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Using Automation to Boost the Effectiveness of Your APM Solution

Chris Bloom

Companies are outsourcing their applications to the cloud in droves, and the trend is sure to continue. In fact, the International Data Corporation (IDC) forecasts worldwide spending on cloud solutions will reach $141 billion by 2019. 

So, let’s say your company has examined all the potential pros and cons, and moved your critical business applications to the cloud. The advertised benefits of the cloud seem like they’ll work out great. And in many ways, life is easier for you now. No infrastructure is necessary. Software updates happen magically on the backend. You get on-demand scalability. The benefits seem endless. And with these things taken care of, you might even find yourself with some free time.

But as often happens when things seem too good to be true, this honeymoon period is short-lived. Reality has a way of kicking in to reveal just exactly how many things can go wrong with your cloud setup – things that can directly impact your business.

One of the most common problems in cloud-based solutions is slow performance, causing users to wait an extended period of time for applications to respond. These problems can be difficult to troubleshoot, because you no longer have direct control over an outsourced application sitting in the cloud. And because the application is in a remote cloud being accessed over the Internet, network performance can have an effect as well. End-users feel the symptoms of network issues but usually don’t see the cause. If a network is congested or sluggish, for whatever reason, critical applications such as VoIP, ERP and CRM solutions can quickly bog down, particularly if they are hosted in the cloud.

If any of this sounds familiar, it’s probably because you’ve encountered something similar. This is when you realize that outsourcing your applications to the cloud does not mean your job is done. On the contrary, deploying applications in the cloud or relying on Software Defined Networks (SDN) or Network Functions Virtualization (NFV) architectures to reduce cost or complexity generally means that you’ll need to closely monitor your applications. When they are slow, your only choice is usually to contact the vendor and work with them to get it fixed. Since your company no longer hosts the application, you do not have direct access to it. Instead, you have to go through that vendor, whose priorities may not always align with your own.

When you contact the vendor about a problem, you will often find that the vendor will try to blame the network first, telling you to talk to your network service provider. And when you contact the network provider, guess what they are going to say? This is not fun, because at this point it is on you to prove whether the performance problem is being caused by the network or the application.

Although you do not have direct access or control over the network or the application, you do have access to all of the network traffic. In order to monitor the performance of your cloud applications, you’ll want an Application Performance Management (APM) solution that can measure latency of both the network and the application. When the latency is high, your tools should also help you understand why. Your complete solution should also have packet capture and analysis capabilities to investigate application performance issues.

Automation and APM

A good APM solution should provide automatic updates on performance issues that help to classify applications in real time, and gather the information you need to better understand what is going on in the network. A web-based dashboard or a mobile app can be a huge bonus, giving you real-time application-aware network analysis and forensics capabilities for Layer 4-7 traffic.

If the solution does not have automation capabilities built-in, it should at least have an API to get the analysis out, and ideally extend the capabilities of the analysis. For example, maybe you run a proprietary protocol, and need to extend the APM solution to recognize it. Or maybe there is a type of custom quality measurement you want the APM solution to calculate. APIs can include C++ or a REST-API, which allow scripts and programs to be written to configure and pull data. These types of APIs are also useful when integrating with other systems; for example, changing the settings of switches and routers based on APM thresholds.

Unfortunately, having to write some code to create these extensions can put them out of reach for many IT organizations, who do not have access to software development resources. But that’s ok, as long as the APM solution can output snapshots of the data at user defined intervals in a format like CSV or JSON. This allows other products to pick it up, and put it in a database. That, in turn, enables all kinds of visualization automation and customization.

Many organizations already have a solution to collect all kinds of log data from different systems, allowing them to create dashboards and alerts that correlate the data, providing insight into how they affect each other, and the business. One of those solutions has a “prediction” feature that will forecast trends into the future. The more data you have from the past, the further it can forecast into the future. Imagine getting an alert that tells you that in a week the utilization on your network will reach maximum capacity, or that latency on your most critical app will reach an unacceptable level. Talk about being proactive.

The most important thing to understand about automation is that it’s not all or nothing. Automation is something that can be added a little bit at a time, making your life a bit easier each time you incorporate more.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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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.

