Identification of Root Causes of Application Performance Problems

 Application performance monitoring (APM) helps monitor user experience and utilize predictive analytic to improve application performance every time there is a sudden surge in a number of users accessing the application / every time there is sudden degradation in application performance.

Use of Predictive Analytics

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Organizations can utilize predictive analytics to improve their application performance in the following fields:

Identification of Root Causes of Application Performance Problems

By identifying the root causes of application performance using learning techniques, organizations can focus on the right set of areas to take action. Predictive analytics can determine the characteristics of various attributes in each cluster that can provide in-depth insight into what needs to be done to achieve ideal performance and avoid specific obstacles.

Health Monitor application in real-time

Monitoring Real-Time Health Applications Through Multi-Variate Machine Learning Techniques (ML) enable organizations to capture and respond to the deregainality of health applications in a timely manner. Most applications rely on several services to print the actual health of the application. Data can consist of configuration data, application logs, network logs, error logs, log performance and more. Predictive analytic models can analyze past data during the time when the application is in a good condition and then identify whether the incoming data shows normal or not behavior.

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Prediction loading users

Predictive analytics can help in predicting user loads by analyzing past data. Organizations can use this data to prepare better to handle loading users who are predicted and provide guarantees of experience that will help reduce customer churn. This data can also help organizations better to plan their future IT infrastructure requirements and capacity utilization.

Predict application blackouts before it happens

Predict application downtime or blackouts before it happens to help carry out the maintenance needed on the application without downtime. This can save time organizations, money, and more. Before the application blackouts, IT infrastructure left many indirect instructions, or even days, before dying. Predictive analytic models can learn these patterns and continue to monitor similar events, predict future failure before they occur. With this type of prediction model in place, precautions can be taken at the right time.

Apply Predictive Analytics to estimate application performance

Predictive analysis in the field of improvement of application performance focuses on three main areas - loading estimated users, response time predictions, and infrastructure assessment.

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User loading predictions

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Traditionally organizations have relied on past peak user traffic in the past to generate the number of users who can access applications in the future. This model has its own limitations because it does not consider factors such as the emergence of new technology, changes in user behavior and other disturbing factors. By using AI / ML predicted analytics, businesses can avoid this trap as a forecasting model can be built by analyzing user behavior in real time.

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 We use hourly monitoring data from production monitoring for the past 2 years as input to the model. We found that the accuracy provided by a low model when we use classification and regression models because the data is lost and data imbalances.

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Data is normalized to handle lost data and wrangling data is done to reduce data imbalances and foses to neural networks. This increases the accuracy of the model to be around. 75-80%.