It is anticipated that the mainframe market will grow by a further 4.3% by 2025, reaching nearly $3 billion in annual revenue. At present, a single high-end mainframe can process up to 790 billion transactions per day. In other words, a mainframe can manage 30 billion transactions daily. Therefore, it’s no surprise that 92 of the world’s 100 biggest banks are still reliant on mainframe computing.

Organizations are attracted to AIOps’ promise to leverage AI-driven intelligence and automation to make quick and accurate decisions and maintain resilience. By applying artificial intelligence to IT operations management, AIOps can automate problem resolution and accelerate IT operations management in modern, complex IT environments.

Introduction to the Mainframe

The mainframe is a sizeable high-speed computer, supporting numerous workstations or peripherals. These are stored backend data by multiple companies and organizations that can efficiently mine and transfer it to various sources simultaneously. These become the heart of many projects carried out in firms worldwide. This data management system is so efficient that nearly 70% of Fortune 500 companies worldwide use it extensively to provide services and manage their data. 

Understanding the Mainframe

The main components of mainframe environments are JCL and COBOL.

1. JCL Jobs Control Language (JCL) expands JCL, and batch processing is the fundamental principle behind JCL. Computers can be operated and customized with JCL’s authority and flexibility.
2. COBOL In the 1950s, it was widely used for data processing in business sectors as a high-level language. Three major groups devised the new language at a meeting of the US Department of Defense in May 1958. It was called Cobol, which translates as Common Business Oriented Language, in 1959. COBOL was officially introduced in May 1961. From a compiler perspective, it was initially designed in 1962; users began developing Cobol programs. 

Have you ever wondered why companies keep selling mainframes? There’s no reason to live in the past. It’s because mainframes are still paramount in several industries.

Not only do some companies still rely on mainframe systems they bought years ago and have yet to decommission, but they are also investing in brand-new mainframes.

Industries where mainframes play a significant role

What makes companies continue to invest vast sums of money in mainframe systems? Why do businesses keep their mainframes and even invest in more mainframes?

The short answer is that they are still the only type of hardware capable of handling the enormous volumes of transactions that are a necessary part of business operations in many industries today.

Mainframes are still a crucial resource in industries like the following:

• Banking Banks of all types must process massive volumes of transactions. Investment banks prioritize high-frequency trading, and they must react instantly to changes in financial markets. Financial services, which revolve around credit card transactions, ATM withdrawals, and online account updates, necessitate massive transaction volumes for banks of all sizes. In both cases, mainframes enable banks to process data at a scale that commodity servers are incapable of.
• Insurance Insurance companies live and die by data – and there is a lot of it. Insurers rely on mainframes to ensure that the data that drives their business can be handled. Data enables them to assess risk, set prices, and invest in the appropriate markets.
• Healthcare Αnother industry where data is king – and, by extension, mainframes. Mainframes power the secure, compliant, high-volume, high-availability data storage and transactions that keep modern healthcare running.
• Government From the IRS to the National Weather Service, government agencies of all types require vast amounts of data to be stored and analyzed. Mainframes are still assisting them in this endeavor.
• Aviation You don’t need to be a pilot to understand that flight networks are complex and ever-changing. That is why airlines and government regulators who oversee airlines and even aircraft manufacturers rely on mainframes to ensure that people and planes arrive at their destinations in the most efficient manner possible.
• Retail Business For a long time, traditional retailers have used mainframes to aid transaction processing and inventory management. These machines, however, are not limited to old-fashioned brick-and-mortar stores. Mainframes are still used by 23 of the top 25 retailers in the United States. Online retailers can also benefit from modern mainframe systems’ ability to handle massive transactions.

What are the challenges faced today?

Performance management plays a crucial role in setting, evaluating, and understanding the goals for IT resources as they relate to business needs. When using hybrid clouds, it is critical to understand the relationship between applications and resources to meet Service Level Agreements and other performance goals. If they are not, the correct information is available to appropriate stakeholders to make prompt, accurate choices as to what should be done.

Planning capacity often involves making the best use of existing resources and deciding where to invest smartly, for example, by upgrading technology or purchasing new hardware. If these investments are made blindly, they may not produce the expected benefits. The process of gathering the correct data to make the best decision can be complicated and time-consuming, not to mention the skills of the capacity planner that may be required to make judgments and recommendations.

To summarize, many enterprises struggle today to curate the critical data needed to make business decisions about their ever-changing IT environments. As a result, it hinders decision-making, which results in a long time to determine the best course of action and ultimately affects business results.

How does what I have today differ from what is required now?

Most enterprises today have some tooling to help manage performance and capacity, and some have overlapping products for the same purpose, which is inefficient. Homegrown solutions built over many years by domain specialists are often relied upon. Most of the time, these tools cannot provide the reporting and functionality needed, either because the original maker of the device is no longer around or because assumptions about how workloads will be used and the impact on performance has changed.

Due to the mainframe being a part of an integrated environment, workloads are increasingly being driven by hybrid applications using API-enabled resources. The level of expectation to resolve problems is an example of these changing assumptions. Reports were traditionally generated the following day after operations data, such as SMF records, had been processed overnight. As a result of the volume of SMF data that can be produced, it is understandable that many look for ways to avoid processing the data during peak periods, resulting in detailed reporting not being available until the following day.

