Born some 15 years ago, Application Performance Management (APM) “strives to detect and diagnose complex application performance problems to maintain an expected level of service.” The cloud was barely born back then. The standard enterprise application was based on a three-tier architecture, with each tier neatly tucked away on its own OS instance and physical or virtual server.

Ascertaining the root causes for application performance problems was a hairy problem to solve; regardless, this simpler application architecture and deployment model made the scope of the situation relatively small and manageable. Because of the smaller scope, it was possible to resolve the problem manually. APM showed where the problems lay, and human ingenuity would resolve them in a problem space that was sufficiently small for the human mind to do its magic.

Innovation Wave I: The Public Cloud

Then came the public cloud. This innovation allowed for optimized consumption of the infrastructure, but at a cost: Developers no longer had neither access to nor control of the underlying infrastructure. Solving application performance problems became trickier.

Innovation Wave II: Cloud-Native Architectures

Then came cloud-native architecture, to take advantage of cloud computing. Ultimately, cloud-native design enables faster application time-to-market. Engineering has much more flexibility, thereby making the business more agile.

However, this innovation has another cost. The neatly-packaged three-tier architecture is now a multi-service spaghetti bowl.

APM Struggles to Keep Up

Cloud-native monitoring, traceability, and observability are the subsequent phases of the APM evolution. These efforts and related technologies all intend to push the sleepy approach to APM out of the traditional enterprise software mindset.

Complexity is the underlying reason for this innovation wave. Let’s assume that we have successfully identified the application performance problem source in our infrastructure and API soup. Where does the SRE go next to solve the problem? There are too many interdependencies for the old-fashioned human brain to work. These new classes of tools generate a sea of data, drowning the human mind into indecision.

Innovation Wave III: Enter Opsani

Machine learning (ML) techniques are particularly good at solving multi-dimensional problems that are too complex for the human brain. Opsani leverages ML and a small set of experiments to determine how these data are correlated with each other. Therefore, configuration adjustments are needed for the application to meet its SLOs.

Opsani reconfigures applications autonomously. By tapping into the APM data stream, Opsani recommends the correct corrective set of actions. Moreover, Opsani autonomously reconfigures applications in light of changing conditions, such as changes in the codebase, underlay infrastructure, or load profile to ensure that the application remains within its performance requirements.

This type of autonomous operations frees up developers and SREs from investigative troubleshooting and guesswork, while optimizing the application to do what it must to delight customers.

It is time to elevate the APM to the next level. Opsani makes it possible to drive continuous, autonomous cloud-native workload optimization. Go beyond just ascertaining where the problems are. Go the last mile and resolve the issue proactively and autonomously.