Analyzing application performance data takes time, but it's worth it, because in the future, IT administrators can solve problems faster.
What is the best way to monitor the performance of distributed applications?
Organizations install applications on multiple platforms — locally, in one or more clouds, or in both of these environments — and use distributed architectures, for which they deploy code in microservices and in containers. Compared to monolithic, distributed applications make it difficult to monitor and also have a poor effect on the efficiency and accuracy of the analysis. Therefore, organizations need to find the right tools and implement the right approach to monitoring distributed applications.
As distributed applications and computing become more and more popular, new tools for predictive IT analytics are emerging, designed to deal with the problems of a reactive IT infrastructure. Predictive technology assesses current statistics, trends, and historical data, and then makes assumptions about future or unknown events using machine learning and data analysis. Despite the fact that to work with predictive IT analytics, you need to have in-depth knowledge, even a small reduction in the number of incidents during the operation of the application will bring significant savings. However, predictive monitoring and analysis will not solve all the problems, because IT organizations will still need to independently decide what to track and when.
Distributed application problems
Outdated monolithic applications deployed on a single server replaced distributed applications with several components that are installed on many parts of the IT infrastructure. When monitoring applications, it is unacceptable to look only in one direction - it is necessary to monitor a large range of resources, including external storage, networks and computing power.
And at the same time, predictive analytics of distributed applications is becoming increasingly difficult. To find out which components of the application and infrastructure should be monitored, start from different parts of the system, and then come to a common denominator. The upper part of the system is the usability of the application.
From the client’s point of view, aspects of the application strongly affect the overall assessment, but it is difficult to apply predictive analytics to them. Application performance problems (for example, functions that work with alternate success) can occur relatively infrequently, so they are difficult to predict. However, usability is a very important indicator to underpin analytical information.
If the predictive analytics algorithm does not allow to use the collected information in several different systems to compile a complete picture, it cannot be called effective, because without this it will be difficult to determine how the problems of one system affect the entire application stack. The days of disparate applications behind - in their place come combined components. But from an operational point of view, this can lead to a multitude of errors and missing parts.
What is worth tracking
To combine several analytical tools in cloud and local systems to track distributed applications without any omissions, you need a whole team of IT professionals. If you do not have an unlimited budget for monitoring, this is unreasonable. The success of predictive tools depends on the methodology used to collect, share and use data — even more than on machine learning opportunities or the study of trends.
In order for the system to collect all the information necessary for the work of the IT department, the forward-looking IT analytics must also consider usability. If you put usability at the head of the table, the team of experts will be able to prevent or reduce errors and failures encountered by customers, or at least understand how these errors can affect the work, and propose a solution.
Limitations of predictive IT analytics
Predictive analytics for monitoring distributed applications is designed to detect and prevent errors, but it is impossible to avoid absolutely all errors or incidents. Analytics is not real-time.
The timing of predictive IT analytics is another very important aspect. Machine learning and data analysis are incompatible with real-time reporting. Both management and technical staff need to understand that predictive IT analytics systems need time to collect enough data for processing and analysis, after which you can expect any decent results. Depending on the amount of data, it can take several hours or several days. Administrators can reduce the amount of data, but this may not have the best effect on the accuracy of the analysis. Projected IT analytics should prevent problems in order to improve usability and optimize IT resource management. To send error messages and respond to incidents, it is necessary to implement other methods.
Projected IT analytics is not able to eliminate every possible incident that could affect the application stack. Large-scale power outages, malfunctioning of cloud providers and major equipment failures can occur completely unexpectedly. But the more data your organization has, the better results you can achieve. To succeed, it is necessary to understand the disadvantages and advantages of this technology and make the most of its opportunities.