn’t really impact the R4HA, but for a pay per MSU usage model, the number of MSU hours per month does matter.  One must draw one’s own conclusions, but it’s clearly easier to exceed the 25,000 MSU threshold in a 31-day month, when compared with a 30, 29 or 28 day month!  From a dispassionate viewpoint, I can’t see any reason why the 20% discount can’t be applied when the 25,000 MSU threshold is exceeded, without a financial commitment form the customer.  This would be a truly win-win situation for the customer and IBM, as the customer doesn’t have to concern themselves about exceeding the arbitrary 25,000 MSU threshold and IBM have delivered a usable and attractive pricing mechanism for the desired New Application workload.

The definition of a New Application workload is forever thus, based upon a qualified and verified workload by IBM, assigned a Solution ID for SCRT classification purposes, integrating CICS, Db2, MQ, IMS or z/OS software.  Therefore existing workloads, potentially classified as legacy will not qualify for this New Application status, but any application re-engineering activities should consider this lower price per MSU approach.  New technologies such as blockchain could easily transform a legacy application and benefit from New Application pricing, while the implementation of DevOps could easily transform non-Production workloads into benefiting from the Application Development and Test Solution Container Pricing mechanism.

In conclusion, MSU management is a very important discipline for any IBM Z user and any lower cost MSU that can be eliminated from the R4HA metric delivers improved TCO.  As always, the actual IBM Z Mainframe user themselves are ideally placed to interact and collaborate with IBM and perhaps tweak these Container Pricing models to make them eminently viable for all parties concerned, strengthening the IBM Z ecosystem and value proposition accordingly.

Optimizing Mission Critical Data Value – IBM Machine Learning for z/OS

Typically the IBM Z Mainframe is recognized as the de facto System Of Record (SOR) for storing Mission Critical data.  It therefore follows for generic business applications, DB2, IMS (DB) and even VSAM could be considered as database servers, while CICS and IMS (DC) are transaction servers.  Extracting value from the Mission Critical data source has always been desirable, initially transferring this valuable Mainframe data source to a Distributed Platform via ETL (Extract, Transform, Load) processes.  A whole new software and hardware ecosystem was born for these processes, typically classified as data warehousing.  This process has proved valuable for the last 20 years or so, but more recently the IT industry has evolved, embracing Artificial Intelligence (AI) technologies, ultimately generating Machine Learning capabilities.

For some, it’s important to differentiate between Artificial Intelligence and Machine Learning, so here goes!  Artificial Intelligence is an explicit Computer Science activity, endeavouring to build machines capable of intelligent behaviour.  Machine Learning is a process of evolving computing platforms to act from data patterns, without being explicitly programmed.  In the “what came first world, the chicken or the egg”?  You need AI scientists and engineers to build the smart computing platforms, but you need data scientists or pseudo machine learning experts to make these new computing platforms intelligent.

Conceptually, Machine Learning could be classified as:

  • An automated and seamless learning ability, without being explicitly programmed
  • The ability to grow, change, evolve and adapt when encountering new data
  • An ability to deliver personalized and optimized outcomes from data analysed

When considering this Machine Learning ability with the traditional ETL model, eliminating the need to move data sources from one platform to another, eradicates the “point in time” data timestamp of such a model, and any associated security exposure of the data transfer process.  Therefore, returning to the IBM Z Mainframe being the de facto System Of Record (SOR) for storing Mission Critical data, it’s imperative that the IBM Z Mainframe server delivers its own Machine Learning ability…

IBM Machine Learning for z/OS is an enterprise class machine learning platform solution, assisting the user to create, train and deploy machine learning models, extracting value from your mission critical data on IBM Z platforms, retaining the data in situ, within the IBM Z complex.

Machine Learning for z/OS integrates several IBM machine learning capabilities, including IBM z/OS Platform for Apache Spark.  It simplifies and automates the machine learning workflow, enabling collaboration on machine learning projects across personal and disciplines (E.g. Data Scientists, Business Analysts, Application Developers, et al).  Retaining your Mission Critical data in situ, on your IBM Z platforms, Machine Learning for z/OS significantly reduces the cost, complexity security risk and time for Machine Learning model creation, training and deployment.

Simplistically there are two categories of Machine Learning:

  • Supervised: A model is trained from a known set of data sources, with a target output in mind. In mathematical terms, a formulaic approach.
  • Unsupervised: There is no input or output structure and unsupervised machine learning is required to formulate results from evolving data patterns.

In theory, we have been executing supervised machine learning for some time, but unsupervised is the utopia.

