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Le Groupe NRB : Le meilleur partenaire pour votre z/modernisation

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Le Groupe NRB : Le meilleur partenaire pour votre z/modernisation

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Le Groupe NRB partage avec vous les présentations données le 24 novembre à Paris lors de la deuxième édition française de son Mainframe day.
Le thème de cette édition :
Le Groupe NRB : Le meilleur partenaire pour votre z/modernisation.

Le Groupe NRB partage avec vous les présentations données le 24 novembre à Paris lors de la deuxième édition française de son Mainframe day.
Le thème de cette édition :
Le Groupe NRB : Le meilleur partenaire pour votre z/modernisation.

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Le Groupe NRB : Le meilleur partenaire pour votre z/modernisation

  1. 1. 1 MAINFRAME DAY ÉDITION 2022
  2. 2. 2
  3. 3. 3 → INTRODUCTION Frédéric Deboudt - CEO Trigone → LE MAINFRAME AU COEUR DE L’INNOVATION Justine Mawet - Head of Innovation & Business Consulting NRB Benjamin Brandt - Information System Architect NRB → DÉMYSTIFICATION DE LA MISE À DISPOSITION EN TEMPS RÉEL DES DONNÉES DU Z Hélène Lyon - zArchitect NRB → DES FRAMEWORKS DE DÉVELOPPEMENT JAVA S’INTÉGRANT AVEC COBOL & PL/I Sébastien Georis - Information System Architect NRB → CONNECTER LE CI/CD MAINFRAME AU MONDE OUVERT Benoît Ebner - Mainframe engineer NRB → ARCHITECTURE D’INTÉGRATION Z ET PRÉSENTATION D’UN USE CASE Sébastien Georis - Information System Architect NRB Benjamin Brandt - Information System Architect NRB → COMMENT MODERNISER L’IA AVEC L’IBM Z16 Guillaume Arnould - Data & AI on IBM z Technical Sales – Client Engineering - EMEA → TENDANCES DE L’IT & MAINFRAME Henri Gilabert - Expert of «Tendances de l’Informatique» AGENDA
  4. 4. 4 INTRODUCTION Frédéric Deboudt - CEO Trigone
  5. 5. 5 5
  6. 6. 6 Introduction NRB Mainframe business roadmap extending beyond 2035, fueled by: ❭ Continued investment in state-of-the-art hardware and software ❭ Continued investment in people and expertise, with in-house training at the « NRB zAcademy » and recruitment ❭ Innovation and application modernization, supported by the « NRB Software Factory » ❭ Sustained market growth for modernization of legacy Mainframe application Business development ❭ Consolidation of NRB’s position as top Mainframe service provider in Belgium ❭ Footprint expansion in the French market, with early successes in 2021-2022, building up strong interest in NRB Mainframe services CSR and Green-IT ❭ Focus on energy efficiency and self-sufficiency (wind turbine, extension of the solar park) ❭ Inherent cost-efficiency of the Mainframe is key in this strategy
  7. 7. 7 LE MAINFRAME AU COEUR DE L’INNOVATION Justine Mawet - Head of Innovation & Business Consulting NRB Benjamin Brandt - Information System Architect NRB
  8. 8. 8 AGENDA I n n o v a t e w i t h Z S y s t e m s NRB Innovation Hub introduction Our co-creation approach They 6 key success factors of innovation Digital platform as an innovation booster Q&A
  9. 9. 9 Hello, nice to meet you !
  10. 10. 10 The NRB innovation hub is part of NRB. We scan today's behaviors & technologies to respond to new needs in a creative and innovative way. We are a business booster We help our customers move forward faster by providing optimal skills, processes and technologies. About us EXPLORE EXPLOIT
  11. 11. 11 O u r d e p a r t m e n t m i x e s t e c h n i c a l a n d b u s i n e s s k n o w l e d g e i n o r d e r t o c o v e r t h e e n t i r e v a l u e c h a i n Digital Transformation and Innovation I n n o v a t e w i t h Z S y s t e m s Digital Transformation & Innovation William Poos IT & Digital Strategy Bertrand Josse Integration Solutions & IoT Jean-Marc Herzet Analytics (Models +AI) Leila Rebbouh BI & Data Management Didier Brabant Innovation Business Consulting Justine Mawet Cloud Native Application Olivier Blanpain Scrum Practice Manon Filon 11 FTE 25 FTE 66 FTE 15 FTE 5 FTE 7 FTE 7 FTE
  12. 12. 12 T h i s o r g a n i z a t i o n i s a l s o r e i n f o r c e d b y c r o s s - f u n c t i o n a l s k i l l s t o e n s u r e m a x i m u m v a l u e g e n e r a t i o n f o r o u r c u s t o m e r s Digital Transformation and Innovation I n n o v a t e w i t h Z S y s t e m s Vincent Jassogne – PreSales, EA and Azure Architecture Benjamin Brandt – Innovation & Cloud Architect Fabian Delhaxhe – Digital Marketer & Squad Creator Olivier Lefèvre – Mister Smart Cities Bruno Franki – Innovation & Cloud Architect Olivier Fekenne Architecture
  13. 13. 13 Our ecosystem I n n o v a t e w i t h Z S y s t e m s
  14. 14. 14 Our mission & objectives I n n o v a t e w i t h Z S y s t e m s Our Mission • To strengthen the innovation of the group and its customers, by mobilizing key competencies and information, and by evolving our framework of actions Our objectives • To support external customers’ innovation • Strengthen the service offer of the group's entities - reduce the Time2market • Build a Go-2-market that enhances cross-sector synergies with subsidiaries and partner clients • Guarantee the orchestration and/or participation of the group within the main ecosystems • Generate new revenues through the creation of new integrated services / group digital platform – IP • Create and support intra-group startups responsible for integrated services • Evolve our culture (learning, collaboration, agile...) • Attract young talent • Be 25% profitable explore exploit
  15. 15. 15 We innovate by co-creating
  16. 16. 16 Our co-creation model | TRL – TECHNOLOGY READINESS LEVEL Observation Concept formulation Experimental evidence Laboratory Validation Representative environment validation Prototype demonstration in a representative environment Demonstration of a prototype in an operational environment Qualification of the real system in an operational environment Real-world system validation in real environment - successful operational missions 1 2 3 4 5 6 7 8 9 Phases Actors Financing I n n o v a t e w i t h Z S y s t e m s
  17. 17. 17 Observation Concept formulation Experimental evidence Laboratory Validation Representative environment validation Prototype demonstration in a representative environment Demonstration of a prototype in an operational environment Qualification of the real system in an operational environment Real-world system validation in real environment - successful operational missions 1 2 3 4 5 6 7 8 9 Phases Actors Finan-cing Basic and applied research Universities/ Governments I n n o v a t e w i t h Z S y s t e m s Our co-creation model | TRL – TECHNOLOGY READINESS LEVEL
  18. 18. 18 Our co-creation model | TRL – TECHNOLOGY READINESS LEVEL Observation Concept formulation Experimental evidence Laboratory Validation Representative environment validation Prototype demonstration in a representative environment Demonstration of a prototype in an operational environment Qualification of the real system in an operational environment Real-world system validation in real environment - successful operational missions 1 2 3 4 5 6 7 8 9 Phases Actors Finan-cing Basic and applied research Advanced research and technology demonstration Universities/ Governments Private/Public Partnerships Co Creation Co Financing NRB/Customers I n n o v a t e w i t h Z S y s t e m s
