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Driving a Simple Performance Baseline

Larry Dragich

Adopting an Application Performance Management (APM) strategy will help you manage the quality of the customer experience. The challenge is that APM has evolved into a mosaic of monitoring tools, analytic engines, and event processors that provide many solutions to different problem sets.

When you step back and look at the big picture it all comes into focus, but when you’re trying to rationalize one technology over another, things aren't so clear at close range. Product overlaps, ongoing costs, and ownership come into question, and then someone will eventually inquire about the benefits they are receiving from these tools sets.

You will be tempted to demonstrate all the bells and whistles the products provide in hoping to convince them that the value outweighs the complexity. Don't overlook the profound impact a childlike view can have with understanding application performance. “Sometimes the questions are complicated and the answers are simple.” - Theodor Seuss Geisel (aka. Dr. Seuss)

It is important to show how an APM solution can tie into existing systems, by articulating the big picture, so that IT leaders can conceptualize the value coming from the new solution. How you articulate the Manager of Managers (MoM) concept and how it will support the APM strategy is essential for buy-in.

As you begin, start by identifying the dual purpose toolsets (i.e. provisioning and monitoring) already in the organization to incorporate as part of the APM solution. Then consider choosing a technology that is “application aware” to spear-head your real-user-monitoring (RUM) initiative. When done correctly, Application Aware Network Performance Monitoring (AANPM) can become the linkage between silos, providing relevant performance data in a context that all groups will understand, and subsequently trust. For more on this read, APM & MoM - Symbiotic Solution Sets.

I have found that the simplicity and ease of use with agent-less monitoring (i.e. wire data analytics) is a great place to start. It should have the capability to provide insight for the protocols specific to your critical applications (e.g. XML, SQL, PHP, etc.).

Since agentless monitoring is “always on” it will be ready to monitor any new applications launched into production within its purview. There’s no need to worry about managing a fully burdened application life cycle for a typical agent installation, although when the time comes agent monitoring is recommended to fully instrument an application.

For example, when expanding a critical business application across our northern locations we used agentless monitoring to present a dashboard that answered specific performance questions about availability and user response times. It was a basic graph with two data elements across one dimension of time, making it easy to compare the current end-user-experience (EUE) to its normal baseline. A somewhat elementary but very effective way to communicate the real-time performance back to the developers and IT leadership at the same time during the rollout. See Figure 1.






As the application usage increased with user load, we noticed that performance rose well above the normal baseline. We observed an increase in application operation time, which had a direct correlation to page aborts, indicating user frustration. Then the system reached the point of critical mass and started presenting HTTP 500 errors. Based on a simple performance breakdown showing server time over its threshold and network time within baseline, the developers were directly engaged and the network team was on standby. See Figure 2.



Click on Figure 2 below for a larger image


Given this starting point, the development team began troubleshooting the performance discrepancy early on and before mid-day they had identified the root cause and added a new Index to one of the very large data sets that was being called frequently.

Conclusion

As you strive to achieve new levels of sophistication when creating performance dashboards, don’t overlook the simplicity of highlighting just a few metrics on one page that mean something to the support team. This will take an understanding of the application and knowledge of how the metrics are being collected to be succinct. Be patient, just as water seeks its own level, an application performance baseline will eventually emerge as you track the real-time performance metrics outlining the high and low watermarks of the application.

For further insight, Click here for the full article.

You can contact Larry on LinkedIn.

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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 ...

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

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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 ...

Driving a Simple Performance Baseline

Larry Dragich

Adopting an Application Performance Management (APM) strategy will help you manage the quality of the customer experience. The challenge is that APM has evolved into a mosaic of monitoring tools, analytic engines, and event processors that provide many solutions to different problem sets.

When you step back and look at the big picture it all comes into focus, but when you’re trying to rationalize one technology over another, things aren't so clear at close range. Product overlaps, ongoing costs, and ownership come into question, and then someone will eventually inquire about the benefits they are receiving from these tools sets.

You will be tempted to demonstrate all the bells and whistles the products provide in hoping to convince them that the value outweighs the complexity. Don't overlook the profound impact a childlike view can have with understanding application performance. “Sometimes the questions are complicated and the answers are simple.” - Theodor Seuss Geisel (aka. Dr. Seuss)

It is important to show how an APM solution can tie into existing systems, by articulating the big picture, so that IT leaders can conceptualize the value coming from the new solution. How you articulate the Manager of Managers (MoM) concept and how it will support the APM strategy is essential for buy-in.

As you begin, start by identifying the dual purpose toolsets (i.e. provisioning and monitoring) already in the organization to incorporate as part of the APM solution. Then consider choosing a technology that is “application aware” to spear-head your real-user-monitoring (RUM) initiative. When done correctly, Application Aware Network Performance Monitoring (AANPM) can become the linkage between silos, providing relevant performance data in a context that all groups will understand, and subsequently trust. For more on this read, APM & MoM - Symbiotic Solution Sets.

I have found that the simplicity and ease of use with agent-less monitoring (i.e. wire data analytics) is a great place to start. It should have the capability to provide insight for the protocols specific to your critical applications (e.g. XML, SQL, PHP, etc.).

Since agentless monitoring is “always on” it will be ready to monitor any new applications launched into production within its purview. There’s no need to worry about managing a fully burdened application life cycle for a typical agent installation, although when the time comes agent monitoring is recommended to fully instrument an application.

For example, when expanding a critical business application across our northern locations we used agentless monitoring to present a dashboard that answered specific performance questions about availability and user response times. It was a basic graph with two data elements across one dimension of time, making it easy to compare the current end-user-experience (EUE) to its normal baseline. A somewhat elementary but very effective way to communicate the real-time performance back to the developers and IT leadership at the same time during the rollout. See Figure 1.






As the application usage increased with user load, we noticed that performance rose well above the normal baseline. We observed an increase in application operation time, which had a direct correlation to page aborts, indicating user frustration. Then the system reached the point of critical mass and started presenting HTTP 500 errors. Based on a simple performance breakdown showing server time over its threshold and network time within baseline, the developers were directly engaged and the network team was on standby. See Figure 2.



Click on Figure 2 below for a larger image


Given this starting point, the development team began troubleshooting the performance discrepancy early on and before mid-day they had identified the root cause and added a new Index to one of the very large data sets that was being called frequently.

Conclusion

As you strive to achieve new levels of sophistication when creating performance dashboards, don’t overlook the simplicity of highlighting just a few metrics on one page that mean something to the support team. This will take an understanding of the application and knowledge of how the metrics are being collected to be succinct. Be patient, just as water seeks its own level, an application performance baseline will eventually emerge as you track the real-time performance metrics outlining the high and low watermarks of the application.

For further insight, Click here for the full article.

You can contact Larry on LinkedIn.

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 ...