Essentially Machine Learning for z/OS comprises the following functions:

  • Data ingestion (From SOR data sources, DB2, IMS, VSAM)
  • Data preparation
  • Data training and validation
  • Data evaluation
  • Data analysis deployment (predict, score, act)
  • Ongoing learning (monitor, ingestion, feedback)

For these various Machine Learning functions, several technology components are required:

  • z/OS components on z/OS (MLz scoring service, various SPARK ML libraries and CADS/HPO library)
  • Linux/x86 components (Docker images for Repository, Deployment, Training, Ingestion, Authentication and Metadata, services)

The Machine Learning for z/OS solution incorporates the following added features:

  • CADS: Cognitive Assistant for Data Scientist (helps select the best fit algorithm for training)
  • HPO: Hyper Parameter Optimization (provides the Data Scientist with optimal parameters)
  • Brunel Visualization Tool (assist the Data Scientist to understand data distribution)

Machine Learning for z/OS provides a simple framework to manage the entire machine learning workflow.  Key functions are delivered through intuitive web based GUI, a RESTful API and other programming APIs:

  • Ingest data from various sources including DB2, IMS, VSAM or Distributed Systems data sources.
  • Transform and cleanse data for algorithm input.
  • Train a model for the selected algorithm with the prepared data.
  • Evaluate the results of the trained model.
  • Intelligent and automated algorithm/model selection/model parameter optimization based on IBM Watson Cognitive Assistant for Data Science (CADS) and Hyper Parameter Optimization (HPO) technology.
  • Model management.
  • Optimized model development and Production.
  • RESTful API provision allowing Application Development to embed the prediction using the model.
  • Model status, accuracy and resource consumption monitoring.
  • An intuitive GUI wizard allowing users to easily train, evaluate and deploy a model.
  • z Systems authorization and authentication security.

In conclusion, the Machine Learning for z/OS solution delivers the requisite framework for the emerging Data Scientists to collaborate with their Business Analysts and Application Developer colleagues for delivering new business opportunities, with smarter outcomes, while lowering risk and associated costs.

DB2: Internal Subsystem Security vs. External Security Manager (ESM)?

With the ever increasing requirement for regulatory compliance and the clear and present danger associated with cybersecurity attacks, isn’t now the best time to safeguard your organizations most important asset, namely business data?  Various industry analyst quotes state that ~80%+ of Mainframe data resides in databases and associated data sources and ~80%+ of global corporate data originates or resides on IBM Mainframes.  Depending on your viewpoint, rightly or wrongly DB2 is the most pervasive of database subsystems, offering two mechanisms for security, either internal subsystem or External Security Manager (ESM) based via ACF2, RACF or Top Secret.  When DB2 was first released in 1983, Mainframe security was in its infancy and perhaps even an afterthought, and so implementing internal DB2 security might have been the typical approach for many years.  Some several decades later, asking that age old rhetorical question; what is the best security solution for my mission critical and priceless data?  I’m not sure it is a rhetorical question, the answer is patently obvious, external security!

RACF and DB2 security integration was introduced in 1997 with OS/390 2.4 and DB2 Version 6 and so a ~14 year period where DB2 internal security was the only option!  Personally, ~20 years ago I was involved with an internal DB2 to RACF security migration project, part of a larger Operating System, DB2 and indeed CICS upgrade.  Basically the DB2 DBA team stated “we would have never implemented internal DB2 security if the RACF option was available; can you migrate to RACF for us”?  The simple reality being that Security Management is not a core DBA skill and such a process is ideally delivered by a Subject Matter Expert (SME).  Of course, DB2 was somewhat straightforward ~20 years ago, as were its security features, but in the last ~20 years, DB2 has become more complex and enterprise wide, while I’m often surprised by the number of organizations I encounter, both small and large, still deploying internal DB2 security…

Recognizing a ~20 year longevity period of RACF security support for DB2, maybe even the most conservative of organizations might concede that the technology is proven and works?  From a business viewpoint, such a migration from DB2 internal to an External Security Manager (ESM) is the proverbial “no brainer”, because:

  • Subject Matter Expert (SME): Clearly all IBM Mainframe organizations now have dedicated security professionals who are ideally placed to implement enterprise wide security policies. A DB2 DBA is a highly skilled SME in their own discipline, most likely welcoming the migration of security from DB2 to ACF2, RACF or Top Secret.
  • Compliance: A plethora of industry regulations, including but not limited to GLB, SOX, PCI-DSS, et al, dictate that a hybrid of technical skills and business policy knowledge is required. This has generated a requirement for the executive level CISO role and associated security certifications (E.g. CISA, CISM, CISSP) for SME resources.
  • Auditability: From a board level CxO viewpoint, which technical resource would you want responsible for your security policy, the CISO/CIO and their security engineers or a DB2 DBA?
  • Hacking-Penetration Testing: Rightly or wrongly, rightly in my opinion, Penetration (Pen) Testing is a methodology to try and hack a system in order to highlight security vulnerabilities, supplementing the traditional periodic audit processes. Once again, high levels of security expertise are required for such activities.

From a technical viewpoint, what is the complexity of performing a DB2 internal to RACF external security migration?