  19. 19. 19 Observation Concept formulation Experimental evidence Laboratory Validation Representative environment validation Prototype demonstration in a representative environment Demonstration of a prototype in an operational environment Qualification of the real system in an operational environment Real-world system validation in real environment - successful operational missions 1 2 3 4 5 6 7 8 9 Phases Actors Finan-cing Basic and applied research Advanced research and technology demonstration Qualification and technological operationality Universities/ Governments Private/Public Partnerships Private Co Creation Co Financing NRB/Customers I n n o v a t e w i t h Z S y s t e m s Our co-creation model | TRL – TECHNOLOGY READINESS LEVEL
  20. 20. 20 The 6 key success factors of innovation
  21. 21. 21 1| Business Strategy The real innovation is not technological, it aims at resolving “real” business problems. I n n o v a t e w i t h Z S y s t e m s
  22. 22. 22 I n n o v a t e w i t h Z S y s t e m s ▪ Sectorial vision – understanding challenges ▪ Understanding Customer Strategy - Think Tank ▪ Definition of business strategy and OKRs ▪ Business model transformation ▪ Value Proposition Design/Testing ▪ Customer segment ▪ Partnership and operating model ▪ Functional optimizations ▪ Revenues/costs ▪ Focus on real problems: Design Thinking ▪ Customer journey optimization ▪ Co-Creation on TRL < 7 ▪ Study of ecosystems (participation, orchestration) – detection of good partners Business Strategy
  23. 23. 23 2| Target Operating Model We co-elevate and cross the finish line all together. I n n o v a t e w i t h Z S y s t e m s
  24. 24. 24 I n n o v a t e w i t h Z S y s t e m s ▪ Perimeter - When what needs to be done or how it is done is vague and unpredictable ▪ Identify multidisciplinary teams of 7 to 10 people supporting the strategy by assigning them OKRs ▪ Manage dependencies between teams via scrum of scrum ▪ Turning middle managers into entrepreneurs ▪ Transform « direct and control » culture to « empower and support » ▪ Deploy iteratively within the organization ▪ Building a Transformation Team to Address Irritants - CoE ▪ Review HR process - training, assessment and recruitment processes Business Strategy TOM
  25. 25. 25 3| Information Management Information to fuel innovation I n n o v a t e w i t h Z S y s t e m s
  26. 26. 26 I n n o v a t e w i t h Z S y s t e m s ▪ Vision: Create a centralized data environment and analytics capability in a secure and documented manner that provides data consumers with reliable insights and insights ▪ Priorities: one version of reality, governed trusted data, adapted visualization tools ▪ Projects : Data catalog, Data Lake et company wide DWH, Data Masking / GDPR, Visualisation ▪ Transformation: Using cloud levers to • Integrate new data sources –internal and external • Accelerate consolidation load times • Accelerate decision-making • Supporter les uses case « near real time » • Faster/Bigger/Cheaper Business Strategy TOM Information
  27. 27. 27 4| Technology Multi Cloud Digital Platform zSystem at the heart of the digital platform I n n o v a t e w i t h Z S y s t e m s
  28. 28. 28 I n n o v a t e w i t h Z S y s t e m s ▪ Incremental, agile construction ▪ Growth at the rate of value generated ▪ Multitech service set – ux, twins, microservices, AI, IOT, ... • UX - language, text, image, ... • Ecosystem and Open API • Third Party Development Platform • Microservices - Kubernetes, azure functions, lambda • DATA/AI – Data Lake, Machine Learning • Legacy Systems Integration ▪ Reusable component library • Infrastructure component (security, identity & access management, event processing, … ) • Reusable business capabilities/component (billing, twins, …) • Specific components specific to a particular offer • Cross-sector digital offer Technology Business Strategy TOM Information
  29. 29. 29 5| Culture and communication Everyone is an innovator I n n o v a t e w i t h Z S y s t e m s
  30. 30. 30 I n n o v a t e w i t h Z S y s t e m s ▪ Inclusive approach - gamification ‒ Bronze: participation in a scrum training, ideation, ... ‒ Silver: participation in a sprint of definition of value proposal / realization of a POC ‒ Gold: achievement of a successful MVP ▪ Showcase your innovation - services, skills, method, sector issues, implementation ▪ Sparking the idea – Id8or by providing collective intelligence tool ▪ Innovation functions / career Management: innovation manager, product owner, agile coach, … ▪ Setting up creative spaces ▪ Organize innovation sprints with partners and internal challenges Culture Communication Technology Business Strategy TOM Information
  31. 31. 31 6| Compliance Compliant by design
  32. 32. 32 I n n o v a t e w i t h Z S y s t e m s ▪ Early integration of all company functions ‒ DPO , Communication, Legal, Marketing, Risk Officer ▪ Definition of Explore / Exploit criteria – Insure smooth transition ‒ Accountability ‒ SLA ‒ Environments ‒ Architecture Culture Communication Technology Business Strategy TOM Information Compliance SUCCESS
  33. 33. 33 Digital platform as a booster to create a real added value by capitalizing on the core business
  34. 34. 34 Mainframe Day 2016 A (short) look to the past
  35. 35. 35
  36. 36. 36 Z Systems in the Digital Platform Stability in an agile world
  37. 37. 37 ZSystems in the Digital Platform ▪ Stable ▪ Robust ▪ Reliable ▪ Predictable ▪ Fast ▪ Agile ▪ Connected ▪ Enjoyable Core System on Z Digital Platform
  38. 38. 38 ZSystems in the Digital Platform Core is accessible and opened Core leverage other technologies to innovate Core System on Z Digital Platform
  39. 39. 39 Digital Platform - What’s in it ? An ecosystem of technologies
  40. 40. 40
  41. 41. 41 Digital Platform components ▪ Mobile Applications ‒ Native (IoS / Android) ‒ Flutter ▪ Web sites / applications ‒ Angular ‒ React ▪ End-user devices ‒ Assistants ‒ Virtual Reality headsets ▪ Data Lake ‒ Store ‒ Purpose-built DB ‒ SQL DBs ‒ Object Storage ‒ GraphDB ‒ … ▪ Analytics ‒ Exploit & understand ‒ AI /ML ‒ BI ‒ Streaming Analytics ‒ …
  42. 42. 42 Digital Platform components ▪ Microservices Apps ‒ Serverless ‒ AWS Lambda ‒ Azure Functions ‒ Containers ‒ Kubernetes ‒ Java Spring Boot ‒ .net core ‒ NodeJS ‒ Python ▪ Core systems ‒ Packaged ‒ SAP, GuideWrire, … ‒ ESB ‒ SAG, Mule, Talend, … ‒ Mainframe ‒ Z systems
  43. 43. 43 Digital Platform components ▪ Internet of Things (IoT) ‒ For Home ‒ Lights, Smoke detector, camera,… ‒ For Business ‒ Self Driving truck, harvester, … ▪ Ecosystems ‒ Expose services ‒ Consume external services ‒ Monetization ‒ Co-creation ‒ Open Data ‒ Networking connectivity ‒ Rules engine ‒ Fleet management
  44. 44. 44 Digital Platform components ▪ Internet of Things (IoT) ‒ For Home ‒ Lights, Smoke detector, … ‒ For Business ‒ Self Driving truck, harvester, … ▪ Ecosystems ‒ Expose services ‒ Consume external services ‒ Monetization ‒ Co-creation ‒ Open Data ‒ Networking connectivity ‒ Rules engine ‒ Fleet management
  45. 45. 45 Digital Platform components ▪ Identity Provider ‒ Essential building block ‒ Build applications faster ‒ Standard protocol ‒ Open Id Connect (OIDC) ‒ Connect with social medias (Facebook, Google, Tweeter, …) ▪ Transversal Services ‒ Monitoring ‒ Alerting ‒ CI/CD ‒ Compliance
  46. 46. 46
  47. 47. 47 Digital Platform - Where to build it ? All things distributed
  48. 48. 48 Digital Platform is distributed. It span across your on-premises, public cloud and partner’s systems. Choose the right tool for the job.
