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The AppD Approach: Monitoring Cloud at Scale

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Summary
Our next-generation controller architecture and AI-driven predictive analytics can scale up and out to meet your enterprise's needs.

To keep pace with dynamic scaling needs in the enterprise, AppDynamics has released its next-generation controller architecture. This new release allows users to rapidly on-board distributed applications into a single, scalable monitoring platform. Most importantly, these applications can constitute a large number of nodes, containers, serverless functions and other service components.

Cloud Adoption Doesn’t Happen Overnight

Enterprises are embracing the cloud as a cost-effective, reliable and flexible solution for running business-critical applications. But unless your business was born in the cloud, it’s unlikely that your entire application portfolio will rely on cloud resources alone. Instead, cloud adoption is more often a gradual process in which an enterprise moves some applications to the cloud, but continues to run others—or certain application components—on-premises for some time. As your enterprise adopts a cloud-native application stack, release velocity increases, as does the number of application components. This becomes more relevant as you adopt microservices and serverless components.

Monitoring at Cloud Scale

One difference between on-premises enterprise applications and cloud applications is scale— specifically, enterprise scale vs. cloud scale. Enterprise scale is bounded, predictable and measurable. However, scale experienced by cloud applications is not.

The microservices and serverless paradigm makes applications dynamic and widely distributable. And a big reason for this is the density and volatility of containerized environments.

Since operating systems and kernels don’t need to be loaded for each container, containerized environments enable greater workload density than more traditional virtualized environments. As a result, the total volume of components that need to be created, monitored and destroyed across the production environment is exponentially larger. This significantly increases the complexity of monitoring container-based environments. In addition to having more things to manage, these environments are changing faster than ever before.

Another significant change: new cloud-deployment patterns with higher churn rates, including blue-green deployments and canary releases. These patterns often involve shutting down an entire stack of applications and bringing up a parallel stack. This creates more velocity and demands the monitoring platform to be capable of handling such variance. The dynamic cloud makes it faster and easier to deploy applications that can scale as required.

As your application environment scales up and out, the volume, variety and velocity of data multiplies exponentially. With a traditional monitoring approach, you’d drown in this cloud-scale sea of data before uncovering any meaningful insights. The bottom line is that you need better ways to aggregate, process and deliver metrics.

Monitoring the dynamic cloud can have a very different set of challenges than monitoring traditional static applications. You’ll need to know not only how a resource ran and was deployed, but also the data on when it ran. In addition, it’s critical to know which resources were being used when a problem occurred. A monitoring approach that may have worked well in the past was never designed to handle dynamic cloud infrastructure deployments, microservices, serverless and Internet of Things (IoT) technologies.

Most application performance management (APM) solutions can’t scale because they’re still built on top of an older architectural paradigm. But today’s application landscape requires a new way to monitor. AppDynamics has developed a highly elastic cloud-native controller architecture and artificial intelligence to scale up and out to tens of thousands of applications in even the most diverse and complex environments.

 

Tens of Thousands of Agents Under a Single Controller

AppDynamics’ next-generation controller architecture can scale up and out to meet your enterprise’s needs. It allows you to rapidly on-board distributed applications into a single, scalable monitoring platform, and brings greater efficiency to deployment choices such as blue-green or canary releases. With improved scalability, the AppDynamics controller can easily and continuously support new deployments.

Since AppDynamics helps your enterprise scale to manage tens of thousands of applications and high-churn deployments, it enables you to manage millions of metrics and data points. In fact, AppD has an aggressive goal to support the ingestion and processing of multiple millions of metrics. To equip you with a robust alert system, we have optimized our alerting engine to support the evaluation of tens of thousands of alerts per minute.

Once you’ve successfully addressed scaling issues, there’s still the daunting challenge of monitoring the ever-expanding cloud applications landscape. Rather than making you monitor everything manually, AppDynamics automates all the discovery, modelling and analysis, enabling you to deliver exceptional application experiences at the speed of modern business.

That’s where artificial intelligence comes in. AI can absorb huge amounts of information, automatically connect the dots between billions of metrics and millions of dependencies, and perform in milliseconds the kind of analysis that takes hours or days via traditional manual monitoring approaches.
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