From a DB2 viewpoint, internal security rules are stored in DB2 catalog tables with the SYSIBM.SYSxxxAUTH naming convention.  Therefore these data repositories can be processed with a simplistic DB2 to RACF security migration tool (RACFDB2).  As per any migration activity, Garbage In, Garbage Out (GIGO) applies, and this golden rule dictates the requirement for a collaborative team effort to execute a DB2 to RACF security migration process.  Of course, the most important resources will be the DB2 DBA(s) responsible for maintaining DB2 security and a RACF SME.  Between them, these 2 resources have all of the skills required to perform this migration process, if not the experience.

From a documentation viewpoint, there are several resources that can be referenced to simplify this process:

The purpose of this blog post is a “call to action”, for those sites still deploying DB2 internal security, to migrate to their External Security Manager (ESM), whether ACF2, RACF or Top Secret.  There are also options for the migration of internal DB2 security to CA ACF2 and Top Secret respectively.

As previously stated, the DB2 DBA will be ideally placed to review the existing internal DB2 security environment, performing any clean-up and rationalization before the actual migration process.  The initial pass of the migration process will inevitably produce a one:one (1:1) mapping of rules, generating numerous security definitions extraneous to requirements.  This is where the ACF2, RACF or Top Secret SME can collaborate with their DB2 DBA, applying grouping, masking and generic filters to vastly reduce and simplify the number of security definitions required.  As with any migration, perform on the lowest level non-Production environment first, apply the lessons learned, and use common sense, issuing warning messages for inadvertent security policy errors, as opposed to denying security access for Production migrations!  Therefore allowing for the smooth transition from DB2 internal to ESM based security.

In my opinion, each and every IBM Mainframe organization has the ability to initiate this DB2 internal to external ACF2, RACF or Top Secret migration project.  Leveraging from 3rd party organizations also makes sense and in no particular order, other than alphabetical, I would suggest IBM Global Services, millennia, RSM Partners or Vanguard.

In conclusion, the IBM System z External Security Manager (ESM), whether ACF2, RACF or Top Secret is an ever-evolving solution with highly advanced security functionality and the de facto central repository for IBM Mainframe security policy management.  From a Security Information & Event Management (SIEM) integration viewpoint, any IBM Mainframe security policy violations will be reported upon in near real-time, while being managed by IBM Mainframe security experts.  Without doubt, if DB2 was implemented before 1997, internal security would have applied, but there has been a ~20 year period where migration to the ACF2, RACF or Top Secret ESM could have happened.  If such a migration activity applies to your organization, I would hope it’s a high priority item, given the potential security risk and priceless value of your business data!

zHyperLink: Just Another System z DASD I/O Function Enhancement?

Over the last several decades or so the IBM Mainframe platform has delivered several new technologies that have dramatically improved the performance of disk (DASD) I/O performance.  Specifically the deployment of ESCON as the introduction to Fibre Optical channels, followed by EMIF for channel sharing and reduced I/O protocol, superseded by FICON and most recently zHPF.  All of these technologies have allowed for ever larger amounts of data to be processed by the System z server and the adoption of Geographically Dispersed Parallel Sysplex (GDPS) implementations for business continuity reasons.  Ultimately mission critical data and decisions are facilitated by applications and sub-second response times for these transactions is expected.  Some might say that we’re always running to stand still from a performance perspective when implementing the latest System z technologies?

In reality, today’s 21st Century mission-critical application is not just capturing and storing customer data, it’s doing so much more, attempting to make informed business decisions for a richer customer experience!  Historically a customer transaction would be on a one-to-one basis (E.g. ask for a balance query), whereas today, said transaction might generate more data for the customer, potentially offering them a new or enhanced product.  In theory, this informed and intelligent transaction processing delivers a richer experience for the customer and potentially new revenue opportunities for the business.

For several years IBM have integrated the Cloud, Analytics, Mobile, Social & Security (CAMSS) initiative into their product offerings, recognising that a business transaction can originate from the cloud or a mobile device, potentially via a Social Media platform, require rich processing via real-time analytics, while requiring the highest levels of security.  Of course, one must draw one’s own conclusions, but maintaining sub-second or ultra-fast transaction response times, with this level of CAMSS complexity requires significant performance enhancements.  To deliver such ultra-fast response times requires the DASD I/O subsystem to maintain the highest levels of performance, aligned with the latest System z server platform…

In January 2017 IBM issued a Statement of Direction (SoD) and associated FAQ for their zHyperLink technology.  zHyperLink is a new short distance mainframe link technology designed for up to 10 times lower latency than zHPF.  zHyperLink is intended to accelerate DB2 for z/OS transaction processing and improve active log throughput.  IBM intends to deliver field upgradable support for zHyperLink on the existing IBM DS8880 storage subsystem.  zHyperLink technology is a new mainframe attach link.  It is the result of collaboration between DB2 for z/OS, the z/OS operating System, IBM System z servers and the DS8880 storage subsystem to deliver extreme low latency I/O access for DB2 for z/OS applications.  zHyperLink technology is intended to complement FICON technology, accelerating those I/O requests that are typically used for transaction processing.  These links are point-to-point connections between the System z CEC and the storage system and are limited to 150 meter distances.  These links do not impact the z Architecture 8 channel path limit.

