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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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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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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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