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How to Ensure APM Success

Keith Bromley

A recent APMdigest blog by Jean Tunis, The Evolving Needs of Application Performance Monitoring - Part 2, provided an excellent background on Application Performance Monitoring (APM) and what it does. APM solution benefits are much more understood than in years past. An interesting data point from Gartner Inc. mentioned in the article confirms this, stating that IT departments are planning to increase the use of APM solutions to monitor their applications from 5% in 2018 to a projected 20% in 2021.

A further topic that I wanted to touch on though is the need for good quality data. If you are to get the most out of your APM solution possible, you will need to feed it with the best quality data. Irrelevant data, fragmented data, and corrupt data are all common culprits that either end up decreasing the speed to resolution, or prevent problem resolution altogether, by APM solutions.

There are two easy activities you can conduct to increase the quality of the input data to your APM tool. First, install taps to collect monitoring data. Taps can be installed anywhere across your network. This lets you collect ingress/egress traffic to your network, data to/from remote branch offices, and data from anywhere across the network that you think might be experiencing some sort of issue.

Taps deliver the ultimate experience in flexibility. In contrast, SPAN and mirroring ports off of your Layer 2 and 3 switches do not have that same flexibility. For instance, placing switches all over your network to capture data is unnecessary and expensive. In addition, mirroring ports can drop data, especially in CPU overload situations. When it comes to troubleshooting and performance monitoring, you need every piece of relevant data, not just portions of relevant data.

Secondly, you need to deploy a network packet broker (NPB) in your network. The function of the NPB is to aggregate monitoring data from across your network, filter that data based upon the criteria you are looking for, and remove unnecessary, duplicate copies of the data. Once this is accomplished, the NPB forwards the data onto your APM solution. The NPB may reduce the traffic sent to your APM solution by 50% or more; making your APM solution that much more effective and potentially reduce your future APM tool costs.

Something else to consider is that the tap and NPB concept can be used in cloud solutions as well. This means you can deploy the concept for both physical on-premises and virtual network. This is especially important for hybrid cloud (mixture of physical on-premises and public/private cloud) scenarios that are prevalent in today’s enterprise networks. This mixture of different network types can be a significant problem that is easily remedied with a tap, virtual tap, and NPB approach.

In the end, APM solutions are a critical component to troubleshooting and performance monitoring, but you need to make sure that the APM solution is getting the right data.

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How to Ensure APM Success

Keith Bromley

A recent APMdigest blog by Jean Tunis, The Evolving Needs of Application Performance Monitoring - Part 2, provided an excellent background on Application Performance Monitoring (APM) and what it does. APM solution benefits are much more understood than in years past. An interesting data point from Gartner Inc. mentioned in the article confirms this, stating that IT departments are planning to increase the use of APM solutions to monitor their applications from 5% in 2018 to a projected 20% in 2021.

A further topic that I wanted to touch on though is the need for good quality data. If you are to get the most out of your APM solution possible, you will need to feed it with the best quality data. Irrelevant data, fragmented data, and corrupt data are all common culprits that either end up decreasing the speed to resolution, or prevent problem resolution altogether, by APM solutions.

There are two easy activities you can conduct to increase the quality of the input data to your APM tool. First, install taps to collect monitoring data. Taps can be installed anywhere across your network. This lets you collect ingress/egress traffic to your network, data to/from remote branch offices, and data from anywhere across the network that you think might be experiencing some sort of issue.

Taps deliver the ultimate experience in flexibility. In contrast, SPAN and mirroring ports off of your Layer 2 and 3 switches do not have that same flexibility. For instance, placing switches all over your network to capture data is unnecessary and expensive. In addition, mirroring ports can drop data, especially in CPU overload situations. When it comes to troubleshooting and performance monitoring, you need every piece of relevant data, not just portions of relevant data.

Secondly, you need to deploy a network packet broker (NPB) in your network. The function of the NPB is to aggregate monitoring data from across your network, filter that data based upon the criteria you are looking for, and remove unnecessary, duplicate copies of the data. Once this is accomplished, the NPB forwards the data onto your APM solution. The NPB may reduce the traffic sent to your APM solution by 50% or more; making your APM solution that much more effective and potentially reduce your future APM tool costs.

Something else to consider is that the tap and NPB concept can be used in cloud solutions as well. This means you can deploy the concept for both physical on-premises and virtual network. This is especially important for hybrid cloud (mixture of physical on-premises and public/private cloud) scenarios that are prevalent in today’s enterprise networks. This mixture of different network types can be a significant problem that is easily remedied with a tap, virtual tap, and NPB approach.

In the end, APM solutions are a critical component to troubleshooting and performance monitoring, but you need to make sure that the APM solution is getting the right data.

Hot Topics

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...