From a DB2 I/O service performance perspective viewpoint, at short distances, a native FICON or zHPF originated I/O typically requires 300 Microseconds (μs) for a simple I/O operation.  The coupling facility for z Systems typically can read or write 4K of data in in under 8 Microseconds.  zHyperLink technology will provide a new short distance link from the mainframe to storage to read and write data up to 10 times faster than FICON or zHPF; reducing DB2 I/O service times to an anticipated 20-30 Microseconds.

In conclusion, with a promise of 10 times faster processing, as per its fibre optic channel technology predecessors, particularly EMIF and zHPF, zHyperLink is a revolutionary DASD I/O function and not just another DASD I/O subsystem function enhancement.  At this stage, the deployment of zHyperLink functionality is restricted to DB2 and the IBM DS8880 storage subsystem, while we eagerly await compatibility support from EMC and HDS accordingly.  Moreover, as per the evolution of zHPF, we hope for the inclusion of other I/O workloads to benefit from this paradigm changing I/O response time technology.

Finally, as always, the realm of possibility always exists for each and every System z DASD I/O subsystem to be monitored and tuned on a proactive and 24*7*365 basis.  Although all of this DASD I/O performance data has always been and still is captured by RMF (CMF) data, intelligent processing of this data requires an ever evolving Performance Management process and arguably an intelligent software solution (E.g. IntelliMagic Direction Disk Magic or Technical Storage Easy Analyze Disk Mainframe) to provide meaningful information and business decisions from ever increasing amounts of RMF (CMF) data.  In November 2016 ago I delivered the DASD I/O Performance Management Is Easy? session at the UK GSE Annual 2016 meeting accordingly…

Are You Ready For z Systems Workload Pricing for Cloud (zWPC) for z/OS?

Recently IBM announced the z Systems Workload Pricing for Cloud (zWPC) for z/OS pricing mechanism, which can minimize the impact of new Public Cloud workload transactions on Sub-Capacity license charges.  Such benefits will be delivered where higher Public Cloud workload transaction volumes may cause a spike in machine utilization.  Of course, if this looks familiar and you have that feeling of déjà vu, this is a very similar mechanism to Mobile Workload Pricing (MWP)…

Put simply, zWPC applies to any organization that has implemented Sub-Capacity pricing via the basic AWLC or AEWLC pricing mechanisms, for the usual MLC software suspects, namely z/OS, CICS, DB2, IMS, MQ and WebSphere Application Server (WAS).  An eligible transaction is one classified as Public Cloud originated, connecting to a z/OS hosted transactional service and/or data source via a REST or SOAP web service.  Public Cloud workloads are defined as transactions processed by named Public Cloud applications transactions identified as originating from a recognized Public Cloud offering, including but not limited to, Amazon Web Services (AWS), Microsoft Azure, IBM Bluemix, et al.

As per MWP, SCRT calculates the R4HA for Public Cloud transaction GP MSU resource usage, subtracting 60% of those values from the traditional Sub-Capacity software eligible MSU metric, with LPAR granularity, for each and every reporting hour.  The software program values for the same hour are aggregated for all Sub-Capacity eligible LPARs, deriving an adjusted Sub-Capacity value for each reporting hour.  Therefore SCRT determines the billable MSU peak for a given MLC software program on a CPC using the adjusted MSU values.  As per MWP, this will only be of benefit, if the Public Cloud originated transactions generate a spike in the current R4HA.

One of the major challenges for implementing MWP was identifying those transactions eligible for consideration.  Very quickly IBM identified this challenge and offered a WorkLoad Manager (WLM) based solution, to simplify reporting for all concerned.  This WLM SPE (OA47042), introduced a new transaction level attribute in WLM classification, allowing for identification of mobile transactions and associated processor consumption.  These Reporting Attributes were classified as NONE, MOBILE, CATEGORYA and CATEGORYB.  Obviously IBM made allowances for future workload classifications, hence it would seem Public Cloud will supplement Mobile transactions.

In a previous z/OS Workload Manager (WLM): Balancing Cost & Performance blog post, we considered the merits of WLM for optimizing z/OS software costs, while maintaining optimal performance.  One must draw one’s own conclusions, but there seemed to be a strong case for WLM reporting to be included in the z/OS MLC Cost Manager toolkit.  The introduction of zWPC, being analogous to MWP, where reporting can be simplified with supplied and supported WLM function, indicates that intelligent and proactive WLM reporting makes sense.  Certainly for 3rd party Soft-Capping solutions, the ability to identify MWP and zWPC eligible transactions in real-time, proactively implementing MSU optimization activities seems mandatory.

