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APM and Viewpoints - Part 1

Terry Critchley

Application Performance Monitoring (APM) is a set of disciplines, part of Performance Management, designed provide accurate information on how business applications are performing. Many organizations rely on APM to give them sufficient information to see if their internally‐developed applications and third party applications are performing well. The purpose of this exercise is both operational and, in the longer term, capacity planning purposes.

The overarching reason is to match delivered performance with the service level agreements (SLAs) developed between IT and the business(es). There are other reasons, not least those of organization productivity and external customer acceptance of the online service, particularly web sites.

The cruel fact of the matter is that poor or erratic performance (response times and throughput) are bad for business. Zero performance when the system is down doesn't help the cause either. As an aside, note that availability is an essential component of performance.

Aspects of Performance

There are several aspects of applications and related software that need to be monitored since an application makes use of other software in its execution. The number of aspects needing consideration depends on the complexity of the supporting environment. Typically, IT personnel will need to be aware, at a detailed level, of the performance of:

■ Internet services

■ Response times (overall)

■ Network traffic and latency

■ Transaction tracking (visibility) where applicable

■ The infrastructure - operating system, hypervisors

■ Database

■ Web server software

■ Other middleware

■ ERP and other application systems. These sometimes have their own resource and reporting monitors.

■ File servers, messaging systems etc.

■ Use of what are known as "deep dive diagnostics" for knotty problems

An important aspect of performance (and other) monitoring is where the observer stands when looking at the IT scenario. If a complaint says the performance of an application is dreadful, the network man might say "Everything is fine" and the database man may agree, both saying "What's the problem?" All these people may say that the performance world is rosy but not to other people who have a different idea on what is rosy and what is not.

These are what I call viewpoints, a popular concept in IT architecture design method. Read APM and Viewpoints - Part 2, outlining the different viewpoints.

Dr. Terry Critchley is the Author of "Making It in IT", "High Performance IT Services" and “High Availability IT Services”.

This blog was created from extracts from Terry Critchley's book: High Performance IT Services [ August 25 2016]

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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 ...

APM and Viewpoints - Part 1

Terry Critchley

Application Performance Monitoring (APM) is a set of disciplines, part of Performance Management, designed provide accurate information on how business applications are performing. Many organizations rely on APM to give them sufficient information to see if their internally‐developed applications and third party applications are performing well. The purpose of this exercise is both operational and, in the longer term, capacity planning purposes.

The overarching reason is to match delivered performance with the service level agreements (SLAs) developed between IT and the business(es). There are other reasons, not least those of organization productivity and external customer acceptance of the online service, particularly web sites.

The cruel fact of the matter is that poor or erratic performance (response times and throughput) are bad for business. Zero performance when the system is down doesn't help the cause either. As an aside, note that availability is an essential component of performance.

Aspects of Performance

There are several aspects of applications and related software that need to be monitored since an application makes use of other software in its execution. The number of aspects needing consideration depends on the complexity of the supporting environment. Typically, IT personnel will need to be aware, at a detailed level, of the performance of:

■ Internet services

■ Response times (overall)

■ Network traffic and latency

■ Transaction tracking (visibility) where applicable

■ The infrastructure - operating system, hypervisors

■ Database

■ Web server software

■ Other middleware

■ ERP and other application systems. These sometimes have their own resource and reporting monitors.

■ File servers, messaging systems etc.

■ Use of what are known as "deep dive diagnostics" for knotty problems

An important aspect of performance (and other) monitoring is where the observer stands when looking at the IT scenario. If a complaint says the performance of an application is dreadful, the network man might say "Everything is fine" and the database man may agree, both saying "What's the problem?" All these people may say that the performance world is rosy but not to other people who have a different idea on what is rosy and what is not.

These are what I call viewpoints, a popular concept in IT architecture design method. Read APM and Viewpoints - Part 2, outlining the different viewpoints.

Dr. Terry Critchley is the Author of "Making It in IT", "High Performance IT Services" and “High Availability IT Services”.

This blog was created from extracts from Terry Critchley's book: High Performance IT Services [ August 25 2016]

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 ...