It’s no longer acceptable to wait until the following day to examine the root causes of performance issues or produce insights aligned with other enterprise-wide reports. In addition, self-service accessibility of news and information enables the end-user – from management to technical stakeholders – to access and customize the information to their needs, rather than depending on one or two skilled report builders. Insufficient access to information to manage workloads and make decisions about optimizing resources can negatively affect service levels and increase operating costs.

Cloud computing and cloud storage solutions are upcoming booming industries. Users can store their data at a data center in California and easily access them through the internet in Mumbai. It is a b2c application of a Datacenter application. It requires excellent human effort so that user data is not tampered with yet stored in the most organized way possible. This cloud-based solution is a commercialized way of the mainframe usage in firms which is background.

As the resources, consumers and workstations are multiple and sparsely placed worldwide, the data needs to be managed, stored, and shared in the most efficient way possible. This way of data transfer is dependent on very efficient CPUs. But since they have a limit of prompt, correct, and accurate data transfer beyond a particular limit, their processing cannot cope with the data transfer and computation. So, optimizing the mainframe becomes of utmost importance to keep up with data flow.

How to Optimize Mainframe Performance

The mainframe is still a critical backend system for business logic and transaction processing that drives the digital transformation of many enterprises. As new business initiatives such as DevOps, automation, and modernization expand application delivery capabilities across distributed, cloud, and mainframe systems, several industry surveys have shown that mainframe workloads grow each year.

Over the past few years, growing customer expectations and business demands have driven the need for cross-platform applications and increased computing power. While IT organizations have had to manage this complexity and lower data center costs, new challenges such as the Coronavirus pandemic have forced businesses to operate on even leaner budgets without abandoning digital transformation.

Choosing to reduce capacity on the mainframe to save costs will decrease application performance, and dissatisfied customers as workloads grow and businesses strive to innovate. However, paying for more MIPS (millions of instructions per second) to increase mainframe capacity is something most IT departments cannot afford, let alone during pandemics.

To achieve this, IT teams should use performance and capacity management tools to understand how existing capacity is being used and where adjustments need to be made to support increased application and transaction growth without needing to purchase additional capacity.

Optimizing CPU MIPS using Mainframe Performance Tools

Implementing a mainframe performance and capacity management solution which provides a visual and data-driven approach to reducing or controlling MIPS consumption is critical to getting the most from existing mainframe resources. Choosing the right tool will increase your ability to:

• Identify what workloads are causing high CPU consumption and when and what is causing CPU spikes that negatively impact mainframe performance and budget.
• Project future MIPS consumption per workload over time-based on projected application growth and changing business requirements – identifying whether existing resources are sufficient to handle application growth without purchasing more processing power.
• Study whether it would be possible to process the same amount of work using ZIIPS instead of more expensive MIPS – reserving more expensive MIPS for critical workloads that have to run on general-purpose processors. 
• Determine the best configuration settings for various logical partitions (LPAR) and assess the impact of potential changes to settings to ensure efficient distribution of CPU resources.
• The placement of LPARs on different CPCs and their movement to maximize resource utilization and control MIPS growth.
• Using pre-defined capacities for z/OS LPARs can be an effective way of controlling MIPS consumption. However, soft capping can have adverse effects on LPAR CPU access.
• Identify delays when an LPAR accesses processor resources, rather than actual high CPU MIPS consumption, so that you don’t waste MIPS capacity unnecessarily and optimize configuration options and tuning.

Benefits of Mainframe Performance Management Solution

Modern mainframe performance management and capacity planning solutions can help you accomplish advanced tasks more efficiently and accelerate your business transformation in the following ways: 

• Processing time is recovered, and CPU consumption is reduced.
• Through the identification of performance problems in the database, I/O, and applications.
• Identifying application execution delays and metrics.
• Analysis of transaction performance profiles.
• Optimizing application performance and avoiding costly upgrade costs.

Having access to physical and virtual performance and capacity data across your enterprise, and analyzing it automatically, will help you know where your problems are, how to improve performance, and what needs to be done to make your IT more effective.

While there are plenty of system performance monitoring tools for workstations and servers, there aren’t many available for mainframes. Third-party monitoring system providers have developed full-stack monitoring packages that cover networks, endpoints, and applications, however, mainframes are usually left out. Of all the mainframe brands, the one that has the highest availability of third-party monitoring tools available is the IBM Z Series.

Risks involved in mainframe modernization

• People risks Mainframe programming is done in COBOL, a language at the core of the mainframe industry. Today, 95% of ATM applications are written in COBOL. However, it is also the reason ATMs have problems. Growing numbers of COBOL experts are exiting the workforce, posing a real threat to the industry. There are no replacements for these developers in education and training programs today. The growing demand for COBOL developers as the Baby Boomers will intensify this problem. 
• Financial risks Another critical risk organizations face today is the rapid increase in costs associated with mainframe usage. Its mainframe maintenance costs usually dominate a company’s IT budget. Many major users pay mainframe vendors billions of dollars in software and infrastructure fees annually. Today’s modernization efforts are primarily driven by reducing these costs and other financial risks.
• Business risks Performance, reliability, and security are all excellent features of mainframe applications. However, they can also act as an obstacle to businesses adapting and innovating quickly. Monolithic mainframe applications are challenging to change. Think about the traditional Waterfall process, the complexity of large applications, and the difficult task of testing all changes and enhancements. Meanwhile, these companies face competition from cloud-native startups emerging in practically every industry and disrupting them with quick feature releases and modern capabilities that deliver a fantastic user experience. Companies need to be proactive about addressing this issue.