Redefining Application Performance Monitoring: Trends to Watch For in 2020

Ranjani
Site24x7
Learn more about ManageEngine

From monolithic architecture to distributed systems and microservices, the art of application development has changed over the years, and the needs for monitoring those applications have evolved with it. Application performance monitoring (APM) solutions now meet the monitoring needs of all applications deployed across various platforms, be it on-premise, cloud, or even hybrid models.

Organizations are embracing APM solutions and the APM industry is realizing significant growth as a result. According to a report by Mordor Intelligence, the APM market was valued at $5.8 billion in 2019 and is expected to grow to $11.43 billion by 2025 at a CAGR of 12 percent.


Visualization, customization, and artificial intelligence (AI) shape the current APM market, but what can we expect to see in the future?

What are the evolving trends in automation and monitoring tools that DevOps teams look out for?

How can DevOps teams utilize APM solutions and get the most out of them? Read on to learn more.

1. Application Monitoring to Digital Experience Monitoring

APM used to mean monitoring standard application performance metrics and errors. But in recent years, this definition has evolved. The latest one, according to Gartner, is: "APM is a suite of monitoring software comprising digital experience monitoring, application discovery, tracing and diagnostics, and purpose-built AI for IT operations."

The emphasis on digital experience plus AI is one of the most easily observable industry trends. The de facto standard for DevOps extends beyond ensuring uptime and availability, and now includes actively understanding user experience and proactively enhancing performance.

To thrive in a diverse user market, developers need to create apps that deliver a uniform user experience across different environments. This requires gathering details about how external factors like user geography, device, OS, browsers, and network impact the performance of the application, in addition to the traditional performance monitoring factors behind app servers.

Integrating APM with tools like synthetic monitoring and real user monitoring, which provide insight on user experience, helps developers build global and scalable applications.

2. Value from AIOps

DevOps plus AI = AIOps

AI is no longer a fancy word for superhuman intelligence, but a staple in the tech world. AI can no longer be ignored by DevOps. In the current state, monitoring tools use AI to identify and alert IT admins about anomalies, letting DevOps take over from there. However, as AI grows more pervasive, valuable insights into app behavior allow for deep data mining into business trends that are otherwise hard to identify in a complex architecture.

AI-based alerting systems save valuable time by preventing false and unnecessary alerts that can arise during an update. Having AI integrated with an APM tool helps businesses take proactive rather than reactive actions. For instance, letting AI autoscale its instances depending on the load, frees admins from constantly monitoring the load. Plus, AI is great at detecting anomalous or suspicious behaviors, understanding and reporting performance bottlenecks over time, and detecting when it's ideal to reboot servers.

3. Logs and APM

The industry has accepted that metrics, traces, and logs form the three pillars of observability. APM tools cover standardized metrics like Apdex, stacktrace, errors, and exceptions that aid DevOps teams to figure out where something went wrong. But for application developers, who need to resolve performance issues without compromising functionality, logs are required to provide crucial and context-sensitive information in their own language.

Currently, APM tools provide integrations that associate logs with stacktrace to provide contextual information on logged exceptions. But the future demands APM tools that can integrate their own and third-party log monitoring solutions to give complete observability into the application behavior.

4. One Agent to Rule Them All

Earlier we discussed that digital experience monitoring is no longer about one tool or a simple set of metrics. It requires coordinating and collaborating results from a varied set of tools that monitor numerous aspects of your application performance. This, in turn, requires a single agent from APM vendors to monitor various technical and functional aspects of your application built upon various platforms and to provide a unified view of your services. Automatic detection and instrumentation is an added bonus.

5. From Solution to Skill Set

DevOps has evolved from a set of practices to organizational culture, and what is culture without shared beliefs and practices? We at Site24x7 believe that APM is already an intrinsic part of the DevOps culture. Continuous integration and continuous delivery is made possible only with continuous monitoring.

Like DevOps, in the future an APM specialist will be required to identify the pain points in application performance, benchmark the performance KPIs, and tailor the monitoring needs according to the demands of the application. Performance is not an afterthought, and to implement shift-left in the DevOps cycle, an APM specialist will play a crucial role.

We believe the APM market will continue to evolve, take on a greater role in IT, and provide more solutions for organizations.

