Current trends in APM (Application Performance Monitoring)

What do you get when you combine application monitoring with artificial intelligence? Dynatrace. You can find outages and software bugs instantly, evaluate millions of data in seconds, and do it automatically. No coding required.

A few years ago, most application jobs were primarily monolithic and deployed on-premise. It was quite easy to provide application monitoring by collecting selected metrics. And log entries because the scope was manageable and all infrastructure belonged to the company or organization. Most applications were more or less independent of each other and their only common element could be the data repository.

This type of application (and organizational) independence allowed IT teams to choose any solution for application monitoring. At that time, a single, comprehensive solution that covered the entire company or organization represented only a minimal advantage.

Today, however…

The application environment is now evolving in the opposite direction. Application/IT teams create services that are used by other teams in the organization. Many tasks have moved from the data center to the cloud, where they use various cloud services. In addition, the sheer number of core applications has increased exponentially.

This has created a new requirement – to completely (end-to-end) monitor performance across application boundaries. Down to components that the business or organization does not own (e.g., cloud services and third-party applications/services).

Observability = a new trend in the multicloud environment

Observability is the new standard in application monitoring for native cloud architectures. It builds on a vast amount of collected telemetry data such as metrics, logs, events, and distributed transaction logs that measure the state or “health” of application performance and behavior.

But collecting the data is not the most important thing. The key is to process it afterwards using artificial intelligence and focus on significant deviations from normal operation. This allows algorithms to quickly determine the root cause of an incident. I.e. what is causing the application incident and what specific behaviors or events are leading up to it.

This helps developers understand not only what’s wrong with the system – what’s slow or broken. But also why the problem occurred, where it originated, and what the impact was.

Benefits of the new standard

With Observability, IT departments, developers and DevOps teams not only gain visibility into applications. But also additional information regarding the infrastructure, platforms and customer experience that support and depend on those applications. With this standard, they can:

  • find outages, software bugs, unauthorized activity and degradation of service levels
  • report on “application health”, i.e. the state of the system by measuring performance and resources
  • understand how neighbouring or dependent services may interact
  • find completely new unknown conditions/incidents that have never occurred in the past
  • identify long-term trends for capacity planning against business objectives and KPIs

Key capabilities of APM solutions that take advantage of the new observability capabilities include:

  1. AI-powered core – automatically evaluates millions to billions of data points in seconds. With its help, IT teams can cope with situations where they receive hundreds or thousands of alerts about potential incidents at once. Without AI, it’s nearly impossible for teams to determine on their own which alerts are relevant, which ones matter most.
  2. Automated process – without the need for manual configuration or coding, it tracks all components in the environment in real time from top to bottom, including interrelationships and dependencies. With traditional monitoring tools, metrics, logs, traces, and user experience data were stored in data silos with no context to connect and make sense of them. An automated, intelligent, observability-enabled APM solution identifies the impact on users at the time of an incident so developers can focus on the most pressing issues and fix them quickly.
  3. Distributed tracing across open-source and native cloud architectures – comprehensively analyzes transactions to eliminate gaps, blind spots and incomplete transaction trace records.

Prevent application performance problems – automated, real-time, with AI

IT teams running multi-cloud ecosystems need to monitor the entire process and its individual steps and components continuously, automatically and with the help of artificial intelligence. This is the only way they can find, fix and prevent application performance issues in real time.

The Dynatrace platform and its Davis® AI engine automate root cause analysis and discover new unknowns in even the most complex cloud architectures. This makes it one of the top performers in APM. As evidenced by the fact that Gartner named Dynatrace an APM leader for the tenth consecutive year and that Dynatrace received 5 of the 6 highest scores in Gartner’s Critical Capabilities for Application Performance Monitoring (APM) report.