Today’s information systems are highly complex and subject to change over time. Legacy systems and more or less modern applications coexist with relational, non-relational, and specific databases; with on-premises infrastructure, in private clouds, or based on one or more public clouds (multicloud).
Comprehensive monitoring (cloud monitoring) is difficult to achieve even as a single snapshot, and becomes even more complex when planning and controlling future resources and costs.
Only effective monitoring can guarantee high and lasting performance for your software assets.
The reasons for cloud monitoring
Constant system monitoring allows you to manage the performance and availability of software applications. This leads to fast response times, improved processing, and satisfied customers. Above all, the digital end-user experience is one of the main objectives when it comes to interactions with real users and commercial transactions.
Such a rich landscape of components generates a very large number of events to record, frame, and manage. Monitoring tools simplify these steps by directing you to the component that could be causing performance issues. It also helps improve performance and related provisioning in the short and medium term.
Obviously, there is a difference in the granularity of the analysis depending on the degree of software modularity. Legacy systems can be analyzed in their large block structure, while the more closely they adhere to the microservices approach, the more granular the direct analysis will be.
Complex solutions for complex systems
Obviously, most cloud systems provide a monitoring tool. The suitability of these tools for business objectives must be assessed both in absolute terms and in relation to the possibility of integration with innovative tools. Recently, there has been a lot of focus on AI in monitoring and subsequent decision-making phases, but new possibilities are constantly being added to the rich landscape of cloud monitoring.
This is why sufficiently complex systems require an equally complex control solution that must support a sufficient number of databases, cloud services, and technologies. Much of today’s software, and that of the coming years, is container-based; therefore, these units must also be properly monitored. The solution must integrate into the delivery pipeline, automating where possible and providing a KPI-based evaluation system. In particular, automation efficiency must be considered among the most widely used global indices.
Finally, we must not forget the importance of system metrics as seen by users.
An initial assessment concerns both the back-end and the front-end.
To go into a little more detail, let’s look at the main functions provided by the monitoring platforms on the market today.
Database monitoring
Let’s start with an observation: most bottlenecks in application performance arise in the storage structure. The percentage is constantly rising, as the number and type of databases that each application can access is constantly increasing. Database performance management allows you to track data as it flows, identifying when a problem arises and thus creating the best situation for resolving it. This requires a tool for analyzing all database activities, down to individual SQL and NoSQL statements.
CI/CD analysis
The developer management area absolutely needs innovation metrics. Choosing the right ones, with analysis on each build, allows for continuous improvement of performance at the CI/CD pipeline level.
Application performance management
Applications deployed in the cloud can be monitored end-to-end from a single point of control. It can be important to get to the code, going down to the business transaction level. The most frequently observed environments are Java, .NET, Node.js, and PHP.
Cloud server monitoring
Once application performance flows have been identified, infrastructure performance analysis allows for a very interesting comparison. In these cases, we talk about “contextual visibility” of mismatches between the two flows (software and hardware) and also of the related management costs.
Mainframe monitoring
Architectures that include mainframes need to integrate specific analysis into the general flow. This is not a difficult operation, given the nature of mainframe business. Even complex mainframe applications are fairly easy to track correctly.
Stack monitoring
Monitoring the performance of the entire stack provides a very broad context. Information is collected on data, applications, cloud, infrastructure (possibly containers), and UX. For this reason, monitoring must include stack-level analysis and be tailored to the relevant indices.
In particular, by focusing on user volume, it is also possible to set up automatic resource scaling.