This blog was originally posted on www.site24x7.com

Ranjani is a Product Analyst at Site24x7

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Redefining Application Performance Monitoring: Trends to Watch For in 2020

Ranjani
Site24x7
Learn more about ManageEngine

From monolithic architecture to distributed systems and microservices, the art of application development has changed over the years, and the needs for monitoring those applications have evolved with it. Application performance monitoring (APM) solutions now meet the monitoring needs of all applications deployed across various platforms, be it on-premise, cloud, or even hybrid models.

Organizations are embracing APM solutions and the APM industry is realizing significant growth as a result. According to a report by Mordor Intelligence, the APM market was valued at $5.8 billion in 2019 and is expected to grow to $11.43 billion by 2025 at a CAGR of 12 percent.


Visualization, customization, and artificial intelligence (AI) shape the current APM market, but what can we expect to see in the future?

What are the evolving trends in automation and monitoring tools that DevOps teams look out for?

How can DevOps teams utilize APM solutions and get the most out of them? Read on to learn more.

1. Application Monitoring to Digital Experience Monitoring

APM used to mean monitoring standard application performance metrics and errors. But in recent years, this definition has evolved. The latest one, according to Gartner, is: "APM is a suite of monitoring software comprising digital experience monitoring, application discovery, tracing and diagnostics, and purpose-built AI for IT operations."

The emphasis on digital experience plus AI is one of the most easily observable industry trends. The de facto standard for DevOps extends beyond ensuring uptime and availability, and now includes actively understanding user experience and proactively enhancing performance.

To thrive in a diverse user market, developers need to create apps that deliver a uniform user experience across different environments. This requires gathering details about how external factors like user geography, device, OS, browsers, and network impact the performance of the application, in addition to the traditional performance monitoring factors behind app servers.

Integrating APM with tools like synthetic monitoring and real user monitoring, which provide insight on user experience, helps developers build global and scalable applications.

2. Value from AIOps

DevOps plus AI = AIOps

AI is no longer a fancy word for superhuman intelligence, but a staple in the tech world. AI can no longer be ignored by DevOps. In the current state, monitoring tools use AI to identify and alert IT admins about anomalies, letting DevOps take over from there. However, as AI grows more pervasive, valuable insights into app behavior allow for deep data mining into business trends that are otherwise hard to identify in a complex architecture.

AI-based alerting systems save valuable time by preventing false and unnecessary alerts that can arise during an update. Having AI integrated with an APM tool helps businesses take proactive rather than reactive actions. For instance, letting AI autoscale its instances depending on the load, frees admins from constantly monitoring the load. Plus, AI is great at detecting anomalous or suspicious behaviors, understanding and reporting performance bottlenecks over time, and detecting when it's ideal to reboot servers.

3. Logs and APM

The industry has accepted that metrics, traces, and logs form the three pillars of observability. APM tools cover standardized metrics like Apdex, stacktrace, errors, and exceptions that aid DevOps teams to figure out where something went wrong. But for application developers, who need to resolve performance issues without compromising functionality, logs are required to provide crucial and context-sensitive information in their own language.

Currently, APM tools provide integrations that associate logs with stacktrace to provide contextual information on logged exceptions. But the future demands APM tools that can integrate their own and third-party log monitoring solutions to give complete observability into the application behavior.

4. One Agent to Rule Them All

Earlier we discussed that digital experience monitoring is no longer about one tool or a simple set of metrics. It requires coordinating and collaborating results from a varied set of tools that monitor numerous aspects of your application performance. This, in turn, requires a single agent from APM vendors to monitor various technical and functional aspects of your application built upon various platforms and to provide a unified view of your services. Automatic detection and instrumentation is an added bonus.

5. From Solution to Skill Set

DevOps has evolved from a set of practices to organizational culture, and what is culture without shared beliefs and practices? We at Site24x7 believe that APM is already an intrinsic part of the DevOps culture. Continuous integration and continuous delivery is made possible only with continuous monitoring.

Like DevOps, in the future an APM specialist will be required to identify the pain points in application performance, benchmark the performance KPIs, and tailor the monitoring needs according to the demands of the application. Performance is not an afterthought, and to implement shift-left in the DevOps cycle, an APM specialist will play a crucial role.

We believe the APM market will continue to evolve, take on a greater role in IT, and provide more solutions for organizations.

This blog was originally posted on www.site24x7.com

Ranjani is a Product Analyst at Site24x7

Related Links

Hot Topics

Related Vendors

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The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

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Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

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Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...