The Workload X-Ray (WLXR) solution from zIT Consulting delivers this WLM reporting function, seamlessly integrating with their zDynaCap and zPrice Manager MSU optimization solutions.  Of course, there is always the possibility to create your own bespoke reports to extract the relevant information from SMF records and subsystem diagnostic data, for input to the SCRT process.  However, such a home-grown process will only work on a monthly reporting basis and not integrate with any Soft-Capping MSU management, which will ultimately control z/OS MLC costs.

In conclusion, from a big picture viewpoint, in the last 2 years or so, IBM have introduced several new Sub-Capacity pricing mechanisms to help System z Mainframe users optimize z/OS MLC costs, namely Mobile Workload Pricing (MWP), Country Multiplex Pricing (CMP) and now z Systems Workload Pricing for Cloud (zWPC).  In theory, at least one of these new pricing mechanisms should deliver benefit to the committed System z user, deploying this server for strategic and Mission Critical workloads.  With the undoubted strategic importance associated with Analytics, Blockchain, Cloud, DevOps, Mobile, Social, et al, the landscape for System z workloads is rapidly evolving and potentially impacting those sacrosanct legacy Mission Critical workloads.  Seemingly the realm of possibility exists that Cloud and Mobile originated transactions will dominate access to System z Mainframe System Of Record (SOR) data repositories, which generates a requirement to optimize associated MLC costs accordingly.  Of course, for some System z users, such Cloud and Mobile access might not be on today’s to-do list, but inevitably it’s on the horizon, and so why not implement the instrumentation ability ASAP!

How to Connect Mobile Workloads to System z

Despite potential security concerns, primarily data encryption and multiple-factor authentication related, mobile transactions continue to increase their share of the market, accounting for up to half of online transactions. Mobile payments now account for 30%+ of all global online transactions as of Q3 2015, continuing the upward trend experienced for the last several years. Although there are global differences in mobile transaction adoption, all global locations are experiencing rapid growth in mobile transaction adoption. Furthermore, as a general rule of thumb, seemingly ~66% of mobile transactions originate from a smartphone, a ~2:1 ratio when compared with tablet devices. Therefore it seems highly probable that smartphone originated mobile transactions will become the de facto standard for online transactions…

For System z users, the majority of their TCO continues to be IBM MLC software related and seemingly the realm of possibility exists for retail operations to reduce IBM MLC TCO as a result of modernizing their business for this mobile transaction phenomenon. Recognizing the security, scalability and transaction ability of the System z platform, why wouldn’t it be the ideal platform for mobile transactions? Furthermore, deploying mobile workloads that can take advantage of modern low cost System z pricing metrics, namely System z Collocated Application Pricing (zCAP) and Mobile Workload Pricing (MWP) for z/OS, could substantially reduce IBM MLC TCO. In theory, existing legacy applications might become somewhat static in nature, as mobile transactions replace existing traditional transaction mechanisms. Therefore the cost per business transaction reduces, potentially significantly.

So, just how easy is it to connect mobile transactions to the System z platform?

z/OS Connect is a software function engineered to leverage from the Liberty Profile for z/OS, acting as an enabler of connectivity between the mobile environment (client) and the System z platform (host). Put another way, z/OS Connect exposes System z assets for mobile and cloud workloads. Quite simply z/OS Connect delivers JSON (JavaScript Object Notation) and REST (REpresentational State Transfer) functionality to leverage from existing z/OS subsystems (E.g. CICS, IMS, Batch, et al). These traditional System z transaction systems (E.g. CICS, IMS) often integrated with DB2, are repositories for vast amounts of business transactions and data. There is no incremental cost for z/OS Connect usage, being packaged with WebSphere Application Server (WAS), CICS and IMS software products.

z/OS Connect provides a discovery function allowing developers to query services that have been configured for a z/OS Connect instance. A single z/OS Connect REST call returns a list of all configured services and another REST call will return the details of a given service. Importantly, developers only need to know the REST API service and associated JSON requirements to achieve this mobile device to System z interoperability; they do not need to know the underlying CICS or IMS subsystem. z/OS Connect incorporates a data conversion function that maps JSON to the host (I.E. CICS or IMS) data format requirement. Put really simply, when a request is received, z/OS Connect converts the data for CICS or IMS subsystem processing and when a response is produced, z/OS Connect converts the data back to JSON.

From a security viewpoint, standard or bespoke code can be used for control before and after a request is processed, identified as an interceptor. For Security, the calling user identity can be checked against defined roles, determining if they have authority to use z/OS Connect or the configured service. On z/OS the security interface is SAF, supplemented by an External Security Manager (ESM), namely ACF2, RACF or TopSecret. For Audit, request information can be logged via SMF for later analysis. Information about each request is logged, including timestamp, bytes processed, response time and USERID.

