2 Ways to Reduce Mainframe MIPS: Containers & Caching

by | Mar 8, 2023

What are MIPS?

MIPS (Million Instructions Per Second) is a performance measurement used to assess the computing power of a computer system. It measures the rate at which the system can execute instructions, with a higher MIPS rating indicating a faster system. Mainframe MIPS specifically refers to the MIPS rating of an IBM Z computer, and they are often correlated with the operating cost of the system. In other words, the higher the MIPS, the higher the cost. As such, MIPS are an important metric for organizations to measure the effectiveness of their mission-critical systems.

Average Cost of Mainframe MIPS

The cost per MIPS is influenced by a variety of factors and will vary from one company to the next. An often-cited 2015 article claimed the average cost per MIPS at the time was $3,285. A more recent analysis, however, showed that the average annual cost per MIPS for a large mainframe is about $1,600. Regardless of the exact cost, these systems can be expensive to use. Using the smaller of the two cost numbers a mid-sized mainframe can cost over $10 million dollars a year to operate.

As companies face increasing pressure to reduce costs and improve efficiency, the mainframe and its high operating cost are often areas of scrutiny. Reducing these costs isn’t as straightforward as it may seem. Since many organizations have their most important applications and data running on these systems, it’s not always as simple as “doing more with less.”

Fortunately, there are modern approaches and tools that are making it easier for organizations to optimize infrastructure and networks, and allow mainframe teams to shift workloads and reduce MIPS. Let’s take a look at two techniques that can potentially save mainframe users big bucks.

1. Mainframe Containerization to Reduce MIPS

Containerization refers to the process of encapsulating mainframe applications and their dependencies into a single isolated and portable container that can run in an external environment. This containerization technology leverages virtualization technology to create an abstraction layer between the application and the underlying hardware, allowing the mainframe applications to run consistently across different platforms. With containerization, mainframe components can be deployed and managed in a more agile and efficient manner, as it simplifies the application deployment process and allows for faster testing, development, and deployment cycles.

Containerizing mainframe applications also gives organizations improved flexibility, scalability, and resource utilization of legacy systems. Containers allow mainframe applications to be run in a more lightweight and portable way, reducing the resources required to run the application. This, in turn, allows applications to be deployed more quickly and with greater agility than within mainframe environments.

In addition to these performance benefits, MIPS reduction occurs by using containers to move certain applications from mainframe systems to more modern and efficient external environments, such as cloud or distributed systems. By shifting these workloads to containerized environments, organizations can reduce the workload on mainframes – either reducing MIPS or saving workload for other critical applications. Containerization can also enable more efficient use of mainframe resources by allowing containers to be scaled up or down as needed, based on demand, thereby reducing MIPS consumption and costs.

2. Data Caching to Reduce MIPS

With the proper tools, mainframe data caching can reduce workload and improve the performance of systems by temporarily storing frequently accessed data in high-speed cache memory. This can not only reduce the amount of time it takes to retrieve data from the mainframe, but also reduce workload and MIPS. Caching algorithms are used to determine which data should be stored in the cache and when it should be evicted to make room for new data. The cache is typically located close to the mainframe, which allows for faster access times and reduced latency.

Mainframe data caching is especially impactful in large-scale environments where multiple applications can be accessing the same data simultaneously. Imagine an insurance company that has an underwriting engine and claims adjudication application both calling for a customer address located on the mainframe. By pulling the customer data from the cache, the two applications not only reduce calls to the mainframe but are likely seeing lower latency from the external cache than the mainframe.

The key to optimizing mainframe data caching is selectively pulling data that should be stored in the cache and setting appropriate expiration policies in a very granular way. It is important to carefully manage the cache to avoid issues such as cache thrashing or data consistency problems, which can negatively impact system performance and data integrity.


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