Container monitoring is the process of performing real-time diagnostics on containerized applications and microservices. Because containers are volatile or "ephemeral" in nature compared to applications running on virtual servers or "bare metal" servers, monitoring them is more difficult, but not impossible.
This is more and more true, as the architectures that are spreading today are the ones based on microservices requiring a monitoring activity similar to the one provided for traditional environments. It implies a container monitoring system must be able to collect metrics that analyze and measure whether the performance of running applications is in line with the expected business objectives.
Container monitoring and observability
In order for a container monitoring system to perform its task correctly, it must provide for the use of special agents in each node. It also needs to allow the IT team to see some essential metrics on a dashboard. Taken together, metrics, logs and traces represent a modern concept of monitoring that is now called observability.
In container monitoring, together with the amount of data to be collected, the detection time is also fundamental, and this includes the period between the encounter with a problem, the identification of its cause and then its resolution.
Log management allows the storage and aggregation of data to record all events that have to do with the systems' security. This is why log management is crucial within the container monitoring activities.
Tracking everything that happens at an infrastructural and application level is the guarantee of having full visibility. Traceability becomes necessary, especially if you want to determine, in addition to technological performance, what the impact on the user experience is.
How container monitoring works
To ensure the proper functioning of the applications inside the containers in order to meet the expected objectives, the metrics must be collected and analyzed in real-time. In short, a containerized platform monitoring solution is an application performance management (APM) tool that gives IT teams an immediate view on the operation of application services. This way, the development, release and distribution processes can happen quickly, according to operating cycles based on Agile and DevOps logic.
Compared to the more traditional Application Performance Monitoring solutions they leverage a series of features such as support for different types of data, easy access to the libraries of the languages used, dashboards and visualization systems for rapid information exploration.
With containerization, monitoring operations become more complicated and require to be conducted at multiple levels, considering both the underlying infrastructures - bare-metal servers or virtual machines - and the application layer - runtime environment and microservices. The metrics analyzed (for hardware systems and containers) focus on memory and CPU usage, in particular, highlighting their limits and advising IT teams when it becomes necessary to scale resources. Since components that must interact and move smoothly within a distributed application system run on containers, network performance is a point to keep under close observation and control.
Two main benefits of container monitoring
We talked about how container monitoring works, and now let's consider the benefits it brings to the companies' business, and not just to IT. These advantages can be summarized in two main ones: the ability to identify the relationships between all objects, and the identification of problems in a proactive way to resolve critical issues before they cause malfunctions or even the interruption of systems.
Ability to identify relationships between all objects
Being able to discern between irrelevant anomalies and performance problems is critical. This is the only way to immediately trace the cause, activating the most appropriate remediation actions. It is not enough to acquire the inherent performance data of containers, but it is necessary to understand their impact on users. In other words, to acquire information about the "real user experience" is key, as well as to make sure container monitoring is functional to business objectives. This means that not only logs, events and metrics can be collected to understand what is happening, but the container monitoring tools must be able to correlate all information also through the use of AIOps tools.
Identifying problems, proactively
To optimize company resources, it becomes important to be aware of the link between the performance of applications and the relative performance of the infrastructure on which they rely. In fact, today's race to adopt containers and microservices goes together with a tendency of favoring more agile cloud environments, often hybrid and multi-cloud. The use of these advanced technological solutions, however, cannot be separated from careful attention to monitoring functions that are able to keep the entire stack under control. For this reason, container monitoring makes use of artificial intelligence and machine learning: to anticipate the onset of problems before they occur, thanks especially, as mentioned before, to the use of AIOps platforms.