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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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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...