The Six Pillars of Applying AI to Managing Microservice Applications

October 16, 2017

Two recent eBooks titled Service Quality Management in the Microservices Age and Application Performance Management in Containerized World discuss the challenges and monitoring functionality needed to “thrive in the brave new world of containerized microservices”.While these concepts are a good start, they inspired us to think about what functionality must be in application monitoring tools to enable them to help Ops and DevOps teams manage performance of modern dynamic applications (microservices, containerized, cloud, etc.). The result are these pillars of application management – six fundamental functional needs required in your monitoring tool so that it can help you effectively manage today’s modern dynamic applications.

Why Are Modern Applications More Difficult to Monitor and Manage?

Just like the number of components in a modern app, the reasons making these applications difficult to monitor are varied and numerous. Let’s start with the idea of loosely coupled application components. Some of these issues started to emerge as SOA became a primary application architecture, but not even close to the extent of today’s applications. What started as a few calls in the middle of a SOA Application has ballooned into an all out blitz of constant change.

In fact, the only constant within these large complex application systems is change. Whether change is due to microservice architecture,
container orchestration, or simply more agile delivery execution, to effectively manage microservice applications, DevOps needs up-to-date visibility, a precise picture of the application’s structure and an immediate understanding of its health.

From automation to service level management, everything has to change for DevOps to effectively manage the modern application.  In fact, six distinct things must be considered.

So here are the Six Pillars of Modern Dynamic Application Management

pillars

  1. Automatic, Continuous Discovery & Mapping
    How important is accuracy in mapping and discovery? Think about the old-school programming term “Garbage In, Garbage Out.” If you’re going to apply AI (or even simple automation), it’s imperative to have completely accurate data. Otherwise, you’re simply executing advanced analysis on data that doesn’t matter. Instana delivers continuous, always accurate, application maps so that all analysis can be performed on precise data.
  2. Precise High Fidelity Visibility
    To empower AI, precision and accuracy of data are a requirement. After automatically discovering the components and structure of the application, Instana collects the industry’s most accurate monitoring data: metric data is streamed at 1 second granularity, and every request through the application is captured in a Trace. This data is the source for our AI training and the base for giving the user deep visibility into microservice applications.
  3. Full Stack Application Data Model
    The core technology powering Instana is the internal data model, the Dynamic Graph. The Graph is a model of your discovered application: all physical and logical components, their technology components, dependencies and configuration. The Graph, which updates with any changes in real time, maintains the dependency models needed to enable the AI powered precise troubleshooting, prediction and problem resolution capabilities of Instana. Without a strong model of what an application is, there can be no intelligent analysis of performance, root cause or predictions.
  4. Cloud, Container & Microservice Native
    Modern applications continuously change, are deployed across hybrid clouds, leverage containers and orchestration (like Kubernetes and Mesosphere), all in the name of high speed delivery. Meanwhile, the microservice architecture spawns wide diversity – each engineer can choose the programing language, middleware and databases best suited to code their microservice. Scale, complexity and constant change are the new realities. Traditional Monitoring and APM tools were not designed for these diverse and dynamic use cases. Instana is designed to operate in the modern world. With zero configuration, Instana naturally aligns with the infrastructure, clouds, containers, orchestrators, middleware and languages to automatically keep up with, and visualize, your microservice application.
  5. Real-time AI-Drive Incident Monitoring & Prediction
    Within these complex environments, changes, warnings and errors happen all the time, but many don’t require human intervention. DevOps needs accurate and actionable information within seconds after change occurs, like code deployments, but also in advance of disasters when they are imminent. This is where applying realtime AI to the problem has a powerful impact – Alerts need to be aligned to problems affecting the business and Instana has the industry’s fastest monitoring and analysis engine ingesting metric and tracing data on-the-fly and delivering performance insights in under 3 seconds.predict impending service outages.
  6. AI-Powered Problem Resolution and Troubleshooting Assistance
    Modern applications are simply too complex for humans to effectively manage on their own. Because of the dynamic nature of their structure, their complex dependencies and the sheer scale, machines must be applied to assist with monitoring and management tasks.

So there you have it – the 6 Pillars of Applying AI to Managing Microservice Applications. For a more in-depth look at all six pillars, download our new eBook The Six Pillars of Applying AI to Managing Microservice Applications.

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Instana, an IBM company, provides an Enterprise Observability Platform with automated application monitoring capabilities to businesses operating complex, modern, cloud-native applications no matter where they reside – on-premises or in public and private clouds, including mobile devices or IBM Z.

Control hybrid modern applications with Instana’s AI-powered discovery of deep contextual dependencies inside hybrid applications. Instana also gives visibility into development pipelines to help enable closed-loop DevOps automation.

This provides actionable feedback needed for clients as they to optimize application performance, enable innovation and mitigate risk, helping Dev+Ops add value and efficiency to software delivery pipelines while meeting their service and business level objectives.

For further information, please visit instana.com.