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The Case for Application Experience Monitoring

Why ‘"app assurance" is just as (or more) important than APM
Andrew Marshall

For today's software development teams, application performance monitoring (APM) is a fairly ubiquitous technology and an effective tool to monitor how applications are performing in production. The functionality of APM has evolved since it arrived on the scene in the late 90s, with several vendors building monitoring functionality that works well with distributed (i.e. not monolithic) applications. Despite these advances, APM remains at its core a mechanism for Dev teams to track how an application is working at the code and transaction level.

All the customer cares about is how they are enjoying an app.

While this is still useful, it doesn't address the ultimate goal of DevOps teams: to deliver the desired application experience to end users. Code working perfectly doesn't matter much if apps aren't reaching customers, or are negatively impacted by network latency or outages. All the customer cares about is how they are enjoying an app. To effectively guarantee application availability and usage satisfaction, DevOps teams need to leverage three important application assurance data sets into their delivery automation logic:

■ Application user experience: Real User Monitoring (RUM)

■ Real-time infrastructure health status: Synthetic testing

■ IT tool data feeds: Key IT health data like (traditional) APM, local load balancer (LOB) data and cloud metrics

Application User Experience: Real User Monitoring (RUM)

When is an app truly "green"? Answer: when it's working correctly for end users. Real user monitoring (RUM) allows Ops teams to fully understanding how internet performance impacts customer satisfaction and engagement. No matter where an app is hosted — in clouds, data centers, or CDNs — Ops teams need to make sure delivery of these apps looks good from the user perspective. RUM gives teams a real-time understanding of worldwide network health, which in turn delivers the performance data needed to automate app delivery, and ensure the best user experience your application can offer. An end user-centric approach to application assurance is critical to Application Experience Monitoring.

Real-Time Infrastructure Health Status: Synthetic Testing

Modern infrastructure is dynamic, distributed, and heterogeneous in nature. When your delivery architecture is comprised of one or more clouds, data centers, or CDNs, understanding the status of your infrastructure becomes a difficult proposition. It's critical that you test all of your endpoints: in your public clouds, private clouds, data centers, or CDNs. This provides a comprehensive and uniform view of the overall health of your applications delivery, no matter what the status of your various infrastructure components happens to be.

Synthetic testing acts like a virtual end point, testing the throughput of an application, video, or large file download. Being able to test your app from remote locations worldwide helps ensure your data has incredibly low latency, and therefore is actually usable for your app delivery strategy. Healthy infrastructure makes for deliverable apps.

IT Tool Data Feeds

As mentioned, a basic understanding of how an app is performing at the code and transaction level (i.e. traditional APM) is still important. This monitoring data is a key part of the third aspect of application assurance that DevOps teams need to leverage in addition to RUM and infrastructure health: IT Tool Data Feeds. There are various other monitoring and real-time metrics available to IT Ops to help them automate app delivery with the most robust set of data. (Traditional) APM is certainly one of these. Understanding the health of the app code is obviously still useful for making real-time delivery decisions in your software-defined app delivery platform.

On top of that there are many other data sources to leverage, of course, such as: local load balancer (i.e. NGINX, HAProxy) health metrics, cloud status metrics (i.e. AWS Cloudwatch), etc. These are just a few examples. Chances are your business collects data from LOB apps or other mission-critical services that are instrumental to your IT organization. These are tools you're paying (or paid) for, so you should use them for your application delivery automation if they're accessible. They're just as important as traditional APM.

DevOps Requires Insight + Action

DevOps teams are under constant pressure to support continuous deployment, agile methodology, and an acceptable uptime for applications. "Monitoring" isn't a solution, but actually just a way to collect data. Ops teams then use this data to make sure apps are delivered to customers with an optimal experience in mind. When both dev and ops teams have a single lens to view IT health data (from the three sources above) and a set of application delivery rules, they can react quickly to changes in these data feeds to assure the one thing that matters: the application experience by end users. Application Experience Monitoring as a practice helps make this possible.

