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End User Monitoring 101: Tools - Part 2

Larry Haig

End User Monitoring 101: Tools Part 1

The first aspect of Front End Optimization (FEO) practice in Operations is understanding the outturn performance to external end points (usually end users). This is achieved through monitoring, that is, obtaining an objective understanding of transaction, page, or page component response from replicate tests in known conditions, or of site visitors over time.

Monitoring provides information relative to patterns of response of the target site or application, both absolute and relative to key competitors or other comparators.Tools for monitoring of external performance fall into two distinct types: active or passive.

Active (also called Synthetic) monitoring involves replicate testing from known external locations. Data captured is essentially based on reporting on the network interactions between the test node and the target site.

i) Understanding the availability of the target site.

ii) Understanding site response/patterns in consistent test conditions – for example to determine long term trends, the effect of visitor traffic load, performance in low traffic periods, or objective comparison with competitor (or other comparator) sites.

iii) Understanding response/patterns of individual page components. These can be variations in the response of the various elements of the object delivery chain – DNS resolution, Initial connection, First byte (i.e. the dwell time between the connection handshake and the commencement of data transfer over the connection – a measure of infrastructure latency), and content delivery time. Alternatively, the objective may be to understand the variation in total response time of a specific element, for example 3rd Party content (useful for Service Level Agreement management).

Increasingly, modern Application Performance Management (APM) tools offer a synthetic monitoring option. These tend to be useful in the context of the APM – i.e. holistic, ongoing performance understanding, but more limited in terms of control of test conditions and specific granular aspects of FEO point analysis such as Single Point Of Failure (SPOF) testing of third party content.

In brief, key aspects of such tooling for FEO analysis are:

■ Range of external locations – geography and type
- e.g. Tier 1 ISP/LINX test locations; end user locations; private peer (i.e. specific known test source)
- PC and mobile (the latter increasingly important)

■ Control of connection conditions – hardwired vs wireless; connection bandwidth

■ Ease & sophistication of transaction scripting – introducing cookies, filtering content, coping with dynamic content (popups etc.)

■ Control of recorded page load end point

As a rule of thumb, the more control the better. However, a good compromise position is to take whatever is on offer from the APM vendor – provided you are clear as to exactly what is being captured; and supplement this with a ‘full fat' tool that is more analysis-centric – WebPageTest being a popular, open source choice – though beware variable test node environments if using the public network.

A final word on page load end points. "Traditional" synthetic tools (such as Gomez/dynaTrace synthetic in the above example) relied on the page onload navigation marker. It really is essential to define an end point more closely based on end user experience – i.e. browser fill time. With older tools this needs to be done by introducing a flag to the page. This can either be existing content such as an image appearing at the base of the page (at a given screen resolution), or by introducing such content at the appropriate point. This marker can then be recorded by modification of the test script.

Note that, given the dynamic nature of many sites, attempting to time to a particular visual component can be a short lived gambit. Introducing your own marker, assuming that you have access to the code, is a more robust intervention.

Some modern tooling have introduced this as a standard feature. It is likely that competitors will follow suit. Use of the onload marker will produce results that do not bear any meaningful relationship to end user experience, particularly in sites with high affiliate content loads.

Modifications of standard testing to meet the requirements/manage misleading results in specific cases e.g. server push, Single Page Applications, will be covered in a subsequent post.

Larry Haig is Senior Consultant at Intechnica.

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End User Monitoring 101: Tools - Part 2

Larry Haig

End User Monitoring 101: Tools Part 1

The first aspect of Front End Optimization (FEO) practice in Operations is understanding the outturn performance to external end points (usually end users). This is achieved through monitoring, that is, obtaining an objective understanding of transaction, page, or page component response from replicate tests in known conditions, or of site visitors over time.

Monitoring provides information relative to patterns of response of the target site or application, both absolute and relative to key competitors or other comparators.Tools for monitoring of external performance fall into two distinct types: active or passive.

Active (also called Synthetic) monitoring involves replicate testing from known external locations. Data captured is essentially based on reporting on the network interactions between the test node and the target site.

i) Understanding the availability of the target site.

ii) Understanding site response/patterns in consistent test conditions – for example to determine long term trends, the effect of visitor traffic load, performance in low traffic periods, or objective comparison with competitor (or other comparator) sites.

iii) Understanding response/patterns of individual page components. These can be variations in the response of the various elements of the object delivery chain – DNS resolution, Initial connection, First byte (i.e. the dwell time between the connection handshake and the commencement of data transfer over the connection – a measure of infrastructure latency), and content delivery time. Alternatively, the objective may be to understand the variation in total response time of a specific element, for example 3rd Party content (useful for Service Level Agreement management).

Increasingly, modern Application Performance Management (APM) tools offer a synthetic monitoring option. These tend to be useful in the context of the APM – i.e. holistic, ongoing performance understanding, but more limited in terms of control of test conditions and specific granular aspects of FEO point analysis such as Single Point Of Failure (SPOF) testing of third party content.

In brief, key aspects of such tooling for FEO analysis are:

■ Range of external locations – geography and type
- e.g. Tier 1 ISP/LINX test locations; end user locations; private peer (i.e. specific known test source)
- PC and mobile (the latter increasingly important)

■ Control of connection conditions – hardwired vs wireless; connection bandwidth

■ Ease & sophistication of transaction scripting – introducing cookies, filtering content, coping with dynamic content (popups etc.)

■ Control of recorded page load end point

As a rule of thumb, the more control the better. However, a good compromise position is to take whatever is on offer from the APM vendor – provided you are clear as to exactly what is being captured; and supplement this with a ‘full fat' tool that is more analysis-centric – WebPageTest being a popular, open source choice – though beware variable test node environments if using the public network.

A final word on page load end points. "Traditional" synthetic tools (such as Gomez/dynaTrace synthetic in the above example) relied on the page onload navigation marker. It really is essential to define an end point more closely based on end user experience – i.e. browser fill time. With older tools this needs to be done by introducing a flag to the page. This can either be existing content such as an image appearing at the base of the page (at a given screen resolution), or by introducing such content at the appropriate point. This marker can then be recorded by modification of the test script.

Note that, given the dynamic nature of many sites, attempting to time to a particular visual component can be a short lived gambit. Introducing your own marker, assuming that you have access to the code, is a more robust intervention.

Some modern tooling have introduced this as a standard feature. It is likely that competitors will follow suit. Use of the onload marker will produce results that do not bear any meaningful relationship to end user experience, particularly in sites with high affiliate content loads.

Modifications of standard testing to meet the requirements/manage misleading results in specific cases e.g. server push, Single Page Applications, will be covered in a subsequent post.

Larry Haig is Senior Consultant at Intechnica.

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 ...