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Rampant I/O Demands Crippling Storage Performance

Organizations struggle to gain the full lifecycle from their backend storage due to I/O growth
Brian Morin

When New York's Triborough Bridge opened in 1936, it was widely viewed as the end of traffic congestion. And it was – for several months. But its architect soon realized that better roadways and better bridges inevitably led to more traffic on the road. It's no different in IT.

The Input/Output Operations per Second (I/O) capabilities of modern computer systems are truly a modern wonder. Yet no matter how powerful the processors, no matter how many cores, how perfectly formed the bus architecture, or how many flash modules are added, somehow it never seems to be enough. While existing applications flourish on the latest platforms, software designers are quick to design new ones to take advantage of all this new potential. Result: just like New York City – traffic congestion in IT systems becomes a fact of life.

This is confirmed by a newly released survey by Condusiv Technologies, the third annual I/O Performance Survey, which consulted over 1,400 IT professionals and revealed some startling facts. Organizations have been keen to adopt the latest in all-flash arrays, hybrid arrays, hyperconverged architectures, PCI-e flash cards, and servers with even more cores as a solution to their performance woes. Yet a full 27 percent continue to receive user complaints about sluggish performance on mission-critical applications such as MS-SQL. They see little option but to add yet more expensive hardware to alleviate the many bottlenecks they are dealing with.

The bulk of these organizations are operating heavily virtualized Windows environments. Almost half of them admit serious problems supporting one or two of their most demanding applications. The specifics vary from business to business, but encompass the likes of MS-SQL, SAP, Oracle, Microsoft Exchange, various ERP tools, CRM, databases, BI, analytics, VMware, VDI, Splunk, financials and security applications.

A full 30 percent of those surveyed believe that the growth of I/O from applications has outpaced the useful lifecycle they expected from their underlying storage architecture. Only 41 percent consider that they are able to cost-effectively keep up with the growth of I/O. The rest face a grim future of budget restrictions in the face of urgent demands to add more server and storage hardware.

They find themselves with some tough choices. Should they grit their teeth and endure these performance issues until the next budget cycle, and hope there is some leeway in next year's budget to purchase new hardware? Or should they divert money away from other urgent IT initiatives to solve the incessant flood of user complaints?

While the latest and greatest hardware is always going to make a difference, it doesn't solve the problem in the long run. As the architects of our road systems have found, newer, wider and better roads only lead to more traffic and eventual gridlock. That's why they are turning to software as a key part of their vision for a better future. They are working hard on initiatives such as driverless cars, traffic flow optimization software, and vast road sensor networks feeding data into to traffic flow analytics systems. Certainly, such systems must be supported by new hardware. But software is being turned to as the ultimate solution to city congestion.

Similarly, in the world of ever rising I/O demands, software solutions are emerging that provide relief from performance bottlenecks. The various approaches to I/O streamlining and reduction include caching, micro-tiering, fragmentation prevention and performance tuning. They offer a way to better performance now without breaking the bank.

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.

Rampant I/O Demands Crippling Storage Performance

Organizations struggle to gain the full lifecycle from their backend storage due to I/O growth
Brian Morin

When New York's Triborough Bridge opened in 1936, it was widely viewed as the end of traffic congestion. And it was – for several months. But its architect soon realized that better roadways and better bridges inevitably led to more traffic on the road. It's no different in IT.

The Input/Output Operations per Second (I/O) capabilities of modern computer systems are truly a modern wonder. Yet no matter how powerful the processors, no matter how many cores, how perfectly formed the bus architecture, or how many flash modules are added, somehow it never seems to be enough. While existing applications flourish on the latest platforms, software designers are quick to design new ones to take advantage of all this new potential. Result: just like New York City – traffic congestion in IT systems becomes a fact of life.

This is confirmed by a newly released survey by Condusiv Technologies, the third annual I/O Performance Survey, which consulted over 1,400 IT professionals and revealed some startling facts. Organizations have been keen to adopt the latest in all-flash arrays, hybrid arrays, hyperconverged architectures, PCI-e flash cards, and servers with even more cores as a solution to their performance woes. Yet a full 27 percent continue to receive user complaints about sluggish performance on mission-critical applications such as MS-SQL. They see little option but to add yet more expensive hardware to alleviate the many bottlenecks they are dealing with.

The bulk of these organizations are operating heavily virtualized Windows environments. Almost half of them admit serious problems supporting one or two of their most demanding applications. The specifics vary from business to business, but encompass the likes of MS-SQL, SAP, Oracle, Microsoft Exchange, various ERP tools, CRM, databases, BI, analytics, VMware, VDI, Splunk, financials and security applications.

A full 30 percent of those surveyed believe that the growth of I/O from applications has outpaced the useful lifecycle they expected from their underlying storage architecture. Only 41 percent consider that they are able to cost-effectively keep up with the growth of I/O. The rest face a grim future of budget restrictions in the face of urgent demands to add more server and storage hardware.

They find themselves with some tough choices. Should they grit their teeth and endure these performance issues until the next budget cycle, and hope there is some leeway in next year's budget to purchase new hardware? Or should they divert money away from other urgent IT initiatives to solve the incessant flood of user complaints?

While the latest and greatest hardware is always going to make a difference, it doesn't solve the problem in the long run. As the architects of our road systems have found, newer, wider and better roads only lead to more traffic and eventual gridlock. That's why they are turning to software as a key part of their vision for a better future. They are working hard on initiatives such as driverless cars, traffic flow optimization software, and vast road sensor networks feeding data into to traffic flow analytics systems. Certainly, such systems must be supported by new hardware. But software is being turned to as the ultimate solution to city congestion.

Similarly, in the world of ever rising I/O demands, software solutions are emerging that provide relief from performance bottlenecks. The various approaches to I/O streamlining and reduction include caching, micro-tiering, fragmentation prevention and performance tuning. They offer a way to better performance now without breaking the bank.

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.