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Using Application Monitoring Data By @Dynatrace | @DevOpsSummit [#DevOps]

Using performance models, potential future scenarios can be evaluated in advance without setting up expensive test environments

How to Create Performance Models Using Application Monitoring Data

Dynatrace collects a wealth of monitoring data on applications and one of the great aspects is that it also provides interfaces allowing external applications to use this information. An example we've just recently seen in a blog post showed how you can use Dynatrace data to monitor your entire application landscape across a server farm. However, potential usage scenarios are not limited to simplify the monitoring of existing applications, and one of our technology partners, the Performance Management Group (PMG) of fortiss GmbH, has developed a solution that uses Dynatrace data to build performance models.

Using performance models, potential future scenarios can be evaluated in advance without setting up expensive test environments or evaluation projects. These models allow for evaluating the performance of an application in scenarios that cannot be measured on existing systems. Exemplary questions that can be answered by performance models are for example:

What happens if...

  • ... we add new servers/CPUs to our data center?
  • ... we combine multiple VMs on one HW machine?
  • ... we add new enterprise applications to our data center?
  • ... additional users access an enterprise application?
  • ... the user behavior changes?
  • ... we migrate our on-premise deployment to a cloud deployment?

Consider, for example, the last question: "What happens if we migrate our on-premise deployment to a cloud deployment?" In order to evaluate this scenario and to estimate the expected performance, all system topology changes need to be considered. Performance models can help simplify this evaluation. Instead of migrating the full application in a first step and measuring the performance afterwards, one can simply use a model derived from measurements on the existing deployment that describes the performance-relevant aspects of the application. This model is adapted to the new system topology on the cloud and simulation results based on this adapted model give immediate feedback on the expected performance on the new platform.

You might wonder what a performance model looks like. A performance model depicts three layers that influence the performance of an overall system, namely the workload (user count and behavior), the performance-relevant aspects of a software architecture (e.g., component relationships, transaction control flows and resource demands) and the hardware environment (e.g., available servers and the deployment topology). It is possible to represent these layers in different ways. While traditional performance models depict these aspects in one monolithic model, modern architecture-level performance models allow to represent these aspects independently from each other. One example of such an architecture-level performance model is the Palladio Component Model (PCM). PCM is a performance modeling language that comes with a comprehensive tooling support, including graphical editors, simulation engines and result analyzers. PCM depicts the workload, software architecture and hardware environment in an UML-like notation that is annotated with performance-relevant meta-data (e.g., resource demands, probabilities,). For more details on the modeling notation and the associated tooling please visit the PCM website.

Unfortunately, constructing performance models of an enterprise application is associated with a lot of effort nowadays. In order to simplify their use and make them better applicable in your environment, the PMG developed a performance model generator that uses monitoring data to generate PCM-based performance models [1,2]. These models can be used to evaluate the application's performance in different scenarios, like changes in workload or the hardware environment. The focus of our cooperation is to connect Dynatrace APM data with the performance model generator of the PMG as shown in Figure 1.

Figure 1: Performance Model Generation Framework [3]

Dynatrace is used to instrument a Java EE application and to collect monitoring data. The Performance Model Generator Framework extracts and aggregates the monitoring data (i.e., Pure Paths) via the Dynatrace REST interfaces. The information is transformed into a performance model that represents the instrumented Java EE application and contains the application components, the transaction control flow and their resource demands.

Click here for the full article.

More Stories By Andreas Brunnert

Andreas Brunnert is co-founder of the SPEC RG DevOps performance working group and leads the performance management group at the Fortiss Institute in Munich. Prior to his current role, he was Software Engineer and IT Consultant at IBM. His work focuses on performance evaluations of enterprise applications using performance modeling techniques (e.g., capacity planning, management or performance predictions in continuous delivery processes

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