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Mainframe Performance Management Guide

The mainframe market has been rapidly growing over the past few years. Projections show that the market is likely to grow by 4.3% until 2025, getting to almost three billion USD in annual revenue.

This is due to the sheer usefulness of mainframes’ computing power, with most mainframes handling over 30 billion transactions per day. Due to this, it’s not a surprise that 92% of the world’s top 100 banks rely on mainframe computers.

Almost 70% of all Fortune 500 companies rely on mainframes for their crucial computing needs. Naturally, these corporations seek to get the most out of their large investment in the mainframes.

Some businesses are fond of AIOps (Artificial Intelligence Operations) approaches where highly intelligent AIs can perform quick and accurate decision-making while maintaining their resilience. AIOps tries to automate the process of problem resolution while accelerating IT management tasks in modern IT structures.

Since mainframes are such a large part of enterprises’ infrastructures, maintaining their uptime and peak performance are paramount to ensure their massive computing power goes to good use. Today, we’ll be looking at the best ways to optimize your mainframe’s performance and manage its uptime.

What is a Mainframe?

A mainframe is a large, high-speed computing device that can support multiple peripherals and workstations. Mainframes have massive amounts of memory and their processors can handle billions of transactions in real-time. Mainframes store backend data for a multitude of corporations and can quickly mine and transfer information to multiple sources at once. Because of this, they’ve become a crucial part of many business projects.

Components of a Mainframe Environment

The two main components of a mainframe environment are COBOL and JCL.

COBOL (Common Business-Oriented Language) is an imperative, procedural, and object-oriented computer programming language. It was created back in 1958, and officially introduced in 1961. It was the most used language for data processing in the business sector in its age and is still commonly used for financial and administrative systems to this day.

JCL or the Jobs Control Language is the name for a variety of scripting languages used on the IBM mainframe OSs that instruct the system to run a batch job or start a subsystem. Using JCL’s high authority and flexibility, mainframes can be optimized to perform a select series of tasks extremely efficiently.

Even though mainframes have been getting used in the business sector for years, they’re still crucial for a variety of industries. Not only are many firms reliant on old, but still functioning mainframes, many of them are investing in brand-new ones.

What Industries do Mainframes Play a Large Role in?

Now, mainframes are expensive, and often narrow pieces of technology. For a business to invest their hard-earned profits into them, they need to provide a massive benefit.

The reason why they’re still used in firms across the world is that they’re the only kind of hardware that can take care of the massive transaction volumes that are ever-present in certain business sectors.

Financial Industries

Financial institutions like banks have to process a huge volume of transactions on a moment-to-moment basis. An investment bank, for example, will have to quickly react to the minutiae of the financial markets to remain competitive. Services like ATM withdrawals, any transaction made by card, or online account changes must be kept fast, secure, and consistent.  For both of these scenarios, banks have to rely on mainframes to make sure they’re able to process all the data promptly.

Insurance companies are another kind of financial institution that needs to handle huge amounts of data. Since insurance prices are driven by a lot of factors, each of which is difficult to track without a device that can handle a massive amount of transactions, they rely on mainframes. Using mainframes, they can gather the data they need to properly assess risks and set a fair price.

Healthcare

In healthcare, hospitals have to track a lot of data about their patients. Everything from patient information to billing information has to be highly secure and available at a moment’s notice. A small slip-up can be the difference between life and death for their patients, hence they rely on mainframes to handle the high-volume transactions they need.

Government Services

The government has a variety of sectors that require mass data computation. The IRS has to handle the tax documentation of millions of citizens, for example, and errors or delays will cost the country millions.

National weather services rely on extremely advanced computational capabilities to accurately calculate and predict the weather. This means looking at thousands of factors simultaneously about each other- hence mainframes are the only sensible solution.

Aviation Industries

The aviation industry is extremely complex. Dealing with flight networks that are constantly changing, with flight paths intertwining at a moment’s notice. This is why air travel regulators have to rely on mainframes to ensure that planes are arriving where they need to be at the right time.

Retail

Retail businesses have always relied on mainframes, even in times when online shopping was scarce. Today, retailers like Walmart and Amazon are processing millions of transactions daily, and each of these transactions has to be referred to the closest warehouse to ensure the shortest shipping times. Because of this, out of the top 25 retailers in the US, 23 of them take advantage of mainframes to help them process their customers’ data.

What Do Businesses Struggle with in Mainframe Performance Management?

In the past, mainframe management mainly consisted of ensuring the physical well-being of the mainframe and ironing out bugs in the code. There simply weren’t enough different technologies to cause any major issues.

Today, with the advancement of IT, cloud computing, hybrid systems, and the internet being in all facets of the business world, mainframes have a lot more to contend with. Hybrid cloud computing, for example, requires businesses to have a solid grasp of the relationship between applications and their resources to meet SLAs and other goals.

Since mainframes are such a big part of many businesses, mainframe performance management is crucial for evaluating and understanding the goals of a business’ IT section about what the business needs.

Capacity planning, for example, refers to making the best out of the resources a business has, as well as picking places where investing in upgrades or new technology would be smartest. In case a business upgrades its tech without proper capacity planning, it’ll inevitably face bottlenecks and not reap the full benefits of its upgrades. Gathering enough data and interpreting it correctly to facilitate capacity planning can be quite difficult.

All in all, businesses often face challenges when it comes to collecting and interpreting all of the data they need to adjust to an ever-changing IT environment. This can lead to improper decision-making and issues with risk assessment, which will ultimately affect the business’ performance.

