Mainframe modernization using Model9

Blob Storage
ExpressRoute
VPN Gateway
Synapse Analytics
Monitor

This article outlines a solution for migrating mainframe data to the cloud. Besides Model9, the solution's core components include Azure storage and database services.

Apache®, Apache Ignite, Ignite, and the flame logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks.

Architecture

Screenshot that shows the Azure architecture to migrate mainframe data to the cloud.

Download a Visio file of this architecture.

Workflow

  1. The Model9 agent acts as an interface for data migration between z/OS and Azure.

  2. The agent reads data from the mainframe and then migrates that data to Blob Storage.

  3. Model9 cloud data platform for mainframe manages and transforms the migrated data from on-premises to Blob Storage.

  4. The Model9 cloud data platform transfers migrated data to Azure data services.

Components

The solution uses the following components.

Model9 cloud data platform components

The main components of Model9 cloud data platform are:

  • Model9 agent
    A Java-based agent that runs on-premises on one or more z/OS logical partitions (LPARs). It performs the read and write operations from and to the Azure cloud object storage. It also uses zIIP engines to save expensive CPU consumption.

  • Management server
    A docker-based application that manages the web UI and the communication to the z/OS agents. It provides a way for you to define several types of policies for data protection, migration, and data archival.

  • Lifecycle management engine
    A Java-based application that runs on-premises on a z/OS LPAR and deletes expired data both from the object storage and from z/OS.

  • Data management command-line interface (CLI)
    A CLI that runs on-premises on a z/OS LPAR. You can use this interface to perform backup, restore, archive, recall, and delete resource-based actions to and from the Azure cloud object storage.

  • Data engine
    A Java-based application that supports the transformation of Model9 managed objects into an open format that gets processed by AI, business intelligence, and machine learning applications. The data can be transformed either to a CSV or JSON file, or directly to Azure Database for SQL.

  • Data Transformation CLI
    A Java-based application that invokes data transformation requests wraps the standard REST–API based calls into a basic, easy-to-use client.

Networking and identity

  • Azure ExpressRoute extends your on-premises networks into cloud services that are offered by Microsoft over a private connection from a connectivity provider. With ExpressRoute, you can establish connections to cloud components such as Azure services and Microsoft 365.

  • Azure VPN Gateway is a specific type of virtual network gateway that sends encrypted traffic between Azure Virtual Network and an on-premises location over the public internet.

  • Azure Active Directory (Azure AD) is an identity and access management service that synchronizes with an on-premises active directory.

Application

  • Apache Kafka is an open-source distributed event-streaming platform that's used for high-performance data pipelines, streaming analytics, data integration, and business-critical applications.

Storage

  • Azure SQL Database is part of the Azure SQL family and is built on the cloud. This service offers all the benefits of a fully managed and evergreen platform as a service (PaaS). Azure SQL Database also provides AI-powered, automated features that optimize performance and durability. Serverless compute and Hyperscale storage options automatically scale resources on demand.

  • Azure Database for PostgreSQL is a fully managed relational database service that's based on the community edition of the open-source PostgreSQL database engine. With this service, you can focus on application innovation instead of database management. You can also scale your workload quickly and easily.

  • Azure Database for MySQL is a fully managed relational database service that's based on the community edition of the open-source MySQL database engine.

  • Azure SQL Managed Instance is an intelligent, scalable cloud database service that offers all the benefits of a fully managed and evergreen PaaS. SQL Managed Instance has nearly 100 percent compatibility with the latest SQL Server (Enterprise Edition) database engine. This service also provides a native virtual network implementation that addresses common security concerns.

  • Azure Synapse Analytics is a fast and flexible cloud data warehouse that helps you scale, compute, and store elastically and independently, with a massively parallel processing architecture.

  • Azure Storage is a cloud storage solution that includes object, file, disk, queue, and table storage. Services include hybrid storage solutions and tools for transferring, sharing, and backing up data.

