What is a Mainframe?
A mainframe is a large computer that runs critical business applications requiring a significant amount of processing power. Banks and other large organizations often require this computing power for data processing, transactions, and enterprise apps, exceeding the demands of a server. Organizations migrate their legacy on-premises mainframes to modern cloud infrastructure to increase usability, scalability, and reduce operational costs.
What is mainframe computing?
Mainframe computing enables large-scale data processing through a single, powerful central system. This system supports multiple users and terminals in real time. The term “mainframe” originated in the 1950s, when these high performance computers required large physical enclosures. Modern mainframe cabinets are roughly the size of a refrigerator.
Over the decades, commercial servers and personal computers have taken over roles once handled by mainframes. However, mainframes remain important to governments, financial institutions, aviation services, healthcare, and other critical industries. Much of this mainframe infrastructure is now migrating to cloud computing services.
Modern mainframe computers are more powerful than their predecessors because of rapid advancements in the underlying infrastructure. A single mainframe computer may handle billions of transactions daily and support batch processing. Mainframe computers allow you to share data, access resources, regulate access, and distribute resources to users.
Despite their significant processing power, mainframes differ from servers or supercomputers.
- Servers are compact and used for highly specialized tasks. Meanwhile, a mainframe system is more powerful, providing quick access to stored information.
- Supercomputers are high performance computers capable of performing complex floating-point operations at a large scale. They often have more processing power than a mainframe. On the other hand, a mainframe supports millions of small data operations in real time.
How does a mainframe work?
The modern mainframe architecture consists of specialized hardware and software components that work together to provide reliable, high-performance data processing. The following are key components of modern mainframe computers.
Central processing units (CPUs)
Early mainframes ran with a single CPU. However, modern mainframes use multiple processor cores that focus on specific tasks.
System assistance processor (SAP)
The SAP is responsible for passing the data from one device to another, allowing the CPU to focus on data processing.
Control unit
Mainframe computers depend on the control unit to manage communications between processors, I/O channels, and other connected devices.
Input/output (I/O) cards
A mainframe transfers data to connected devices through multiple I/O cards. If one I/O card fails, another replaces it to prevent operational disruption.
Storage space
Mainframe systems store and process very large amounts of data in hard drives, enabling quick retrieval and strict access control.
Clustering technologies
Multiple mainframe systems can combine to increase processing power. With this setup, the system can distribute data to individual mainframes to accelerate processing.
Multiple operating systems
A mainframe computer can support multiple operating systems through virtualization technologies. Virtual machines allow efficient resource allocation to workloads and applications running within each virtualized environment.
Database management system
Mainframe computers include a database management system, which enables them to store, manage, and share data with multiple end devices.
What are the use cases of a mainframe computer?
Mainframe technology excels at applications that require reliable, low-latency, and simultaneous data transfer. The following are some common applications of mainframe computing.
Financial transaction processing
Banks, investment firms, insurance companies, and other financial institutions store, process, and retrieve transactional data in mainframe computers. For example, when you make a withdrawal from an automated teller machine (ATM), the mainframe computer checks its internal database before approving the transaction.
Large-scale statistical analysis
Mainframe computing enables you to run machine learning models, predictive analytics, and other statistical workloads to derive insights from unstructured data. For example, a university receives and stores entrant applications on a mainframe computer and automatically shortlists potential students with artificial intelligence (AI).
Enterprise resource planning
Most enterprise resource planning (ERP) software still relies on mainframe systems for data processing and management. Mainframe computing ensures consistent access to data and allows you to integrate your ERP system with other enterprise applications. Additionally, some industries must adhere to laws to store sensitive data on-premises in mainframe storage.
Blockchain processing
Blockchain, although originating from cryptocurrencies, is now being adopted across various industries, including retail, energy, and healthcare. With blockchain technologies, you can ensure transaction integrity by creating immutable records designed to be tamper-resistant. To do so, they require reliable and secure storage that mainframe computers provide.
Generative AI workloads
Modern mainframe architecture can support the training, fine-tuning, and deployment of large language models for various AI applications. For example, an ecommerce business can deploy an AI chatbot on a mainframe computer. As a result, the chatbot has direct access to commercial data, which it can use to personalize its responses when interacting with customers.
What are the approaches to mainframe modernization in the cloud?
Mainframe modernization is the process of shifting workloads and data from a mainframe infrastructure to a cloud environment. It may involve shifting entire workloads to the cloud or adopting a hybrid setup. Modernization efforts may be part of a broader digital transformation initiative.
Many organizations still operate with mainframes built with old or obsolete hardware and software. These mainframe computers are costly to maintain, secure, and scale. Therefore, you can modernize mainframe workloads to save costs, improve maintainability, and increase scalability.
Depending on business needs, you can use various approaches to migrate, validate, and modernize mainframe applications.
Refactor
Mainframe applications mostly run on legacy programming languages, such as COBOL and PL/I. These languages existed before the modern cloud computing era began. Therefore, legacy mainframe applications cannot take full advantage of cloud capabilities without refactoring the codebase.
Application refactoring involves a complete rewrite of the mainframe application before deploying it to the cloud. For example, you can refactor COBOL mainframe software into a Java-based microservices application.
Replatform
Replatform shifts a mainframe application to the cloud with minor changes to the underlying codebase. You may choose to replatform to prevent disruption to mission-critical applications while enabling future growth, scalability, and innovation.
Replatforming aims to preserve the mainframe application in its original form and deploy it in a supported cloud environment. Therefore, users enjoy continuous access to the application while software teams can gradually modernize it.
Data replication and file transfer
Mainframe computers perform computationally intensive data processing. However, mainframe hardware may face challenges in processing data at scale.
By replicating mainframe data and transferring files to the cloud, software teams can work more effectively with cloud storage, such as databases, data lakes, and data warehouses. Additionally, storing data on cloud storage enables easy disaster recovery, data accessibility, and governance across all data points.
Application testing
Application testing enables software developers to verify that the mainframe application is functioning correctly in the cloud. Testing is important to ensure that the application behaves consistently before and after migration.
To test the application, software developers use automated tools to compare the application running on the mainframe with the one they deploy on the cloud.
How can AWS support your mainframe modernization efforts?
AWS Mainframe Modernization is a managed service that allows you to migrate and modernize your on-premises mainframe applications to a cloud-native, fully managed runtime environment on AWS. AWS Mainframe Modernization offers a range of solutions to modernize, migrate, run, test, and operate your mainframe applications in the cloud.
AWS Transform for mainframe is the first agentic AI service for modernizing mainframe workloads at scale. The service streamlines core phases of modernization, from initial analysis and planning to code refactoring and migration, cutting modernization timelines from years to months and reducing ongoing mainframe costs.
Get started with mainframe modernization on AWS by creating a free account today.