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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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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

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...