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  Correlate performance metrics with business outcomes.
  
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  Automate and continuously adapt application security.
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OpenShift Monitoring

Cloud

The AppDynamics OpenShift JBoss Cartridge Extension provides the ability to monitor and manage your critical cloud-based applications, ensure application performance pre and post-cloud migration, and take full advantage of cloud elasticity with auto-scaling.

OpenShift is a PaaS (Platform-as-a-Service) offering. Written in the Ruby programming language, OpenShift runs on top of Red Hat Enterprise Linux (RHEL), with Red Hat building out and maintaining the underlying infrastructure. It supports most scripting languages such as Java, JavaScript, Ruby, Python, PHP, Perl, .NET, and Haskell, as well as database frameworks like MySQL, PostgreSQL, SQL Server, and MongoDB. It also works with a wide range of web application frameworks, including Rack for Ruby; WSGI for Python; Node.js; PSGI for Perl (work unmodified); Laravel; Code Igniter; CakePHP; Ruby on Rails; Django; Perl Dancer; Flask; Sinatra; Tornado; web2py; and Vert.x, among others. OpenShift underpins these frameworks by supporting their preferred web-integration APIs, so that it doesn’t need to modify the actual framework code.

Automatic application discovery

Automatically discover the entire topology of your OpenShift application and enable your team to spend time on innovation instead of configuration.

Display key performance indicators

Gain a real-time view of the complete health of your OpenShift application by monitoring load, average, response time and error rate for the application, and dynamic baselines for all of these values to show what's normal vs abnormal.

Distributed call graphs

Follow a user request as it traverses your distributed OpenShift application and collect end-to-end code execution and latency data to provide context required to isolate root cause quickly.

Real-time business metrics

Correlate application performance metrics to the metrics that matter most to your business and get real-time insight when performance deviates from the baseline.

Cloud auto-scaling

Ensure accurate capacity planning for cloud environments with tools to dynamically scale up or scale down cloud resources based on performance metrics.