  49. 49. 49 Digital Platform – why the cloud Powerful Security Reliability Tools World-wide footprint Scalability Pay only for what you use. Adapt resources automatically, If need be. Enjoy the power of image recognition, user management, centralized log systems and many others. Use those tools to leverage your business. Thanks to isolated and replicated solutions all over the world, you can build high quality disaster recovery systems. With the global footprint, you deploy applications closest to your customers. To keep their digital journey at its best shape The security OF the cloud is guaranteed by the providers. Plus, they provide you with up-to-date tools so you can guarantee the security IN the cloud to your customers Use the amount of resources you need for your business. Use the right resource for the right job.
  50. 50. 50 Digital Platform – Azure Services
  51. 51. 51 Digital Platform – AWS Services
  52. 52. 52 Build your blocks Combine services & reduce time-to-market
  53. 53. 53 Digital Transformation & Innovation Justine.Mawet@nrb.be Head of Innovation & Business Consulting Benjamin.Brandt@nrb.be Information System Architect William.Poos@nrb.be Head of Digital Transformation & Innovation
  54. 54. 54 DÉMYSTIFICATION DE LA MISE À DISPOSITION EN TEMPS RÉEL DES DONNÉES DU Z Hélène Lyon- zArchitect NRB
  55. 55. 55 ▪Access from Distributed / Cloud Apps to Core Data on z/OS Hybrid Data Integration Need On-Premises Dedicated Local Mainframe Traditional IT Private Hybrid Cloud Off-Premises Multi-Cloud Public NECS 4
  56. 56. 56 Hybrid Data Integration Patterns Access from Distributed / Cloud Apps to Core Data on z/OS READ-ONLY DB Duplication & Transformation including changes Hybrid Integration Data Lake Near real time copy of data Near Real Time Second to minutes old depending on change apply PUSH Data change Batch copy of data READ-ONLY DB Duplication & Transformation Static data Data Warehouse Data Lake One day or more old PUSH Data Near Real Time Second to minutes old depending on event subscription PUSH Apps event Event-based Architecture for Integration Pub/sub of events Data synchronization thru Apps Events Real Time PULL Data Synchronous Integration with zApps No data duplication API Inbound Real Time PULL Data Hybrid DB access using SQL No data duplication Data virtualization & federation Data Latency Use Case & Techno
  57. 57. 57 ▪ Pull versus Push For real time data access and updates, data is pulled from IMS Applications For IMS DB or Db2 For access without « real time » need, event publication thru « PUSH » mechanism Replication of IMS Databases to relational with IBM Change Data Capture (CDC) Creation « Application Event » for Pub/Sub Kafka solution Access to aggregated data with datawarehouse technologies NRB – zData Access Best Practices Method Data Access Level Access Type Data validity Technology PULL Real Time Read IMS Transactionality for IMS DB, Db2, MQ ressources API on top of new IMS PLI/Java Data Service transaction Write IMS Transactionality for IMS DB, Db2, MQ ressources API on top of Business Service (New framework) or existing applications PUSH Near Real Time Read Thru apply of updates Data Event with IBM CDC Use case: IMS DB to SQL based format Near Real Time Read Thru subscription of event & apply updates Application Event with IBM MQ & Kafka 5 Minutes Read Thru apply of updates and transformation Data Event with IBM CDC & post processor to build « enterprise canonical view » in ODS Previous Day Read Static aggregated data Data Warehouse
  58. 58. 58 ▪PULL – zApps & API Access to 100% of z/OS data based on business need On demand creation of API with new IMS Apps to give access to IMS data without reusing existing business logic ▪PUSH "Data Event” – with IBM CDC Replication in Db2 on z/OS, or Oracle on distributed Access limited to the Dbs managed by CDC ▪PUSH "Application Event" - with Confluent Kafka Event publication when some IMS DB are updated MQ message integrated in two phase commit Gateway between MQ & Kafka NRB – zData Access Best Practices … Distributed Apps Including home made apps, SalesForce, Guidewire, … CDC / Apply Raw Data (Oracle) POST- Process Conformed Data (Oracle) Kafka Subscription SQL Queries Kafka IMS IMS DB Db2 subset CDC / Capture Db2 Raw Data CDC / Apply Db2 SP DLI SQL MQI SQL Service API
  59. 59. 59 New “Query Services” in IMS PLI ou JAVA ▪ Read-Only Access to “Real time” IMS data NOT replicated with CDC ▪ Components New “data services” to answer quickly to customer data need API Creation oriented “Query IMS” for a specific business need IMS Transaction creation reusing existing data access components Read-Only access to traditional IMS databases with DLI Calls ▪ Remark: Update access are still done with legacy IMS apps. IMS Data Service (New)
  60. 60. 60 ▪Read-Only Access to “Near real time” IMS data replicated in Db2 z/OS without leaving z ▪Components IBM CDC Db2 Native SP SQL only zIIP support – low cost ;) API managed by z/OS Connect to call Db2 SP New API & Db2 Native Stored Proc
  61. 61. 61 Teasing for NRB Mainframe Day 2023 ▪In 2022 - Hybrid Data Integration Need: Access from Distributed / Cloud Apps to Core Data on z/OS ▪In 2023 - Hybrid Data Integration Need: Access from Core IT Apps on z/OS to Distributed / Cloud Data On-Premises Dedicated Local Mainframe Traditional IT Private Hybrid Cloud Off-Premises Multi-Cloud Public
  62. 62. 62 DES FRAMEWORKS DE DÉVELOPPEMENT JAVA S’INTÉGRANT AVEC COBOL & PL/I Sébastien Georis - Information System Architect NRB
  63. 63. 63 NRB Java Framework PL/1 Cobol interoperability
  64. 64. 64 Agenda 1. NRB’s Application Architecture Evolution 2. NRB’s development framework overview 3. NRB’s development Java framework with interoperability
  65. 65. 65 ‘Historical’ Model To-be Domain 1 As-Is Domain 2 Domain 2 Domain Driven Design Model Transformation From historical to Domain Driven Design service-oriented architecture zApplications NRB’s Application Architecture Evolution
  66. 66. 66 zApplications NRB’s Application Architecture Evolution
  67. 67. 67 Agenda 1. NRB’s Application Architecture Evolution 2. NRB’s development framework overview 3. NRB’s development Java framework with interoperability
  68. 68. 68 zApplications NRB’s development framework overview NRB’s has build a framework the ease, standardize, accelerate the development of applications with a high level of reusability and avoid code duplication. Based on a common services models supporting all the service’s types of the zApps’ Evolved Architecture : IMS transactions, CICS programs, Business Services, Business Objects Services, Business Rules Services, Data Access Services and Utility Services. Abstract layer & services for all the aspect such as : ▪ Applicative context initialisation ▪ Services and operation metadatas ▪ Data Communication : IMS, CICS, MQ, Java Native Interface (interoperability) ▪ ODM ruleset execution ▪ Error handling ▪ Application audit & monitoring ▪ … The framework is available for Cobol, PL/1 and Java
  69. 69. 69 Agenda 1. NRB’s Application Architecture Evolution 2. NRB’s development framework overview 3. NRB’s development Java framework with interoperability
  70. 70. 70 zApplications NRB’s development framework with Java interoperability
  71. 71. 71 Performances test conditions ▪ Invoking mirrored applications written in PL/1 and in Java ▪ Running scenarii simulating realistic business behaviours ▪ Gradual increase of the number of users and number of API calls ▪ Running in a development environment with limited capacity ▪ z/OS Platform up to date (z15 / z/OS 2.3 / uncapped zIIP processors) 0 50 100 150 200 250 300 350 400 z/OS Connect average response time (ms) IMS average response time (ms) Total CPU time (sec) % CP Processor usage % zIIP Processor usage Frameworks performance tests PL/1 Framework Java Framework Framework # API Calls z/OS Connect average response time IMS average response time Total CPU time % CP Processor usage % zIIP Processor usage PL/1 5444 336 ms 196 ms 73,06 secs 100 0 Java 5443 253 ms 65 ms 84,64 secs 18 82 Java framework performances test
  72. 72. 72 Need or Concern Answer Enable the development of Java applications on the IBM z platform Java framework under IMS or CICS Support to be-architecture patterns & architectural concepts Alignment on the architectural patterns & concepts Enable interoperability between different languages Interoperability using Java Native Interface Benefit from existing transactionality and security on the platform ▪ Transactionality : Java runs under the authority of IMS or CICS ▪ Security : via RACF orTop Secret Reduction of run costs through the usage of zIIP type processors 82 % of workload is zIIP processors eligible Increase developments speed and reduce time to market Some aspects should no longer be managed by developers and they can only focus on the business code to be developed + DEVOPS. Adequate performances z15 hardware z/OS 2.3 Java Framework performance is 3X faster than PL/1 Java framework : Answers to needs & concerns
  73. 73. 73
  74. 74. 74 CONNECTER LE CI/CD MAINFRAME AU MONDE OUVERT Benoît Ebner - Mainframe engineer NRB
  75. 75. 75 Agenda 1. How to integrate Java deployment on mainframe? 2. How to control and pilot production deployment? 3. How to implement a quality gate?
