Comparing Azure Monitoring Strategies: AIMS vs. a Manual Approach



February 14, 2022

The AIMS Platform was recently extended with a new agent for Microsoft Azure. AIMS uses machine learning to analyse data on tens of thousands of metrics from your Azure subscription and predicts deviations from normal behavior – in other words, automatic Azure anomaly detection for performance, Azure consumption costs and more.

But Azure comes with monitoring features out-of-the-box, right? In this post, I'll compare the difference between working with Azure default monitoring capabilities and AIMS.

Monitoring in Azure

Azure Monitor is part of Azure Management, which refers to the tasks and processes required to maintain your business applications and the resources that support them. Azure has multiple services and tools that work together to provide complete management.

Azure Monitor

As Microsoft says, “Monitoring is the act of collecting and analyzing data to determine the performance, health and availability of your business application and the resources it depends on”.

An effective monitoring strategy will help you understand the detailed operation of the different components of your application and to increase your uptime by proactively notifying you of critical issues so that you can resolve them before they become problems. Monitoring in Azure is primarily provided by Azure Monitor which provides common stores for storing monitoring data, multiple data sources for collecting data from the different tiers supporting your application, and features for analyzing and responding to collected data.

As you can see in the image below, Azure monitor uses metrics and logs from applications, Operating Systems, Azure Resource, Azure Subscription, Azure Tenant and custom resource. These Metrics and log data can be looked at from different places all over Azure, like Application Insights, Metrics Explorer, Log Analytics etc.

azuremonitor_1

 

Monitoring using Azure is difficult because the data is all over the place. There is no single point of management, and you will have to know exactly where to find metrics, dashboards, views, logs and more.

In Azure, you can use Metrics Explorer to analyze collected metrics and plot them on a chart. This way you can track resource performance (such as a VM, website, or logic app) by pinning charts to an Azure dashboard.

When you need to be alarmed on deviations you must configure a static metric alert rule that sends a notification when the metric crosses a threshold.

You must manually route metrics to Log Analytics to analyze metric data together with log data and to store metric values for longer than 93 days.

You can also stream metrics to an Event Hub to route them to Azure Stream Analytics or to external systems.

azuremonitor_2


Log Analytics uses activity logs from Azure resources that include information on their configuration and health and Diagnostic logs that provide insights into their operation.

Application data is collected by Application Insights and can be used to provide insights into a particular application or service such as Container Insights, VM Insights, or Resource Group Insights.

You can use the Log Analytics page in the Azure portal to write queries analyzing log data.

Log alert rules have to be configured manually, before sending a notification when the results of the query match a particular result.

azuremonitor_3


Billing metrics are also collected in Azure, but since every service is charged in a different way it is hard to make sense of some of the data.

The billing metrics are exposed at service level.

Monitoring with AIMS

AIMS is an AIOps (Artificial Intelligence for IT Operations) solution that simplifies integration and Azure monitoring and reporting. It helps to unravel the complexity of Azure and connected systems, to make informed decisions, to set priorities and to improve the reporting and monitoring of activities.

AIMS' mission is to obtain simplified insight into software performance in complex and connected systems. AIMS agents offer intelligent application monitoring for connected systems and provides a predictive ability; proactive anomaly detection prevents downtime incidents.

AIMS uses machine learning-driven monitoring, which helps us discover unknown anomalies, reduce resource needs, shift the focus to building business value. AIMS can detect anomalies in both a single system and multiple systems. The early detection feature can warn you before the detected abnormality becomes a real problem and causes processing problems or performance issues for transactions.

AIMS makes a graphical map of all systems that participate in the integration flow. You can view all systems from a bird's-eye view to get a complete overview of the entire Azure environment, or dive into the individual systems and components to view the collected data.

After connecting the agent to your Azure subscription, the agent collects one week of historical data from Azure. This data allows AIMS to build normal behavior patterns for every metric collected from each Azure service. Using these normal behavior patterns, AIMS identifies anomalies on each single metric vs normal behavior and correlates the anomalies across the rest of the metrics in “correlation group” to create anomaly warnings.

Anomaly warnings provide early notification of trouble with Azure Services or across cloud / hybrid environments.

As for all AIMS agents, the Azure Agent gives you great insight through custom reports and dashboards into the behavior of your Azure services that deliver your Software Business, and thanks to the metrics from the Azure billing section it also helps you control cost through early notifications of consumption or new resources that increase costs.

It is the only solution that uses machine learning and anomaly detection across metrics, consumption and billing data, providing consumption metrics on hourly basis to gain insight before billing data is available. It provides Analytics, Reports & Dashboards across Azure and on-premise technologies, proving the user with a single point of management for all monitoring needs.

 

Conclusion

Microsoft provides all information needed to be able to monitor your solutions in Azure. All metrics and log data is available to users, but they are all over the place. Not only do you need to know where to look, you should also know what to monitor for as the thresholds are static and have to be put in manually for every service you want to monitor and be alerted on.

The AIMS Azure agent takes a lot of this work out of your hands, as it collects and correlates all metrics and data needed to properly monitor your Azure services. It applies machine learning algorithms to get to know the normal behavior patterns and is able to automatically detect anomalies using dynamic thresholds. No manual steps needed, and you don’t need any knowledge about what is normal for the creation of thresholds. AI takes care of that for you!

We can conclude that monitoring using the AIMS agent makes developers and administrators' lifes easier.

Topics from this blog: Blog

Author

BizTalk and Azure integration consultant, technical writer and #aimsperformancepro.

Eva De Jong

BizTalk and Azure integration consultant, technical writer and #aimsperformancepro.

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