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Leverage AI to Improve Application Performance

application-containers

It’s all about the apps. Nearly all organizations today rely upon a variety of applications to conduct their essential operations. It is estimated that the typical enterprise organization uses more than 500 separate applications, while some use as many as 10,000. Naturally, keeping those apps running and performing well has never been more important. Unfortunately, that job has only gotten harder as IT departments struggle to manage disconnected components and data sources.

Unlike yesterday’s standardized, all-inclusive, packaged applications, today’s apps are an intricate mesh of components. In addition, there is a good chance they are running on virtualized hardware that is shared with other applications. Although an end-user looking at a browser window sees a single app, there are actually multiple moving parts that must execute correctly to deliver a reliable user experience. That experience suffers if there’s a glitch at the web server, app server, database or anywhere else along the line.

With apps, data and users literally everywhere, it has become much tougher to pinpoint performance issues within the application stack. Ideally, organizations should have end-to-end visibility across every application transaction. Yet this has become very difficult because organizations are building hybrid ecosystems of onsite systems and platforms from a variety of cloud providers.

The growing use of microservices and app containers also impact visibility. These technologies involve breaking up monolithic applications into small, independent services that can be rapidly developed and deployed at web scale. However, this requires many more virtual application server instances to run the smaller pieces, which fragments the application transaction path even further.

A recent study from Dynatrace finds that a single application transaction now crosses an average of 35 different technology systems or components. Of the 800 CIO surveyed, 76 percent say they think this growing complexity could soon make it impossible to manage performance efficiently.

Delivering predictable and reliable application performance has become a business imperative. However, traditional application monitoring tools that involved humans watching dashboards, analyzing data sets and responding to alerts doesn’t work anymore. The volume, velocity and variety of information are simply overwhelming.

Artificial intelligence (AI) technologies offer a better solution. AI-powered application monitoring tools can detect issues in real time, pinpoint the source of the problems and automatically launch the proper remediation processes — often before end-users even recognize that there’s been any kind of problem.

Dynatrace was among the first application performance monitoring (APM) vendors to apply AI algorithms, analytics and automation to its solution portfolio. It provides extreme visibility into app performance through Dynatrace OneAgent, which auto-discovers and auto-instruments the full stack — including within containers — with a single installation per host.

Dynatrace builds and maintains a real-time topological model that can track billions of nodes. Dynatrace can map individual end-user actions to specific services, instances, processes, containers, disks, hosts, the network, data centers and log files. Dynatrace also maps all interdependencies between components.

Davis, an AI-powered virtual assistant, provides voice and chat-driven interaction for the platform. By answering natural language questions and simplifying dialogs, Davis makes performance data accessible to everyone in the organization, not just IT experts.

Gartner ranks Dynatrace as a leader in its APM Magic Quadrant, and the tool is being used to monitor tens of thousands of applications for more than 5,000 customers, large and small, around the globe. Give us a call to learn more about leveraging this AI tool to improve your application performance.

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