Using Automation to Boost the Effectiveness of Your APM Solution

Chris Bloom

Companies are outsourcing their applications to the cloud in droves, and the trend is sure to continue. In fact, the International Data Corporation (IDC) forecasts worldwide spending on cloud solutions will reach $141 billion by 2019. 

So, let’s say your company has examined all the potential pros and cons, and moved your critical business applications to the cloud. The advertised benefits of the cloud seem like they’ll work out great. And in many ways, life is easier for you now. No infrastructure is necessary. Software updates happen magically on the backend. You get on-demand scalability. The benefits seem endless. And with these things taken care of, you might even find yourself with some free time.

But as often happens when things seem too good to be true, this honeymoon period is short-lived. Reality has a way of kicking in to reveal just exactly how many things can go wrong with your cloud setup – things that can directly impact your business.

One of the most common problems in cloud-based solutions is slow performance, causing users to wait an extended period of time for applications to respond. These problems can be difficult to troubleshoot, because you no longer have direct control over an outsourced application sitting in the cloud. And because the application is in a remote cloud being accessed over the Internet, network performance can have an effect as well. End-users feel the symptoms of network issues but usually don’t see the cause. If a network is congested or sluggish, for whatever reason, critical applications such as VoIP, ERP and CRM solutions can quickly bog down, particularly if they are hosted in the cloud.

If any of this sounds familiar, it’s probably because you’ve encountered something similar. This is when you realize that outsourcing your applications to the cloud does not mean your job is done. On the contrary, deploying applications in the cloud or relying on Software Defined Networks (SDN) or Network Functions Virtualization (NFV) architectures to reduce cost or complexity generally means that you’ll need to closely monitor your applications. When they are slow, your only choice is usually to contact the vendor and work with them to get it fixed. Since your company no longer hosts the application, you do not have direct access to it. Instead, you have to go through that vendor, whose priorities may not always align with your own.

When you contact the vendor about a problem, you will often find that the vendor will try to blame the network first, telling you to talk to your network service provider. And when you contact the network provider, guess what they are going to say? This is not fun, because at this point it is on you to prove whether the performance problem is being caused by the network or the application.

Although you do not have direct access or control over the network or the application, you do have access to all of the network traffic. In order to monitor the performance of your cloud applications, you’ll want an Application Performance Management (APM) solution that can measure latency of both the network and the application. When the latency is high, your tools should also help you understand why. Your complete solution should also have packet capture and analysis capabilities to investigate application performance issues.

Automation and APM

A good APM solution should provide automatic updates on performance issues that help to classify applications in real time, and gather the information you need to better understand what is going on in the network. A web-based dashboard or a mobile app can be a huge bonus, giving you real-time application-aware network analysis and forensics capabilities for Layer 4-7 traffic.

If the solution does not have automation capabilities built-in, it should at least have an API to get the analysis out, and ideally extend the capabilities of the analysis. For example, maybe you run a proprietary protocol, and need to extend the APM solution to recognize it. Or maybe there is a type of custom quality measurement you want the APM solution to calculate. APIs can include C++ or a REST-API, which allow scripts and programs to be written to configure and pull data. These types of APIs are also useful when integrating with other systems; for example, changing the settings of switches and routers based on APM thresholds.

Unfortunately, having to write some code to create these extensions can put them out of reach for many IT organizations, who do not have access to software development resources. But that’s ok, as long as the APM solution can output snapshots of the data at user defined intervals in a format like CSV or JSON. This allows other products to pick it up, and put it in a database. That, in turn, enables all kinds of visualization automation and customization.

Many organizations already have a solution to collect all kinds of log data from different systems, allowing them to create dashboards and alerts that correlate the data, providing insight into how they affect each other, and the business. One of those solutions has a “prediction” feature that will forecast trends into the future. The more data you have from the past, the further it can forecast into the future. Imagine getting an alert that tells you that in a week the utilization on your network will reach maximum capacity, or that latency on your most critical app will reach an unacceptable level. Talk about being proactive.

The most important thing to understand about automation is that it’s not all or nothing. Automation is something that can be added a little bit at a time, making your life a bit easier each time you incorporate more.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.