To summarize, z/OS Connect is designed to simplify the integration of mobile systems and z/OS assets. Delivering a consistent front-end interface for mobile systems via REST and JSON, z/OS Connect seamlessly integrates with WAS, CICS and IMS subsystems for data processing. In theory, a developer could code a mobile workload application, with no knowledge of the System z platform.

In conclusion, it seems we have to accept the adoption of the smartphone device for processing an ever increasing amount of online transactions. The realm of possibility exists that online transactions (click) will continue to displace traditional and legacy (brick) transactions. Therefore as businesses evolve to accommodate mobile transactions, they should strive to reduce their IBM MLC TCO accordingly, delivering JSON and REST applications that can leverage from optimal cost z/OS MLC software, primarily via the zCAP and MWP pricing mechanisms. z/OS Connect is one such option that simplifies the timely delivery of mobile workload applications.

System z MLC Pricing Increases: Look After The Pennies…

Recently IBM announced ~4% price increases in z/OS Monthly License Charges (MLC) for selected Operating System and Middleware software programs and associated features. Specifically, price increases will apply to the VWLC, AWLC, EWLC, AEWLC, PSLC, FWLC and TWLC pricing metrics. Notably, SDSF price increases will be ~20% with Advanced Function Printing (AFP) product price increases of ~13-24%. In a global economy where inflation rates for The USA and Western Europe are close to 0%, one must draw one’s own conclusions accordingly. Lets’ not forget that product version changes typically have an associated price increase. From a contractual viewpoint, IBM only have to provide 90 days advance notice for such price changes, in this instance, IBM provided 150+ days advanced notice.

Price increases are inevitable and as always, it’s better to be proactive as opposed to reactive to such changes. As always, the old proverbs always make good sense and in this instance, “look after the Pennies and the Pounds will look after themselves”! This periodic IBM price increase is inevitable, but is not the underlying issue for controlling System z software costs. For many years, since 1994 to be precise, when IBM introduced Parallel Sysplex License Charges (PSLC), the need for IBM Mainframe users to minimize MSU usage has been of high if not critical importance. Nothing has changed in this 20+ year period and even though IBM might have introduced Sub-Capacity and specialty engines to minimize chargeable MSU usage, has each and every System z user optimized their MSU usage? Ideally this would not be a rhetorical question, rather being a “Golden Rule”, where despite organic CPU capacity increases of ~10% per annum, a System z environment could maintain near static IBM MLC software costs.

I have written several blog entries and presented on this subject matter over the years, for example:

The simple bottom line is that System z MLC software accounts for ~20-35% of the overall System z TCO, typically being the #1 expenditure item. For that reason alone, it’s incumbent for each and every System z user to safeguard they have the technical and commercial skills in place to manage this cost item, not as an afterthought, but inbuilt into each and every System z process, from application design, through to that often neglected afterthought, application tuning.

Many System z organizations might try to differentiate between a nuance of System and Application tuning, but such a “not my problem” type attitude is not acceptable and will be imposing a significant financial burden on each and every organization.

A dispassionate and pragmatic approach is required for optimizing System z CPU usage. In this timeframe, let’s examine the ~20% SDSF price increase. IBM will quite rightly state that in conjunction with their z/OS 2.2 release, there are significant SDSF product function advancements, including zIIP offload, REXX interoperability and increased information delivery. However are such function improvements over and above the norm and not expected as a Business As Usual (BAU) product improvements, which should be included in the Service & Support (S&S) or Monthly License Charges (MLC) paid for software?

In October 2013 I wrote a blog entry; Mainframe ISV Software: Is Continuous Product Improvement Always Evident? The underlying message was that an ISV should deliver the best product they can, for each and every release, without necessarily increasing software costs. In this particular instance, the product was an SDSF equivalent, namely (E)JES, which many years ago delivered all of the function incorporated in SDSF for z/OS 2.2, but for a fraction of the cost…

As of 1 November 2015, IBM will start billing cycles for Country Multiplex Pricing (CMP), which requires the October 2015 version of SCRT, namely V23R10. A Multiplex is defined as a collection of all System z servers in one country, measured as one System z server for software sub-capacity reporting. Sub-Capacity program utilization peaks across the Multiplex will be measured, as opposed to separate peaks by System z servers. CMP also provides the flexibility to move and run workloads anywhere with the elimination of Sysplex aggregation pricing rules.

Migrating to CMP is focussed on CPU capacity growth and flexibility going forward. Therefore System z users should not expect price reductions for their existing workloads upon CMP deployment. Indeed there are CMP deployment considerations. A CMP MSU baseline (base) needs to be established, where this MSU Base and associated MLC Base Factor is established for each sub-capacity MLC product and each applicable feature code. These MSU and MLC bases represent the previous 3 Month averages reported by SCRT before commencing CMP. Quite simply, to gain the most from CMP, the System z user must safeguard that their R4HA for each and every MLC product is optimized, before setting the CMP baseline, otherwise CMP related cost savings going forward are likely to be null.

