From Legacy Mainframe to the Cloud: The Finnish Railways Evolution with Docker Enterprise

In 2016, Finnish Railways reservation system and many other systems were monolithic applications running on a mainframe or local datacenters. They began a containerization project focused on modernizing the reservation system. The invest paid off. Today, they have containerized multiple applications, running both on-premises and on AWS today. That’s allowed Finland’s leading public transport agency to shut down a data center and become a technology innovator. In this session, Finnish Rail will explain the processes and tools they used to build a multi-cloud strategy that lets them take advantage of geo-location and cost advantages to run in AWS, Azure and soon Google Cloud. You’ll learn: - How to implement a successful multi-cloud deployment - What challenges you can expect to face along the way - The processes and tools that are a critical part of a successful project.

Transcript

1. From Legacy Mainframe to HyperScale Cloud
2. @nikovirtala CLOUD ARCHITECT, VR GROUP Niko Virtala
3. Work Hobbies Home Hyvinkää, Finland with wife and daughters Diebold Nixdorf, Nordcloud, now VR Group Guitar, photograph, fatbike Who is Niko Virtala? @nikovirtala
4. VR Group is Finnish Railways 7,540 employees Net sales 1,251 Million euros 168,1 million train & bus journeys 49 Million tons of goods transported by rail & road Train Operations Maintenance
5. A Technology History of VR Group 1996 2003 1993 Mainframe applications C++/Mainframe client/server -solutions First online store Next generation mobile apps 2016 2012 First mobile apps 2011 SOA based sales system 2016 New web apps, APIs, API management
6. Drivers for Modernization at VR Group with Docker Enterprise Cost Quality Speed
7. Building a Business Case 1. Detailed assessment of apps and resources in existing environment 2. Sizing exercise using Docker containers and AWS resources 3. Build financial model around operational costs 4. The case = minimum 40% savings per app
8. Cost Savings >50% Average Across All Apps Business Case Achieved with Upside Unified Architecture for the First Time New Visibility into Applications Baseline operational functionality for all apps and partners
9. And what did we learn along the way? How Did We Get There?
10. • To go from monolithic apps to microservices and cloud, you need to break old habits • In a traditional organization, start by acting as the example Changing the Culture
11. • We established a new model with first PoC • Gave the app. team greater freedom -- and responsibility • The result: Much faster development and release cycles ...and the Operational Model
12. What is DevOps? “Academics and practitioners have not developed a unique definition for the term DevOps.” --Wikipedia
13. Ok, so what is DevOps? • Culture, a mindset that can’t be bought • Continuous learning • Collaboration and teamwork You Can’t Buy DevOps
14. Ok, so what is DevOps? • Culture, a mindset that can’t be bought • Continuous learning • Collaboration and teamwork You Can’t Buy DevOps
15. Architecting for a Future with the Cloud
16. Building robust, repeatable and predictable cloud platform is not easy, but there is help available when it is getting too overwhelming. From Mainframe to a Cloud Architecture
17. Issues We Considered Account Structure Security Baseline Account Hardening User Access Audit Logging Network/DNS Design Backup/DR Model Tagging Model Centralized Billing and Alerts
18. VR Group Cloud Architecture Hybrid Cloud VRIX by Cinia AWS Azure GCP Direct Connect Express Route Partner Interconnect On Premise Datacenter On Premise Datacenter Office Networks
19. Infrastructure as Code App. Load Balancer ec2 Instance RDS Database Route 53 VPC Terraform infrastructure declaration
20. Infrastructure as Code Code Version Control Integrate Code Review Deploy Monitor tflint terratest Developer
21. The Future is Bright All Cloud Everything as Code Zero Trust Fully Automated
22. The Future is Bright All Cloud Everything as Code Zero Trust Fully Automated
23. Docker Enterprise Platform UCP Controller Node(s) (3) HRM Node(s) (1) DTR Node(s) (3) Worker Node Pool 1 Worker Node Pool n Worker Node Pool 2 HRM Node(s) (3) Worker Node Pool n UCP Controller Node(s) (1) DTR Node(s) (1) Worker Node Pool 1 Worker Node Pool 2 UCP Controller Node(s) (5) Worker Node Pool 1 Worker Node Pool 2 DTR Node(s) (3) HRM Node(s) (3) Worker Node Pool n UCP Load Balancer DTR Load Balancer HRM Load Balancer Internal HRM Load Balancer External UCP Load Balancer DTR Load Balancer HRM Load Balancer Internal HRM Load Balancer External UCP Load Balancer DTR Load Balancer HRM Load Balancer Internal HRM Load Balancer External AWS Account / VPC / test AWS Account / VPC / prod CI cluster QA cluster Production cluster Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group
24. Docker Enterprise Platform UCP Controller Node(s) (3) HRM Node(s) (1) DTR Node(s) (3) Worker Node Pool 1 Worker Node Pool n Worker Node Pool 2 HRM Node(s) (3) Worker Node Pool n UCP Controller Node(s) (1) DTR Node(s) (1) Worker Node Pool 1 Worker Node Pool 2 UCP Controller Node(s) (5) Worker Node Pool 1 Worker Node Pool 2 DTR Node(s) (3) HRM Node(s) (3) Worker Node Pool n UCP Load Balancer DTR Load Balancer HRM Load Balancer Internal HRM Load Balancer External UCP Load Balancer DTR Load Balancer HRM Load Balancer Internal HRM Load Balancer External UCP Load Balancer DTR Load Balancer HRM Load Balancer Internal HRM Load Balancer External AWS Account / VPC / test AWS Account / VPC / prod CI cluster QA cluster Production cluster Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group Auto Scaling Group
25. Our Docker Platform Today • 66 nodes in three(3) clusters • 182 vCPUs • 1256 GB of RAM • 99% cluster deployment automation • Own tools to manage RBAC
26. CI/CD Workflow Build Static Analysis Unit Tests Deploy to “dev” Integrati on Tests Deploy to “test” E2e Tests Deploy to “prod” Measure & Monitor Feedback Loop Automatic Promotion Automatic Promotion Manual Promotion
27. CI/CD Workflow Build Static Analysis Unit Tests Deploy to “dev” Integrati on Tests Deploy to “test” E2e Tests Deploy to “prod” Measure & Monitor Feedback Loop Automatic Promotion Automatic Promotion Manual Promotion
28. • If you can’t measure it, did it happen. • Retain unique request Id throughout the process Logging & Metrics Monitoring
29. + Savings in TCO >= 60% + 20-25x faster release cycle, without downtime + Change lead time down from months to weeks, in best case days Achievements
30. • Be customer focused • Start by setting the example • Take your time • Embrace cloud & automation • Choose right solution for the purpose Key Takeaways
31. Thank You! Kiitos!
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