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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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 ...