From a very high-level management viewpoint, we must observe that IBM are a commercial organization, and although IBM provide mechanisms for controlling cost going forward, only the System z user can optimize System z MLC cost for their organization. Arguably with CMP, Soft-Capping isn’t a consideration, it’s mandatory.

Put very simply, each and every System z user can safeguard that they look after the Pennies (Cents) and the Pounds (Euros, Dollars) will look after themselves by paying careful attention to System z MLC software costs. Setting a baseline of System z MLC costs is mandatory, whether for the first time, or to set a new baseline for CMP deployment. Maintaining or lowering this System z MLC cost baseline should or arguably must be the objective going forward, even when considering 10% organic CPU growth, each and every year. System z decision-makers and managers must commit to such an objective and safeguard the provision of adequately skilled personnel to optimize such a considerable TCO cost line item (I.E. MLC @ ~20-35% of System z TCO). In an ecosystem with technical resources including DBA, Systems Programmer, Capacity Planner, Application Personnel, Performance Tuning, et al, why wouldn’t there be a specialist Software Cost Manager?

Let’s consider how even an inexperienced System z user can maintain a baseline of System z MLC costs, even with organic CPU capacity growth of 10% per annum:

  • System z Server Upgrade: Higher specification CPU chips or Technology Transition Offering (TTO) pricing metrics deliver 10%+ cost per MSU benefits.
  • System z Specialty Engines: Over time, more and more application workload can be offloaded to zIIP processors, with no sub-capacity MLC software charges.
  • System z Software Version Upgrades: Major subsystems such as CICS, DB2, IMS, MQSeries and WebSphere deliver opportunity to lower cost per MSU; safeguard such function exploitation.
  • Application Tuning: Whether SQL, COBOL, Java, et al, or the overall I/O subsystem, safeguard that latest programming techniques and I/O subsystem functions are exploited.
  • New Application Deployment: As and when possible, deploy new or convert existing workloads to benefit from the optimal MLC pricing metric; previously zNALC, nowadays zCAP.
  • Technical & Commercial Skills Currency: Safeguard personnel have the latest System z software pricing knowledge, ideally from an independent 3rd party such as Watson & Walker.

In conclusion, as householders we have the opportunity to optimize our cost expenditure, choosing and switching between various major cost items such as financial, utility and vehicle products. As System z users, we don’t have that option, only IBM provide System z servers and associated base architecture, namely the most expensive MLC software products, z/OS, CICS, DB2, IMS and WebSphere/MQ. However, just as we manage our domestic budgets, reducing power usage, optimizing vehicle TCO and getting more bang from our buck for financial products various, we can and must deliver this same due diligence for our System z MLC TCO. With industry averages of ~$500-$1000 per MSU for z/OS MLC software and associated annual expenditure measured in many millions, why wouldn’t any System z user look to deliver 10%+ cost per MSU optimization, year-on-year for their organization?

Clearly the cost of doing nothing in this instance, is significant, measured in magnitudes of millions, each and every year. Hence for System z MLC TCO optimization, looking after the Pennies is more than worthwhile, while the associated benefit of the Pounds, Euros or Dollars looking after themselves is arguably priceless.

Big Data: Is the zSeries Mainframe A Viable Platform?

Noting that ~80% of global corporate data is still managed by IBM Mainframes, doesn’t it make sense that processing this mission critical data should remain local, whenever practicable and pragmatic?

Industry Analyst’s estimate that 90%+ of existing IT budget expenditure is expended on the maintenance of existing applications and their supporting infrastructure. A significant factor is the siloed, duplicated and complex nature of these existing IT environments. Repeating this often unnecessary data duplication and processing for big data implementations will only exacerbate this significant TCO expenditure. Therefore it is of primary importance to consider big data from a strategic rather than a purely expedient tactical perspective. Put another way, if big data could be accessed and processed by the incumbent IBM Mainframe environment, why create another silo environment, requiring more servers, storage, software and associated maintenance expenditure?

It is estimated that each and every day another ~2.5 Exabyte’s (2.5 quintillion bytes) of data is created, meaning that ~90% of electronic data stored, has been created in the last two years alone. This data comes from numerous sources, largely Internet and mobile telephony based, including social media sources, digital pictures and videos, financial transaction records, cell phone generated, naming but a few.

Industry Analyst’s estimate that only ~1% of global data is currently analysed, leaving massive scope for growth in this functional area, namely big data analytics. Obviously this scope dictates exponential and arguably uncontrolled growth in deployment of big data analytics solutions, generating significant risk that big data projects will lack management oversight, spiralling out of control from a cost viewpoint.

