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The Impact of Storage on Application Performance

Infrastructures have come a long way in the last five years, but one device that is lagging behind is storage arrays.

Sure, arrays are faster and easier to configure, but with applications riding on top of virtualization using shared storage, arrays are often the hidden cause of application performance issues.

These issues are often difficult to pinpoint because the symptoms are often transient. The true problem is several levels away from the symptoms, and most monitoring tools can only look at parts of the problem, making diagnosis very difficult. 

How do IT professionals tell if they have a storage performance issue?

Generally, users should watch several key performance indicators (KPI) on both their applications and systems:

Server and Application

At this level, users need to monitor latency and determine how long their application is waiting on storage to return data. For example, Microsoft recommends that storage for Exchange return data in 20 milliseconds or less, or it could negatively affect the application. 

Virtualization

For VMware and other hypervisors, the big issue here is that storage can be a shared resource. Contention can arise as VMs fight for storage I/O, therefore users need to pay attention to latency and total I/O for VM and datastores but with consideration of the CPU and network load as well – high latency with low I/O could be a host issue. If contention is suspected at the hypervisor level, it typically can be solved by moving VMs or moving to faster storage.

Note that VMware vSphere 5 includes Storage vMotion to help smooth some of these issues, but there is only so much it can do before the user will need to step in.

Storage

Arrays vary in their architectures and capabilities, but in general, users need to monitor LUNs and RAID Groups to look for contention in the array and controllers and ports for overloading.

If a user is experiencing high application latency, but doesn't see any problem at the server or hypervisor level, there may be contention in the array as LUNs vie for storage I/O. If more than one LUN shares a set of disks, then a completely unrelated application could be affecting performance, an issue that would be clearly visible at the array level.

Overloaded controllers or ports will generally slow down all the applications on those LUNs, making part of the infrastructure seem sluggish. The remedy is generally reconfiguring the loads to different disks, ports or controllers. 

Storage performance is one of the big challenges for application administrators today, especially since diagnosis is not always simple. It's important to have tools that can dive into different domains (server, app, virtualization, storage) and dive deep to get to the heart of the issue.

As a final note, planning goes a long way in avoiding storage issues (as with anything else). It is critical to measure or estimate average and peak I/O loads of applications and then place them on the appropriate "tier" of storage. Spending time up front to account for the expected loads (and growth) will help everyone sleep better at night.  

About Jonathan Reeve

Jonathan Reeve, Senior Director of Product Management at SolarWinds, has built a career integrating hands-on technical development with senior-level strategic management. Having previously served as the VP of Product Strategy for Hyper9, Reeve was responsible for the company's flagship product, Virtual Environment Optimization suite. His experience spans computer networking, systems management and virtualization technologies, helping numerous start-ups and established companies generate market traction. Prior to joining Hyper9, Reeve drove product management for the network management product line at Smarts, which was acquired by EMC in 2005. He has a degree in Electrical Engineering and a PhD in Computer Networking from the University of Durham (UK).

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The Impact of Storage on Application Performance

Infrastructures have come a long way in the last five years, but one device that is lagging behind is storage arrays.

Sure, arrays are faster and easier to configure, but with applications riding on top of virtualization using shared storage, arrays are often the hidden cause of application performance issues.

These issues are often difficult to pinpoint because the symptoms are often transient. The true problem is several levels away from the symptoms, and most monitoring tools can only look at parts of the problem, making diagnosis very difficult. 

How do IT professionals tell if they have a storage performance issue?

Generally, users should watch several key performance indicators (KPI) on both their applications and systems:

Server and Application

At this level, users need to monitor latency and determine how long their application is waiting on storage to return data. For example, Microsoft recommends that storage for Exchange return data in 20 milliseconds or less, or it could negatively affect the application. 

Virtualization

For VMware and other hypervisors, the big issue here is that storage can be a shared resource. Contention can arise as VMs fight for storage I/O, therefore users need to pay attention to latency and total I/O for VM and datastores but with consideration of the CPU and network load as well – high latency with low I/O could be a host issue. If contention is suspected at the hypervisor level, it typically can be solved by moving VMs or moving to faster storage.

Note that VMware vSphere 5 includes Storage vMotion to help smooth some of these issues, but there is only so much it can do before the user will need to step in.

Storage

Arrays vary in their architectures and capabilities, but in general, users need to monitor LUNs and RAID Groups to look for contention in the array and controllers and ports for overloading.

If a user is experiencing high application latency, but doesn't see any problem at the server or hypervisor level, there may be contention in the array as LUNs vie for storage I/O. If more than one LUN shares a set of disks, then a completely unrelated application could be affecting performance, an issue that would be clearly visible at the array level.

Overloaded controllers or ports will generally slow down all the applications on those LUNs, making part of the infrastructure seem sluggish. The remedy is generally reconfiguring the loads to different disks, ports or controllers. 

Storage performance is one of the big challenges for application administrators today, especially since diagnosis is not always simple. It's important to have tools that can dive into different domains (server, app, virtualization, storage) and dive deep to get to the heart of the issue.

As a final note, planning goes a long way in avoiding storage issues (as with anything else). It is critical to measure or estimate average and peak I/O loads of applications and then place them on the appropriate "tier" of storage. Spending time up front to account for the expected loads (and growth) will help everyone sleep better at night.  

About Jonathan Reeve

Jonathan Reeve, Senior Director of Product Management at SolarWinds, has built a career integrating hands-on technical development with senior-level strategic management. Having previously served as the VP of Product Strategy for Hyper9, Reeve was responsible for the company's flagship product, Virtual Environment Optimization suite. His experience spans computer networking, systems management and virtualization technologies, helping numerous start-ups and established companies generate market traction. Prior to joining Hyper9, Reeve drove product management for the network management product line at Smarts, which was acquired by EMC in 2005. He has a degree in Electrical Engineering and a PhD in Computer Networking from the University of Durham (UK).

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...