How is Today’s Needs Different From Before?

A lot of enterprises are very tool-heavy when it comes to capacity and performance management, however, a lot of these tools overlap. While this overlap is inefficient, the alternative for most businesses is to develop an in-house solution over multiple years. These tools often lack seemingly peripheral features such as reporting. This usually happens due to assumptions in the development process, or because employee workloads and their impact on performance have been altered since development. The best solution for this is to rely on highly specialized tools for each part of the infrastructure.

Since mainframes are part of an integrated IT infrastructure, their workloads are growing due to the prevalence of API-reliant hybrid applications. Because of this, the expectations for problem-solving are also changing. For example, a few years ago, most reports like SMF records were generated the day after an event. Since the volume of data required to produce a report like this, businesses found it inefficient to produce them during peak operating hours.

Today, relying on next-day reporting is a considerable issue. Businesses can’t wait a whole day to find the root cause of critical performance flaws, or to alter their production strategy due to a business-wide change. Furthermore, self-service accessibility of information due to the internet has enabled users, managers, and even stakeholders to access information at their whim, rather than relying on a couple of report builders. If your business is relying on insufficient reports that don’t give enough info to properly manage workloads and resources, you’ll face increasing service and operating costs.

Another massive factor in this change is the increasing prevalence of cloud storage and computing. Today, it’s simple to save your data in a data center in Shanghai and access it from Milan. However, this means that the business handling this data has to take great care that the data can’t be accessed by malicious users and that it’s stored in as efficient a way as possible. This is how many companies that have mainframes running in the back use them to bring more convenience to their users.

Finally, the rise of individuals working from home, and worldwide offices cooperating on a day-to-day basis has led to more data needing to be stored and shared among employees efficiently. This kind of transfer is extremely reliant on having an efficient CPU to back it up. Since CPUs have an intrinsic limit on how well they can promptly and accurately transfer data, optimizing your mainframe is crucial to keep up with the ever-increasing dataflows demanded of businesses.

Optimizing Mainframe Performance

Mainframes still present a crucial backend element for transaction processing and business logic in most enterprises around the world. With more modern initiatives like the increasing reliance on AI and cloud technologies growing, mainframe workloads are steadily growing.

The past couple of years has presented growing customer demands for convenience and cross-platform compatibility in applications, leading to a further increase in needed computing power. Some of these troubles have been lessened by lower data center costs, however, with the COVID-19 pandemic and a growing work from the home ecosystem, businesses have been forced to embrace the digital revolution.

Reducing mainframe capacity is one way to ensure higher performance. However, the applications related to the mainframe will suffer, leading to dissatisfied businesses. However, amidst the pandemic and dropping profits, most IT departments can’t afford to simply purchase larger mainframe capacities.

Because of this, it’s ideal to look to optimize your current mainframe capacity. By using performance and capacity management software to understand how you use existing capacity and alter it to achieve more growth.

Use zIIP to Optimize Processing

The IBM Series z mainframes have an integrated information processor(zIIP) that can significantly impact the CPU processing requirements for multiple kinds of workloads. Many tasks like sorting CPU cycles, SMS Compression, and the like, can be offloaded to zIIP and get up to a 25% performance boost with no costs outside of purchasing the hardware.

Make More Efficient Code

In modern times, code efficiency is an often understated aspect of performance. Badly written COBOL or SQL code can take a massive toll on your mainframe’s performance. Some relatively common practices like sorting or rounding uneven numbers in COBOL are very inefficient. When permissible, it can be a good idea to revisit your application’s code to see if its efficiency can be improved.

Use Mainframe Performance Tools

Using a mainframe performance management software lets you take a visual and data-centered approach to ensure peak mainframe performance. With the right tool, you’ll be able to:

  • Accurately predict future MIPS (Millions of Instructions Per Second) consumption per workload by changing business practices and requirements. You’ll be alerted in advance if your current loadout is unlikely to be able to handle the load.
  • Find the workloads that are taking up most of your CPU resources, as well as the cause of CPU spikes. This lets you address them, whether it be by optimizing code or implementing a different solution.
  • Find where you can offload a lot of the requirements to zIIPS rather than MIPS, this lets you save MIPS for tasks that require it while using the cheaper zIIPS for other tasks.
  • Detect the best config settings for LPARs (logical partitions) and determine the effect of potential changes on your CPU usage.
  • Find the root cause of the most common issues without using man-hours to do so via automated systems.
  • Determine which workloads are taking up more than their fair share of MIPS, and which subprocesses are responsible for this so you can optimize them further.
  • Finding where pre-defined capacities for z/OS LPARs can help you control consumption. Doing this without a tool can often cause issues with LPAR CPU access.
  • Find where LPAR faces delays when accessing processor resources.

Risks Posed By Mainframe Modernization

  • Expert Shortage COBOL is not a popular language among aspiring and new developers. With more and more experts retiring, there’s a question of where to find proper COBOL professionals.
  • Mainframes Are Resistant to Change While mainframes are nearly unparalleled in terms of performance, security, and reliability, they’re difficult to quickly adapt and innovate with. With apps being designed faster and faster, and a higher emphasis being placed on AGILE and similar methodologies, it can be difficult to adapt massive monolithic mainframes to these changes.
  • Costs Mainframes are not only expensive to purchase, but also quite expensive to maintain, with some major mainframe vendors paying billions just for software and infrastructure fees. Thankfully, there’s been some headway in this area recently.

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