  76. 76. 76 How to integrate Java deployment on mainframe?
  77. 77. 77 C o n s t r a i n s a n d g o a l s Context H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ? • We develop a new framework: PL1 – Java • We want to keep the Java source workflow in the “normal Java way” (Git, Jenkins, …) • The deployment on the mainframe need to be seamless for Java developers • We want to use the same CI/CD pipeline for other mainframe related objects (zOS Connect, ODM, …) • We want to benefit of the NRB private cloud (NECS)
  78. 78. 78 1 T h e J a v a d e v u s e t h e i r p r e f e r r e d e d i t o r a n d s t o r e t h e c o d e i n a G i t l a b i n s t a n c e Flow overview H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ? NECS IntelliJ Netbeans VSCode Gitlab
  79. 79. 79 2 T h e J A R g e n e r a t e d a r e s t o r e d i n N e x u s Flow overview H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ? NECS IntelliJ Netbeans VSCode Gitlab Nexus
  80. 80. 80 3 E v e r y t h i n g i s p i l o t e d b y J e n k i n s Flow overview H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ? NECS IntelliJ Netbeans VSCode Gitlab Nexus Jenkins
  81. 81. 81 4 J e n k i n s i n i t i a t e t h e d e p l o y o n t h e m a i n f r a m e l p a r Flow overview H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ? NECS IntelliJ Netbeans VSCode Gitlab Nexus Jenkins Mainframe ISPW rest API (ISPW Jenkins plugin)
  82. 82. 82 J e n k i n s f i l e : L a u n c h a d e p l o y m e n t o f a n a s s i g n m e n t Flow overview H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ?
  83. 83. 83 How to control and pilot production deployment?
  84. 84. 84 C o n s t r a i n s a n d g o a l s Context H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? • We want to have a full control on MEP • Check if all the element is “promotable” • If we have a problem to promote one element of a change, return to previous state for this change • If something failed, warn the duty • Communicate the result of MEP to all the people involved • Release management • System team • Developer team • Operator team • The trigger for the promote is Control/M
  85. 85. 85 H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? P y t h o n What tool do we use • Works seamlessly on mainframe • More capabilities than REXX • RESTapi call • All Python plugins work on mainframe • Debug online • Call Python with shell script in BPXBATCH Z O A U ( I B M Z O p e n A u t o m a t i o n U t i l i t i e s ) • Add MVS function on USS Shell command line, Python and Java. • Execute MVS command (normal or authorized) • Dataset manipulation • JES utilities (submit, cancel, list…) • Console, operator utility • … • Use here to call IBM System Automation and send WTO. P y t h o n + Z O A U c a n r e p l a c e a p a r t o f y o u r J C L , R E X X a n d e x t e n d m a i n f r a m e c a p a b i l i t y
  86. 86. 86 1 C o n t r o l - M s u b m i t a B P X B A T C H t o t r i g g e r t h e M E P p r o c e s s Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? CTRL/M Python BPXBATCH
  87. 87. 87 2 P y t h o n w o r k w i t h I S P W / R e s t A P I t o d o t h e M E P Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? CTRL/M Python ISPW BPXBATCH rest API
  88. 88. 88 E x a m p l e : G e t a l l t h e s o u r c e s r e a d y t o p r o m o t e t o p r o d u c t i o n Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ?
  89. 89. 89 3 P y t h o n u s e Z O A U t o w o r k w i t h I S A t o s e n d t h e s t a t u s t o t h e o p e r a t o r Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? CTRL/M Python ISPW BPXBATCH rest API ISA ZOAU
  90. 90. 90 S a m p l e o f a I B M S y s t e m A u t o m a t i o n c a l l Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ?
  91. 91. 91 4 A l l s t a t u s e m a i l i s s e n d Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? CTRL/M Python ISPW BPXBATCH rest API ISA ZOAU Email email report
  92. 92. 92 Flow overview H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ? E m a i l s a m p l e : p r o m o t i o n a n a l y s i s
  93. 93. 93 How to implement a quality gate?
  94. 94. 94 W h a t w e t r y t o i m p l e m e n t ? Context H o w t o i m p l e m e n t a q u a l i t y g a t e ? • Before push a source to acceptance level we want to be sure: • The quality control is done • The change is linked to a correct demand • Maximize automatization of the process • If no problem detected ➔ don’t block the promotion • Otherwise: use Teams to warn the Quality Control Team
  95. 95. 95 1 c a l l P o w e r A u t o m a t e w i t h a W e b h o o k Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ? Mainframe ISPW Microsoft Power Automate Office 365 cloud Webhook
  96. 96. 96 W e b h o o k p a n e l Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ?
  97. 97. 97 2 c h e c k i f t h e e l e m e n t i s a l r e a d y v a l i d a t e d Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ? SharePoint Microsoft Power Automate Mainframe ISPW Office 365 cloud Webhook
  98. 98. 98 3 c h e c k i f t h e c h a n g e i s c o v e r e d b y a v a l i d t i c k e t Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ? ITSM Jira SharePoint Microsoft Power Automate Mainframe ISPW Office 365 cloud Webhook
  99. 99. 99 4 I f s o m e t h i n g m i s s i n g , p u s h a m e s s a g e i n a T e a m s g r o u p Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ? ITSM Jira SharePoint Microsoft Power Automate Teams Mainframe ISPW Office 365 cloud Webhook
  100. 100. 100 S a m p l e o f a T e a m s i n t e r r a c t i o n Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ?
  101. 101. 101 5 u s e I S P W r e s t A P I t o r e l e a s e o r c a n c e l t h e p r o m o t i o n Flow overview H o w t o i m p l e m e n t a q u a l i t y g a t e ? ITSM Jira SharePoint Microsoft Power Automate Teams Mainframe ISPW Office 365 cloud Webhook rest API
  102. 102. 102 Modern mainframe development can be connected to open environments. You have all in your hand to unlock it!