It therefore follows that big data initiatives require careful and strategic planning, not only for short-term immediate requirements, but also for future big data projects that can already be perceived and forecasted. Moreover, in addition, there needs to be a strategic, scalable, cost efficient and secure infrastructure in place, managing the interrelationship and interdependencies, between mission critical data stored on the IBM Mainframe and big data being created from Internet and mobile technologies.

Without such a diligent and structured management framework, IT infrastructure expenditure costs (TCO) will increase, efficiency reduce, with the inevitable consequence of siloed environments, with duplication of resources, namely servers, software, storage, et al. As always, we must always apply lessons learned from past experiences to avoid these inefficiencies.

Hadoop is seemingly the big data buzzword, being an open source software framework for storing and processing big data in a distributed environment on large clusters of commodity hardware. Ultimately Hadoop delivers two primary functions, massive data storage and faster in memory I/O processing.

In conclusion, the underlying question remains, can mission critical IBM Mainframe data be “coupled” with big data, typically originating from Internet and mobile platforms, to deliver an integrated single image view of customer and/or product data, for business benefit?

IBM offers an integrated solution, namely the zEnterprise Analytics System (I.E. 9700, 9710), comprising hardware (E.g. z196/zEC12 or z114/zBC 12 Server plus DS8870 Disk) and software (E.g. Optimized z/OS software stack), combined with optional services. Primarily data analytics is delivered by the IBM DB2 Analytics Accelerator solution, incorporating Netezza 1000 product function, allowing for intelligent and rapid in-memory data analytics via the DB2 RDBMS. Therefore existing zSeries Mainframe customers can supplement their current IBM Mainframe infrastructure with the IBM DB2 Analytics Accelerator solution, while the realm of possibility exists for a zSeries Mainframe to be deployed for new workloads, via the zEnterprise Analytics System.

Resource and cost efficiencies are delivered by combining z/OS and Linux on zEnterprise solutions. Data transfer is reduced by keeping data analytics in the same environment as the mission critical source data (I.E. z/OS) using hypersockets to process the data between the IBM z/OS and Linux on zEnterprise systems. Overall TCO efficiencies are delivered by optimizing lower cost Linux on zEnterprise systems resources, where for Sub Capacity z/OS customers, no software charges will be incurred for associated CPU processing. Therefore leveraging from existing zEnterprise infrastructure resources, including people and processes to deploy and support expanding data analytics requirements.

zSeries Mainframe big data analytics solutions, whether via the packaged zEnterprise Analytics System or via the IBM DB2 Analytics solution deliver benefits including:

  • Optimized I/O Processing: Reducing the complexity and cost of data storage and associated processing by bringing data transformation and analytic processes to the data origin (I.E. zSeries Mainframe)
  • Enterprise Wide Data Availability: Safeguarding operational data accessibility to many users in a timely and cost efficient manner without impacting core business processes
  • Near Real Time Data Processing: Delivering near real time operational analytics with minimal latency and superior Quality of Service (QoS) attributes (I.E. RAS – Reliability, Availability, Serviceability)

Syncsort also provide their DMX-h ETL solution to integrate IBM mainframe data with Hadoop technologies. Syncsort DMX-h ETL incorporates a library of Use Case Accelerators to implement common ETL tasks including Mainframe data access, change data capture (CDC), joins, web log aggregations, et al. Implementing a more traditional ETL approach, offloading big data batch workload from the Mainframe to Hadoop platforms, reducing Mainframe MIPS accordingly. Obviously ETL solutions have a long-term history, typically associated with Business Intelligence, Data Warehouse, et al. One must draw one’s own conclusions as to whether ETL solutions contribute to the complexity and cost of managing mission critical business data…

From a business viewpoint, big data analytics delivers benefits, including but not limited to:

  • Optimized & Faster Decision Making: Performing real time analysis of customer transaction and activity data, feedback (E.g. survey and experience) data, et al, can dramatically reduce customer attrition, maintaining existing customer loyalty, applying these lessons learned for attracting new customers.
  • New Products & Services: Customer’s and associated market research have always provided valuable insight into driving innovation, but these traditional processes are time consuming and error prone. Rapidly analysing real life customer data from Internet and mobile sources, delivers an opportunity to offer a new product and/or service, seemingly specialized to their personal individual requirements.
  • Cost Reduction: Performed well, clearly big data analytics can deliver significant cost reduction for the business, reducing product/service development time, while retaining existing customers and attracting new customers. However, done badly, data analytics could be a significant drain on the IT expenditure budget

As always, the zSeries Mainframe delivers an integrated, scalable, secure and cost efficient solution for big data initiatives, even Hadoop, typically perceived as a Distributed Systems solution. Without doubt, big data solutions will be implemented by each and every major global company in the short-term, while pragmatic and careful planning will reduce the associated IT implementation and administration cost. With a legacy of several decades or more delivering enterprise wide solutions, arguably seasoned IBM Mainframe personnel are ideally placed to participate in the design and delivery of big data analytics projects!