Traditional monitoring fails in a modern IT environment when systems and IT applications become more complex, more agile, partly due to interconnect connectivity between applications and the external world and potentially complete supply chains.
Elasticity is also coming into this picture, especially with cloud and Azure. And actually, it's no different from any other modern IT application. The challenges, resources, and metrics that you need to monitor with Azure Service explode, especially when you start introducing microservices.
Furthermore, Microsoft Azure is dynamic, often in a way that code deployments and integrations are quicker and more agile and less waterfall, and containers are being used a lot more. Scaling can be set to be elastic, for microservices especially, and when things are getting elastic, Azure subscription cost becomes also an important part of monitoring. These are not silo applications, these systems are now interconnected.
The next level of monitoring Azure resources
What if a performance monitoring solution could deliver comprehensive monitoring across all performance metrics, billing, infrastructure, application containers, and microservices in Azure?
What if Microsoft Azure monitoring can be completely automated, removing the traditional monitoring manual labor of setting up and defining static thresholds and defining alerting scenarios that typically end up only covering a very limited part of the monitoring unit? Read on to see how it could be done in less than 15 minutes.
Your organization needs access to insights and analytics that let you operate at the speed your business requires - a more agile way to free up your resources and get more done. With AIMS, you can remove the manual work that your employees can no longer accomplish, and which is not possible to sustain with traditional monitoring.
Scalable application performance monitoring relies on automation
Autodiscovery is identifying the resources in your Azure subscription or resource groups or environment. It's about auto capturing performance data and for that cycles of billing data. So, identifying what should be monitored, collecting and capturing performance data.
The second part of that automation engine is about self-learning. So, how can you understand which parts of the Azure resources you use are communicating, interacting, and have some sort of dependencies? That is going to be critical for you to know when something happens in one microservice, or if there is a load on the CPU on a Virtual Machine. How is that going to impact the rest of your applications as may be exposed to your customers? For example; what can you do to optimize it?
And also understanding the normal behavior of performance data that you capture. So the only way to be able to monitor the complexity and scope of performance metrics that your organization needs in these kinds of complex environments is to use Machine Learning to learn what is normal behavior, as you're moving from a few, maybe some 10s of performance metrics, to 1000s, or maybe 10s of 1000s of performance metrics to keep the necessary monitoring data in control.
Self-learning drives anomaly detection. Automating anomaly detection gives you an insight into performance bottlenecks that can bring down your business, and also surfaces important data into dashboards, which are essential for a general understanding of the health of your Azure environment.
More than a monitoring tool
What we've built with AIMS is a unique data processing pipeline. AIMS consumes time-series performance data from any of 300 + technologies, then that data is being normalized or aggregated into time series, which we apply Machine Learning and Artificial Intelligence to understand relationships between resources, and also to automatically detect anomalous behavior across all the collected data.
The outputs of all the above are presented in a user-friendly UI. It is created in a way that users can access early notifications to identify problems that can bring down critical services that your business relies on for your profit and loss. AIMS dashboards are governance and knowledge, and they help you understand what's really going on in your technology stack.
With regards to Azure infrastructure monitoring, we are relying on the Azure monitor API, which Microsoft exposes. So, connecting AIMS to your Azure subscription or any of these sources that you want to let them have access to is simple to configure in your Azure portal.
You can further enrich that data with additional agents like on VMs, install a dedicated Windows Server Agent on a SQL Server agent. AIMS does the monitoring through APIs, but you can further enhance the monitoring by installing dedicated agents. Or you can also, of course, do custom monitoring, if you prefer.
Watch the 15-minute demo on monitoring Microsoft Azure with AIMS
Features, covered in the demo include:
- AIMS auto-discovery of resources and new deployments
- AIMS auto-capture performance and billing data for all resources
- AIMS dynamic monitoring baselines for each metric powered by machine learning
- AIMS anomaly detection that identifies anomalies for all metrics for proactive monitoring
- AIMS topology discovery that identifies relationships and dependencies between resources without any instrumentation of code
- AIMS score and sunburst that let you understand, navigate, monitor, and govern the IT your business runs on
The Azure monitoring tool your organization needs
Provide the toolkit your DevOps and IT operations teams need, so they can easily access real-time application insights, get faster to the root cause, and achieve full-stack observability.
- Comprehensive monitoring of Azure covering performance and billing for infrastructure, applications, containers, and microservices
- Automated, no manual work - free up time for your team and up and running in 15 minutes
- The insight that lets you operate at the speed your business requires