  103. 103. 103 ARCHITECTURE D’INTÉGRATION Z ET PRÉSENTATION D’UN USE CASE Sébastien Georis - Information System Architect NRB Benjamin Brandt - Information System Architect NRB
  104. 104. 104 z/OS Connect EE The Mainframe intergration’s corner stone
  105. 105. 105 Truly RESTful APIs to and from your mainframe DevOps using z/OS Connect EE IMS CICS DB2 MQ … PL/1 zAPP Cobol zAPP Basic of z/OS Connect EE
  106. 106. 106 API Provider ➢ zAssets expositions: IMS transaction, CICS programs, MQ, Db2Services, … ➢ Exposition of real REST resources aligned with the enterprise data model ➢ Authorization using JWT token API Requester ➢ PL1 or Cobol applications calling external API from the digital platform, partners or government ➢ Secured connection using JWT token for authorisation Common ➢ Exploitation of SMF records 123 v2 for auditing & monitoring ➢ Usage of Omegamon for JVM for system monitoring ➢ Secured using z/OS Address Space protection (RACF, TSS) , certificates, IP Stack and NetAccess TCP/IP TLS Secured Connection, Usage of Policy Agent, … z/OS Connect EE usage @NRB
  107. 107. 107 Z in the Digital Platform The Mainframe intergration’s corner stone
  108. 108. 108 • Client Layer ➢ for customers, partners, employees, … • Integration Layer ➢ Public & Private Cloud ➢ System API Gateway • A single gateway for all z/OS Assets ➢ Inbound & Outbound ➢ Secured Integration End-to-End View
  109. 109. 109 API Layers of the Digital Platform Connect the world
  110. 110. 110
  111. 111. 111 API Layers ▪ Channel Layer ‒ Specific to a consumer ▪ Experience Layer ‒ Specific to a product ▪ Capability Layer ‒ Generic APIs ▪ System Layer ‒ Contains Business logic
  112. 112. 112 Integration is key Sebastien.Georis@nrb.be Information System Architect Mainframe Modernization Benjamin.Brandt@nrb.be Information System Architect Digital Transformation & Innovation
  113. 113. 113 COMMENT MODERNISER L’IA AVEC L’IBM Z16 Guillaume Arnould - Data & AI on IBM z Technical Sales Client Engineering - EMEA
  114. 114. Data and AI on IBM z How to modernize for AI with the IBM z16 Guillaume Arnould Data & AI on IBM Z - Expert IT Specialist IBM Client Engineering for Systems | EMEA November 22nd, 2022 | NRB Mainframe Days
  115. 115. La Banque Postale | Juin 2002 Agenda How to modernize for AI with the IBM z16 ? ❑ AI powered by IBM z16 ❑ Exploiting Integrated Accelerator for AI software stack Questions 115
  116. 116. AI powered by IBM z16 116 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation 116
  117. 117. https://newsroom.ibm.com/2021-08-23-IBM-Unveils-On-Chip-Accelerated- Artificial-Intelligence-Processor IBM Z AI & Analytics / IBM / © 2022 IBM Corporation 117
  118. 118. IBM Z AI & Analytics / IBM / © 2022 IBM Corporation 118
  119. 119. IBM z16 Value statement IBM Z AI & Analytics / IBM / © 2022 IBM Corporation Real-time Business insights at Scale Enhanced Data Security & Resiliency Hybrid Cloud with Intelligent Infrastructure Enable clients to infuse AI into every business transaction by seamlessly leveraging the IBM z16 hardware AI engine to accelerate inference on Z Leverage data and transactional gravity on Z to drive real-time AI infused insights in business-critical workloads, while meeting even the most stringent SLAs High throughput, low latency AI, in- transaction decision making before the opportunity has passed. Enhanced cost savings by prevent fraud and mitigate risk with greater accuracy by leveraging deep learning. Safely use personal, sensitive data for analytics and AI in-place within the security-rich IBM Z perimeter – with 100% encryption of all data Apply transactional system-level performance and availability to your analytics and AI workloads to deliver actionable, real-time insights Train anywhere and inference on Z capability enables customers to bring their existing AI investments along Flexibility for practitioners to leverage the tools they are accustomed to, and deploy on Z when beneficial Data federation capabilities through virtualization Improve Security, Data Privacy, IT Operations with AI Deploy advanced, explainable AI across the ITOps toolchain 119
  120. 120. IBM Z: Fully enabled platform for business intelligence Build and train anywhere Deploy on IBM Z Deploy on IBM Z and seamlessly exploit innovations across the stack to infuse AI in every single transaction. Train anywhere Public clouds, private clouds, on-premises, and hyperconverged systems. Organize Data Import data from different applications and sources Import Data Model & Data Prep Model Training Deploy Predict Business Applications 120 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  121. 121. Exploiting Integrated Accelerator for AI software stack 121 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  122. 122. A comprehensive technology stack designed for AI 122 CPU SIMD Hardware & Facilities Operating Systems, Container Env. Math Libraries, Compilers, Optimizations IBM DLC AI Frameworks and Runtime Optimizations IBM SnapML IBM Solutions for Data and AI Watson Machine Learning for z/OS Db2 AI for z/OS z/CX z/OS • Data and AI platform modernizes your data estate • ONNX/DLC enables choice with multiple frameworks • Deep Learning for granular and low latency insight • Support for inference containers • Optimizations of AI inference and pipeline execution AIOps IZOA Z APM Connect zAIU DB2 v13 SQL Data Insights IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  123. 123. 123 IBM Z IBM Z Integrated Accelerated for AI zDNN Library Tensor Flow Snap ML Deep Learning Compiler (ONNX) zADE library Watson Machine Learning for z/OS SQL Data Insights Cloud Pak for Data Db2 for z/OS IBM Z Anomaly Analytics Watson AIOps Db2 AI for z/OS Exploiter of IBM Z Integrated Accelerated for AI 123 How offerings leverage the IBM Z Integrated Accelerated for AI IBM developed accelerator library. Building block used by compiler and framework developers – not generally by clients. Open source and/or community freely available software. Except for zADE, these are often used directly by end users and other open-source sw. Full enterprise AI lifecycle solutions. Leverages the below building blocks but bring huge additional value. Products that embed AI solutions to provide insights used in the offering. IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  124. 124. TensorFlow on IBM z16 • Popular AI open source framework with a broad ecosystem. • Widespread industry adoption. • Highly popular • Develop, train and inference of deep neural networks. • Available on today’s IBM zSystems! • IBM is enhancing TensorFlow to exploit the z16 Integrated Accelerator for AI • Will feature transparent acceleration with no model changes. • Planned to be available initially through open beta late 2Q 2022. 124 ✓ Available for Linux environments ✓ z/OS Container Extensions (zCX) helps integrate Linux on Z applications with z/OS ✓ Run TensorFlow Docker images directly on zCX in proximity to z/OS workloads ✓ Available via IBM Container repository for trusted images. ✓ Manage model serving instances on IBM Z using popular AI frameworks IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  125. 125. 125 125 The IBM DLC (Deep Learning Compiler), optimized for performance and new libraries, generates a program from the model for execution on z/OS or Linux® on IBM z16 Use ONNX, an opensource tool for framework interoperability Models are converted to the ONNX interchange format Leverage zCX and run on zIIP engines Build and train model in any popular framework on any platform of your choice IBM Deep Learning Compiler Generated inference program ONNX interchange format Deploy on IBM z16 and IBM LinuxONE • Bring machine learning & deep learning models to IBM z16 with ONNX/DLC • Exploit IBM Integrated Accelerator for AI for best inference performance. • Repeatable practice for different vendors to leverage IBM z16 and Integrated Accelerator for AI Deploy on IBM z16 and IBM LinuxONE and infuse model into workload application Build and train anywhere – Deploy on IBM Z IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  126. 126. Deep Learning Compiler for Linux on Z environments • Stand-alone compiler docker image. • To be listed as ONNX-MLIR, the open- source partnership the DLC builds on. • Targeted at open-source or do-it-yourself pairings. • No packaged serving environment. • Pair easily with BentoML, FastAPI, etc.! • Exploit the z16 Integrated Accelerator for AI. • Supports C++, Java, Python APIs. • Code examples to be made available. • Will be available on the IBM Z and LinuxONE Container Registry. • Target for GA is May 31st. 126 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  127. 