Analysis and reporting

  • Power BI is a suite of business analytics tools that deliver insights throughout your organization. By using Power BI, you can connect to hundreds of data sources, simplify data preparation, and drive ad hoc analysis. You can produce beautiful reports, then publish them for your organization to consume on the web, and across mobile devices.

Monitoring

  • Azure Monitor delivers a comprehensive solution for collecting, analyzing, and acting on telemetry from cloud and on-premises environments. It contains the Application Insights, Azure Monitor Logs, and Azure Log Analytics features.

  • Model9 supports monitoring the status and results of all activities via the Activities page in the Model9 UI. For more information, see Monitoring Activities.

Alternatives

  • Instead of installing the Model9 management server in the cloud on Azure Virtual Network, you can install it on-premises. You can use the z/OS container extension (zCX) to deploy it directly on z/OS.

  • Model9 data transformation service runs externally to the mainframe in an on-premises environment. This setup saves expensive mainframe resources. You can also deploy on the cloud by using either a server instance or container services.

  • ExpressRoute provides a private and efficient connection to Azure from on-premises, but you can also use site-to-site VPN.

Scenario details

Mainframe data that's stored in physical or virtual tape libraries is critical for customers. As this data grows, its sheer volume can require an unfathomable amount of storage. The data can then become more demanding to maintain on-premises. You can easily migrate this data to Azure storage and use it for AI, business intelligence, machine learning, and analytics applications. Azure storage carries several unique benefits over traditional on-premises storage approaches, and includes data management services, scalability, performance, reliability, and security.

Model9 provides an end-to-end suite of cloud data management solutions for mainframes that solves the migration problem — elegantly, cost effectively, and with no application changes. Based on a unique, proven technology, Model9 solutions migrate mainframe data by using secure and expedited technology to access the Azure cloud.

Model9 solutions are designed to save expensive mainframe CPU resources by using the mainframe zIIP engines and to connect the mainframe data to Azure cloud storage. Cloud applications can use the migrated data in Azure storage services. This article outlines a solution for migrating mainframe data to the cloud. Besides Model9, the solution's core components include Azure storage and database services.

Apache®, Apache Ignite, Ignite, and the flame logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks.

Potential use cases

Model9 offers a suite of services that are based on the Model9 cloud data platform. These services are suitable for the following use cases:

  • Make mainframe data available to Azure data services, AI, machine learning, analytics, and business intelligence tools.

  • Protect mainframe data with backup and archive to Azure Blob Storage.

  • Have mainframe applications write and read data directly to and from Blob Storage.

  • Defend mainframe data against cyberattacks by creating an immutable third copy in Azure.

Considerations

The following considerations, based on the Azure Well-Architected Framework, apply to this solution.

Cost optimization

Use the Azure pricing calculator to estimate the cost of implementing this solution.

Reliability

  • Deploy Model9 cloud data manager in the cloud on Azure Virtual Machines, and on the customer's virtual network for superior availability.

  • Deploy an agent in each z/OS LPAR to allow better availability across the systems complex (sysplex), or the mainframe cluster.

  • Combine the Application Insights and Log Analytics features of Monitor to stay informed about the health of Azure resources.

  • For guidance on resiliency in Azure, see Design reliable Azure applications.

Scalability

  • Use multiple agents to increase scalability and throughput on all the LPARs within the same sysplex.

  • Use multiple transformation instances behind a load balancer to increase scalability and performance.

  • Blob Storage is a scalable system for storing backups, archival data, secondary data files, and other unstructured digital objects.

Security

  • Authenticate Azure resources by using Azure AD. Manage permissions by using role-based access control (RBAC).

  • Model9 uses the z/OS security authorization facility (SAF) for authentication of actions that are performed.

  • The security options in Azure database services are:

    • Data encryption at rest.
    • Dynamic data masking.
    • Always-encrypted database.
  • For general guidance on designing secure solutions, see Azure security documentation.

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

Principal authors:

Other contributors:

Next steps