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Maintaining the Mainframe Performance Advantage in a Mobile Intensive World

Spencer Hallman

By now, we all know the importance of superior application performance. Applications that are fast, reliable and easy to use delight end-users and lead to greater adoption. But for mainframe applications, performance takes on a whole new level of importance. For these applications, even a few added milliseconds in application load or transaction time can lead to application abandonment and lost revenues.

Why? Surely, mainframes process so many transactions for so many people that even a slight improvement in processing time can have an impact on millions of end users. For example, a leading bank recently saw that a mainframe application was taking too long to make a database call, increasing from three milliseconds on average to five milliseconds. While this may seem like a trivial time increase, it caused more than three million transactions during a critical period to slow way down, or even time out. After identifying and fixing the problem, the bank was able to bring response time levels back to normal. Given the sheer number of transactions affected, the impact on customer satisfaction and the overall business was enormous.

But beyond the end-user experience, it is equally important to manage mainframe application performance from a resource efficiency perspective, since problems here can also result in huge costs to the business downstream. This is especially true as trends like cloud, mobile and analytics, and the availability of the new IBM z13, push increased workloads to the mainframe.

Mainframe transaction processing is very cost-effective – even more than commodity servers in many instances. This is because as many businesses experience massive increases in computational loads, the mainframe has decreased in unit cost enough to offset changes in volume – more so than commodity servers.

The mainframe is inherently more scalable than most commodity servers – a reason it has long been a platform of choice for critical transaction processing, along with superior reliability and security. But one potential danger of mainframes is how monthly license charges (MLC) for mainframe software often consume more than 30 percent of mainframe budgets. This can lead to costs spiraling out of control, especially as mobile apps become even more ubiquitous. Consider that a single mobile transaction often triggers a cascade of related events across systems, including such things as comparison to past purchases, business to business reconciliation (like those between banks), customer loyalty and rewards program updates, and many other examples – a phenomenon known as the “starburst effect.” Businesses using mainframes need to keep an eye on MLC costs – otherwise, the promise of low TCO may be endangered.

The global thirst for more computing capacity continues to grow. The mainframe has a very real place in this new paradigm. But the key to maximizing the significant cost-savings and overall business performance advantages the mainframe offers lies not just in managing application performance from an end-user experience perspective – but also from the critical perspective of resource utilization and consumption.

Spencer Hallman is Subject Matter Expert for Compuware.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Maintaining the Mainframe Performance Advantage in a Mobile Intensive World

Spencer Hallman

By now, we all know the importance of superior application performance. Applications that are fast, reliable and easy to use delight end-users and lead to greater adoption. But for mainframe applications, performance takes on a whole new level of importance. For these applications, even a few added milliseconds in application load or transaction time can lead to application abandonment and lost revenues.

Why? Surely, mainframes process so many transactions for so many people that even a slight improvement in processing time can have an impact on millions of end users. For example, a leading bank recently saw that a mainframe application was taking too long to make a database call, increasing from three milliseconds on average to five milliseconds. While this may seem like a trivial time increase, it caused more than three million transactions during a critical period to slow way down, or even time out. After identifying and fixing the problem, the bank was able to bring response time levels back to normal. Given the sheer number of transactions affected, the impact on customer satisfaction and the overall business was enormous.

But beyond the end-user experience, it is equally important to manage mainframe application performance from a resource efficiency perspective, since problems here can also result in huge costs to the business downstream. This is especially true as trends like cloud, mobile and analytics, and the availability of the new IBM z13, push increased workloads to the mainframe.

Mainframe transaction processing is very cost-effective – even more than commodity servers in many instances. This is because as many businesses experience massive increases in computational loads, the mainframe has decreased in unit cost enough to offset changes in volume – more so than commodity servers.

The mainframe is inherently more scalable than most commodity servers – a reason it has long been a platform of choice for critical transaction processing, along with superior reliability and security. But one potential danger of mainframes is how monthly license charges (MLC) for mainframe software often consume more than 30 percent of mainframe budgets. This can lead to costs spiraling out of control, especially as mobile apps become even more ubiquitous. Consider that a single mobile transaction often triggers a cascade of related events across systems, including such things as comparison to past purchases, business to business reconciliation (like those between banks), customer loyalty and rewards program updates, and many other examples – a phenomenon known as the “starburst effect.” Businesses using mainframes need to keep an eye on MLC costs – otherwise, the promise of low TCO may be endangered.

The global thirst for more computing capacity continues to grow. The mainframe has a very real place in this new paradigm. But the key to maximizing the significant cost-savings and overall business performance advantages the mainframe offers lies not just in managing application performance from an end-user experience perspective – but also from the critical perspective of resource utilization and consumption.

Spencer Hallman is Subject Matter Expert for Compuware.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.