127. Watson Machine Learning for z/OS Online Scoring Community Edition • Community edition (free) scoring service for ONNX models, featuring IBM Deep Learning Compiler. • Rapid PoC capability – setup and deploy in 15 minutes! • Deploy models to z/OS Container Extensions. • Exploit the z16 Integrated Accelerator for AI. • Updates for z16 generally available on May 31st • Available under “trial code” here: https://ibm.biz/WMLzOSCE 127 z/OS System z/ OS Container Extensions WMLz Online ScoringService DLC Compiled Model Core Services (Model and Deployment Management) WMLz Base Core Services Deploy & Manage Inference REST endpoint IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  128. 128. Watson Machine Learning for z/OS 2.4 CICS COBOL and WMLz online scoring using ALNSCORE • Native deployment on ONNX models on z/OS. • zIIP eligible for inference. • Simplified AI integration for CICS® COBOL applications. • CICS COBOL applications can invoke ONNX models using standard CICS commands. • Provides features for optimal exploitation of IBM z16 Integrated Accelerator for AI. • Embeds IBM Deep Learning Compiler • Server-side micro-batching. • Numerous other models supported; provides model lifecycle management. • V2.4 generally available on May 31st COBOL Application z/OS System CICS Region COBOL Application Liberty JVM Server Program ALNSCORE WMLz Online Scoring Service DLC Inference Program Core Services (Model and Deployment Management) Watson Machine Learning for z/OS Deploy • PUT CONTAINER(…) CHANNEL(CHAN1) FROM (…) • LINK PROGRAM(ALNSCORE) CHANNEL(CHAN1) • GET CONTAINER(…) CHANNEL(CHAN1) • FROM (…) 128 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  129. 129. 129 Choose your own deployment path for Deep Learning models… When to choose TensorFlow on Z • Direct support of TensorFlow assets (models, pipelines). • Desire a consistent TensorFlow ecosystem experience. • Configure serving infrastructure to scale. • REST API overhead is acceptable. When to choose ONNX and IBM DLC • Optimized inference for many model types (e.g., PyTorch). • Enterprise scalability and support. • Embed inference tightly in- transaction. • Minimal application changes for native z/OS applications. Foundational Technologies ● SIMD Architecture ● Optimized Libraries ● Built on IBM Z IBM Deep Learning Compiler IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  130. 130. 131 AI on IBM Z Use cases IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  131. 131. Announced April 5, 2022 132 Available May 31, 2022 IBM z16 IBM Db2 13 for z/OS + Better together! TM IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  132. 132. AI agility: Business insights without data science skills • Power any Db2 for IBM z16/OS application with AI enhanced SQL • Uncover and monetize hidden insights within your data • Identify similarities, dissimilarities and correlations • Apply a single model across multiple questions • Minimize AI deployment complexity • No data science skills needed Trillions of transactions per day go through IBM z16 and that data is stored in our Db2 for IBM z16/OS engine. Assess whether a customer will churn. Clients can use built-in AI models to understand underlying semantics of the data Learn patterns in that data to identify fraud before the transaction closes. Mine data to determine whether to extend a loan to a customer. “Out-of-the box” AI can be exploited through Db2 for IBM z16/OS IBM z16 supports the most popular machine learning algorithms, providing our clients an AI cloak to help them improve processes and drive greater business value from the existing investment they have made. The IBM z16 platform empowers clients to mine their most valuable enterprise data 133 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  133. 133. 134 134 SQL Data Insights - Enabling Self-service AI Additional Value: • Provides interpretability • Exploits AIU acceleration • Operations on encrypted data – Provides hidden relationships and inferred meaning from data in your database – Reduces need for deep data science skills SELECT X.accountID, X.FirstName, X.LastName, X.openedDate, X.RewardPoints, ai_semanticCluster(X.accountID, ‘1234ABCD’, ‘4567EFGH’,’6789IJKL’) AS RiskScore FROM Data_Table X WHERE ai_semanticCluster(X.accountID, ‘1234ADCB’ 4567EFGH’,’6789IJKL’) > 0.0 ORDER BY RiskScore DESC IBM Z AI & Analytics / IBM / © 2022 IBM Corporation 134
  134. 134. 138 Semantic SQL Functions First set of AI Built-In Functions available in Db2 13 Cognitive Intelligence Query Functional Classification Functional Description Db2 functions semantic similarity and dissimilarities Entity Matching Recommendation • Matching rows/entities based on overall meaning (similarity/dissimilarity) • Suggest choices for incorrect or missing entities AI_SIMILARITY semantic Clustering Recommendation • Find entities/rows based on relationships between attributes in a given set • Example: Find animals similar to (lion, tiger, panther) AI_SEMANTIC_CLUSTER Reasoning Analogy Recommendation • Find entities/rows based on relationships between attributes • Example: Moon : Satellite :: Earth; ? AI_ANALOGY IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  135. 135. AI on IBM z16 : Designed for business insights and intelligent infrastructure 139 Enable a leading AI portfolio & ecosystem Watson Machine Learning for z/OS IBM Cloud Pak for Data Deploy advanced, explainable AI across the ITOps toolchain Enhance database performance with machine learning Data Privacy for Diagnostics Leverage machine learning to detect and redact PII from diagnostic dumps REAL TIME BUSINESS INSIGHTS Infuse AI in Real-time into Every Business Transaction INTELLIGENT INFRASTRUCTURE Improve Security, Data Privacy, IT Operations with AI Watson AIOps & IBM Z Anomaly Analytics Db2 AI for z/OS Watson® Machine Learning for z/OS Unprecedented AI inferencing performance for every transaction while meeting SLAs Db2 for z/OS® with SQL Data Insights Uncover hidden insights in Db2 for z/OS data Db2 Analytics Accelerator for z/OS Db2® Db2 Analytics Accelerator for z/OS Real-time insight from data at the point of origin IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  136. 136. IBM Z – An industry leader in optimized inferencing Business Analytics use cases - Provide consulting and services to Line of Business Db2 z/OS SQL Data Insight - Provides hidden relationships and inferred meaning from data in Db2 or other IBM Z data via DVM - Reduces need for deep data science skills - Minimizes complexity of infrastructure and tooling to deploy AI for your applications ML Performance - Library enhancements for ML performance - Optimization of AI inference and pipeline execution Software enablement for DL acceleration - zDNN is an AIU-accelerated library of primitives for deep neural networks. - ONNX/DLC enables multiple DL frameworks - TensorFlow enablement delivers acceleration in an industry-standard serving environment On-Chip engine for Deep Learning - Industry-first low latency in-transaction inferencing 140 IBM Cloud Pak for Data IBM Open Data Analytics for z/OS Optimized Data Layer Z Core (CPU) Watson Machine Learning for z/OS Libraries-Eigen, Open BLAS, etc. Z AI Unit (AIU) Neural Network Library - zDNN Db2 13 for z/OS SQL Data Insight Business Analytics use cases & IBM Deep Learning Compiler IBM Z AI & Analytics / IBM / © 2022 IBM Corporation 140