Once DevOps teams understand how the Application Experience impacts global customers, the next important step is to do something with that information. That's where a software-defined application delivery platform comes in. Leveraging this powerful data set to automate application, video, and website delivery allows Ops teams to "self-heal" when network outages or latency issues happen. Insight plus action is the next step for APM.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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The Case for Application Experience Monitoring

Why ‘"app assurance" is just as (or more) important than APM
Andrew Marshall

For today's software development teams, application performance monitoring (APM) is a fairly ubiquitous technology and an effective tool to monitor how applications are performing in production. The functionality of APM has evolved since it arrived on the scene in the late 90s, with several vendors building monitoring functionality that works well with distributed (i.e. not monolithic) applications. Despite these advances, APM remains at its core a mechanism for Dev teams to track how an application is working at the code and transaction level.

All the customer cares about is how they are enjoying an app.

While this is still useful, it doesn't address the ultimate goal of DevOps teams: to deliver the desired application experience to end users. Code working perfectly doesn't matter much if apps aren't reaching customers, or are negatively impacted by network latency or outages. All the customer cares about is how they are enjoying an app. To effectively guarantee application availability and usage satisfaction, DevOps teams need to leverage three important application assurance data sets into their delivery automation logic:

■ Application user experience: Real User Monitoring (RUM)

■ Real-time infrastructure health status: Synthetic testing

■ IT tool data feeds: Key IT health data like (traditional) APM, local load balancer (LOB) data and cloud metrics

Application User Experience: Real User Monitoring (RUM)

When is an app truly "green"? Answer: when it's working correctly for end users. Real user monitoring (RUM) allows Ops teams to fully understanding how internet performance impacts customer satisfaction and engagement. No matter where an app is hosted — in clouds, data centers, or CDNs — Ops teams need to make sure delivery of these apps looks good from the user perspective. RUM gives teams a real-time understanding of worldwide network health, which in turn delivers the performance data needed to automate app delivery, and ensure the best user experience your application can offer. An end user-centric approach to application assurance is critical to Application Experience Monitoring.

Real-Time Infrastructure Health Status: Synthetic Testing

Modern infrastructure is dynamic, distributed, and heterogeneous in nature. When your delivery architecture is comprised of one or more clouds, data centers, or CDNs, understanding the status of your infrastructure becomes a difficult proposition. It's critical that you test all of your endpoints: in your public clouds, private clouds, data centers, or CDNs. This provides a comprehensive and uniform view of the overall health of your applications delivery, no matter what the status of your various infrastructure components happens to be.

Synthetic testing acts like a virtual end point, testing the throughput of an application, video, or large file download. Being able to test your app from remote locations worldwide helps ensure your data has incredibly low latency, and therefore is actually usable for your app delivery strategy. Healthy infrastructure makes for deliverable apps.

IT Tool Data Feeds

As mentioned, a basic understanding of how an app is performing at the code and transaction level (i.e. traditional APM) is still important. This monitoring data is a key part of the third aspect of application assurance that DevOps teams need to leverage in addition to RUM and infrastructure health: IT Tool Data Feeds. There are various other monitoring and real-time metrics available to IT Ops to help them automate app delivery with the most robust set of data. (Traditional) APM is certainly one of these. Understanding the health of the app code is obviously still useful for making real-time delivery decisions in your software-defined app delivery platform.

On top of that there are many other data sources to leverage, of course, such as: local load balancer (i.e. NGINX, HAProxy) health metrics, cloud status metrics (i.e. AWS Cloudwatch), etc. These are just a few examples. Chances are your business collects data from LOB apps or other mission-critical services that are instrumental to your IT organization. These are tools you're paying (or paid) for, so you should use them for your application delivery automation if they're accessible. They're just as important as traditional APM.

DevOps Requires Insight + Action

DevOps teams are under constant pressure to support continuous deployment, agile methodology, and an acceptable uptime for applications. "Monitoring" isn't a solution, but actually just a way to collect data. Ops teams then use this data to make sure apps are delivered to customers with an optimal experience in mind. When both dev and ops teams have a single lens to view IT health data (from the three sources above) and a set of application delivery rules, they can react quickly to changes in these data feeds to assure the one thing that matters: the application experience by end users. Application Experience Monitoring as a practice helps make this possible.

Once DevOps teams understand how the Application Experience impacts global customers, the next important step is to do something with that information. That's where a software-defined application delivery platform comes in. Leveraging this powerful data set to automate application, video, and website delivery allows Ops teams to "self-heal" when network outages or latency issues happen. Insight plus action is the next step for APM.

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...