  137. 137. 141 AI on IBM Z Resources Soon updated! IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  138. 138. Thank YOU 142 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  139. 139. Trademarks © 2022 IBM Corporation 143 Notes: Performance is in Internal Throughput Rate (ITR) ratio based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput that any user will experience will vary depending upon considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve throughput improvements equivalent to the performance ratios stated here. IBM hardware products are manufactured from new parts, or new and serviceable used parts. Regardless, our warranty terms apply. All client examples cited or described in this presentation are presented as illustrations of the manner in which some clients have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics will vary depending on individual client configurations and conditions. This publication was produced in the United States. IBM may not offer the products, services or features discussed in this document in other countries, and the information may be subject to change without notice. Consult your local IBM business contact for information on the product or services available in your area. All statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only. Information about non-IBM products is obtained from the manufacturers of those products or their published announcements. IBM has not tested those products and cannot confirm the performance, compatibility, or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. Prices subject to change without notice. Contact your IBM representative or Business Partner for the most current pricing in your geography. This information provides only general descriptions of the types and portions of workloads that are eligible for execution on Specialty Engines (e.g, zIIPs, zAAPs, and IFLs) ("SEs"). IBM authorizes clients to use IBM SE only to execute the processing of Eligible Workloads of specific Programs expressly authorized by IBM as specified in the “Authorized Use Table for IBM Machines” provided at www.ibm.com/systems/support/machine_warranties/machine_code/aut.html (“AUT”). No other workload processing is authorized for execution on an SE. IBM offers SE at a lower price than General Processors/Central Processors because clients are authorized to use SEs only to process certain types and/or amounts of workloads as specified by IBM in the AUT. * Registered trademarks of IBM Corporation Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. IT Infrastructure Library is a Registered Trademark of AXELOS Limited. ITIL is a Registered Trademark of AXELOS Limited. Linear Tape-Open, LTO, the LTO Logo, Ultrium, and the Ultrium logo are trademarks of HP, IBM Corp. and Quantum in the U.S. and other countries. Intel, Intel logo, Intel Inside, Intel Inside logo, Intel Centrino, Intel Centrino logo, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. The registered trademark Linux® is used pursuant to a sublicense from the Linux Foundation, the exclusive licensee of Linus Torvalds, owner of the mark on a worldwide basis. Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. OpenStack is a trademark of OpenStack LLC. The OpenStack trademark policy is available on the OpenStack website. Red Hat®, JBoss®, OpenShift®, Fedora®, Hibernate®, Ansible®, CloudForms®, RHCA®, RHCE®, RHCSA®, Ceph®, and Gluster® are trademarks or registered trademarks of Red Hat, Inc. or its subsidiaries in the United States and other countries. RStudio®, the RStudio logo and Shiny® are registered trademarks of RStudio, Inc. UNIX is a registered trademark of The Open Group in the United States and other countries. VMware, the VMware logo, VMware Cloud Foundation, VMware Cloud Foundation Service, VMware vCenter Server, and VMware vSphere are registered trademarks or trademarks of VMware, Inc. or its subsidiaries in the United States and/or other jurisdictions. Zowe™, the Zowe™ logo and the Open Mainframe Project™ are trademarks of The Linux Foundation. Other product and service names might be trademarks of IBM or other companies. CICS* Db2* Telum IBM * IBM Cloud Paks ibm.com* the IBM logo* z16 z/OS*
  140. 140. 144 IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
  141. 141. 145 TENDANCES DE L’IT & MAINFRAME Henri Gilabert - Expert of «Tendances de l’Informatique»
  142. 142. © Copyright 2022 Bruxelles, Paris November 2022
  143. 143. © Copyright 2022 IT professional and recognized expert, I have accumulated diversified skills with manufacturers, IT service companies, businesses, as well as analysis and synthesis skills acquired in the field of strategic consulting. I am also a recognized speaker and lecturer. Henri GILABERT ▪ «Système» ▪ Amdahl ▪ IBM ▪ Capgemini ▪ Compass ▪ Consultant ▪ SLA ▪ Synthèse Informatique Henri GILABERT Henri.gilabert@sla.lu 6 rue Joan DI 66110 Amélie-les-Bains 06.74.23.84.11 147
  144. 144. • More comfort; • More quality; • Low price. New customer habits • Loyalty; • Laws; • Geography. Less effective barriers to entry • More players; • More value; • Price pressure. More competition More innovation in • Products & services • Production processes • Commercialization • Organization Digital technologies © Copyright 2022 OECD, Oslo Manual
  145. 145. © Copyright 2022 ✓ Three groups of trends. ✓ what about mainframes in all this? ✓ IT department, a value-added reseller (VAR). Knowing that... "Prediction is difficult, especially when it comes to the future" Niels Bohr, Danish physicist 1992 Nobel Prize in physics The complementarity principle was introduced by Niels Bohr following the Heisenberg's indeterminacy principle, as a philosophical approach to the seemingly contradictory phenomena of quantum physics.
  146. 146. © Copyright 2022 ✓ 5G; ✓ IoT; ✓ Complex Event Processing. ✓ DevOps; ✓ Mobility & teleworking; ✓ Cloud computing. Rationalization Agility ✓ SoC & SIEM; ✓ From MDM to UEM, Security & administration
  147. 147. Based on breakthrough technologies, such as millimeter waves, NOMA (Non Orthogonal Multiple Access), MEC (Mobile Edge Computing), massive MIMO (Multiple Input Multiple Output), small Cells and Beamforming. The first 5G networks will use carrier aggregation, massive MIMO or NFV (Network Function Virtualization). Three major types of uses: ✓ mMTC – Massive Machine Type Communications: communications between a large quantity and diversity of objects with varied quality of service needs; ✓ eMBB – Enhanced Mobile Broadband: ultra-high speed connection outdoors and indoors with uniform quality of service, even at the edge of the cell; ✓ uRLLC – Ultra-reliable and Low Latency Communications: ultra-reliable communications for mission-critical and very low latency needs. Public evidence is lacking to demonstrate that Huawei would cooperate with Chinese intelligence. But the equipment manufacturer fails to demonstrate that it poses no risk to the national security of the States in which it equips network operators. […] The most worrying is the 5-year intrusion into the computer systems of the African Union headquarters The Huawei law is voted in France Source : F. Launay Univ. Poitier
  148. 148. Urban area Management of urban lighting, buildings, water, heating, transport, pollution and municipal governance (Barcelona Smart City). Waste and bin management (Plastic Omnium), Management of parking spaces and traffic by video counting people & vehicles. Home automation Thermostats (Nest), Switches, household appliances, intrusion- fire safety, weather, flower pot (Parrot). Business Object location terminals (SenseIOT). Technical objects integrated into the product: label (tracking), electronics (equipment management). Industrialized "consumer" objects (connected lock). Health Measurement of diabetes, blood pressure, electro- cardiogram, stress, rest, UV index, toothbrush (Kolibree), “Quantified self” (measurement of personal data) and Fitness (Adidas, Fitbit)… Personal Glasses (SmartEyeGlass), Smartwatch (iWatch), Forks (Hapifork), Fundawear (Durex), Child monitoring (Buddy), Elderly people (UnaliWare), dogs-cats (Pet-Remote)… Vehicles V2X (Vehicle to X detection), V2V (collision avoidance). Bridging objects Routers / gateways (Smart TV Box, Connected car, smartphone, tablet) Triggers (Proximity)
  149. 149. © Copyright 2022 If the measurement/action couple is the basis of the service, the data collected serves two distinct purposes: ✓ Hot data: Real-time data analysis feeds the feedback loop to control the measurement/action pair as closely as possible (CEP). ✓ Cold data: Big Data analysis fuels deep understanding and strategy. https://deepspace.jpl.nasa.gov/ https://www.confluent.io/kafka-summit- san-francisco-2019/mission-critical-real- time-fault-detection-for-nasas-deep-space- network-using-apache-kafka
  150. 150. © Copyright 2022 ✓ 5G; ✓ IoT; ✓ Complex Event Processing. ✓ DevOps; ✓ Mobility & teleworking; ✓ Cloud computing. Rationalization Agility ✓ SoC & SIEM; ✓ From MDM to UEM, Security & administration
  151. 151. © Copyright 2022 Two expectations as legitimate as contradictory!
  152. 152. © Copyright 2022 Two different modes of operation: Two ways to work; Different cultures; Who will want to work in "mod 1“*? Transient or permanent cohabitation? During the work, the store remains open... and it’s likely to last! IS agility the only criterion for all IS? * (GG) Bimodal IT is the practice of managing two separate, coherent modes of IT delivery, one focused on stability and the other on agility. Mode 1 is traditional and sequential, emphasizing safety and accuracy. Mode 2 is exploratory and nonlinear, emphasizing agility and speed.
  153. 153. © Copyright 2022 Tools exist and the covid-19 pandemic has been the biggest Poc (Proof of concept) in history! That said: ✓ Not all activities are suitable; ✓ Not all IS, applications and infrastructures are ready; ✓ Security and privacy issues are increased; ✓ Psychological, organizational and working conditions issues. Real advantages for companies with teleworking in terms of flexibility and cost. Not to mention LibreOffice Online, Open365, OnlyOffice, etc.
  154. 154. © Copyright 2022 Buying mode Description Examples in IT On shelf Ready-to-use products that can easily be obtained Servers x86 Mass customiza- tion Combines flexibility and customization with low unit costs associated with mass production Packages, cloud computing One of a kind Fully customized and unique solution with the price that goes with Specific developments ✓ In the world of mass customization, the less you customize the more is interesting, which is ideal for back-office applications. ✓ Conversely, in the "front-office", customization is essential to differentiate. ✓ The difficulty is to find the right balance. "cloud computing is a mass customization market, cloud vendors do their segmentation, they propose you their offerings and, if we don't want it, you're back in the One-of-a-kind and the price that goes with it."
  155. 155. Data Mining Email Collaborative Audio conferencing videoconference Development and test environments in PaaS mode Web hosting Benefit Ease of implementation ERP/CRM/SCM For SMB HPC & Cloud AI IoT and Complex Event Processing ERP/CRM/SCM For large companies Traditional transactional applications Workstation and virtual prints BPM DevOps, Microservices Distributed transactions More or less easy to implement with gains in terms of cost and ubiquity. Not very differentiating Difficult to implement with gains in terms of agility. Very differentiating BPM : Business Process Management CEP : Complex Event Processing CRM : Customer Relationship Management ERP : Enterprise Resource Planing HPC : High Performance Computing SCM : Supply Chain Management Everything As A Service ➢ Containers as a Service (CaaS) ➢ Backend (BaaS) and Mobile Backend (MBaaS) for basic application services ➢ Functions (FaaS) for a ServerLess Cloud ➢ Platform integration (iPaaS) ➢ Etc… But An increasingly wide range of services… of which the most differentiating are the most difficult to implement.
  156. 156. © Copyright 2022 ✓ 5G; ✓ IoT; ✓ Complex Event Processing. ✓ DevOps; ✓ Mobility & teleworking; ✓ Cloud computing. Rationalization Agility ✓ SoC & SIEM; ✓ From MDM to UEM, Security & administration
  157. 157. © Copyright 2022 Sometimes imposed by regulations (eg PCI DSS), it does not replace compliance with other obligations (eg GDPR). Its role is to: Stand above firewalls and other VPNs; Track events and detect intrusions; implement prediction rules. SIEM (Security Information Management System) for Information collection, aggregation, normalization, log analysis, correlations, detection of low-signals, "replay" of events, ... (Microsoft, Splunk, Exabeam, IBM, Securonix, etc.)
  158. 158. ✓ Allows to enable disable, encrypt, force company policy ✓ The terminal "belongs" to the company that entrusts it to the user... ✓ The device "belongs" to the user (BYOD) or the company (COPE) who uses it personally and for business ✓ Combination of MDM, MAM and MIM ✓ Based on an app store ✓ Unified and consistent management of all devices, OS and some IoT ✓ Management of configurations, profiles and compliance. ✓ User-centric view. Mobil Device Mgt, Enterprise Mobilty Mgt, Unified Endpoint Mgt BYOD : Bring Your Own Device COPE : Corporate Owned Personaly Enabled MDM : Mobil Device Mgt MAM : Mobil Application Mgt MIM : Mobile Information Mgt
  159. 159. © Copyright 2022 ✓ Three groups of trends. ✓ what about mainframes in all this? ✓ IT department, a value-added reseller (VAR).
  160. 160. © Copyright 2022 Does Mainframe address these challenges? ✓ Yes, as well as all other platforms ✓ Even the more leading-edge concept like AIOps With which advantages? ✓ Surely one of their best advantages, is their ability to run legacy as well With which weaknesses? ✓ Skills: despite a steady shift to a younger workforce, mainframe is still perceived as a legacy platform only… ✓ Mainframe is a high availability system, but it is not built to fail* * If you want to address this issue, you must run Parallel Sysplex or Dispersed Parallel Sysplex and be ready to pay the price that goes with
  161. 161. © Copyright 2022 ✓ Three groups of trends. ✓ what about mainframes in all this? ✓ IT department, a value-added reseller (VAR).
  162. 162. © Copyright 2022 Services can be provided internally or externally. The IT department has to decide where its value-added for the company is the highest, and then : Outsource low value-added activities; Insource services & activities with high added value for the company. Services and activities with high added value: Ensure data confidentiality and security; Collaborate with business lines in their transition from applications to business processes; Help business lines to take advantage of IT innovations such as big data, social networks, Internet of things, AI, design thinking, etc... A value-added reseller is an organization that enhances the value of third-party products by adding customized services for resale to its customers. Whatever the technology involved…
  163. 163. © Copyright 2022 Feeding customer’s needs with the best quality/price ratio The IT department organizes its activities according to the "value chain" concept. Build Run IT department management Infrastructure Gérer la relation 167 The value chain (1982) Michael Porter
  164. 164. 168 Moving from technology provider to service provider Becoming business lines preferred VAR Promoting ICT-based innovation From OS to applications, having an Open Source strategy Streamline infrastructure and IS: For existing applications, it means making them as independent as possible of terminals (RWD, RIA & RDA) For new developments, it means making them as agile as possible (micro-services, agile developments, DevOps, cloud-native) For the infrastructure, it means “webizing” the workstation Manage and redirect skills that will be less necessary (sharp technical specialists) towards those that will be critical (cloud contract manager, data scientist, etc.) Set up an organization able to meet the needs of reliability and agility (GG bi-modal IT organization) (GG) Bimodal IT is the practice of managing two separate, coherent modes of IT delivery, one focused on stability and the other on agility. Mode 1 is traditional and sequential, emphasizing safety and accuracy. Mode 2 is exploratory and nonlinear, emphasizing agility and speed.
  165. 165. © Copyright 2022
  166. 166. © Copyright 2022

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