IBM Turbonomic: Research garbage collector and database indexes in API´s and containers.

Researching a troubling application that integrates several back-end applications, APIs and web services, multiple databases, and even containers is very difficult for the IT department. You need the assistance of high-level Application Monitoring combined with Machine Learning to analyze the time-series event to look at the origin of the performance problem. IBM Turbonomic provides charts of all the infrastructure used by every application to have a depth analysis of the root causes of problems.

Yes, it has an impact on application development because it helps to avoid over-interaction with service calls that you already know are troubled with peaks of demand. Also, it helps to demonstrate that many of the problems are bad database design with poor indexes. Because all the infrastructure is working well but the database server is failing when you have a peak of query requests.

The complexity of the applications that use several resources like distinct databases and API calls to external servers does impossible the task of manual research of the cause of problems. The IT staff needs an Application Monitoring like IBM Turbonomic to respond to what is happening when the users are getting bad service from the applications.

You need to justify the purchase of infrastructure in the Cloud on Premises based on data and statistics. The traditional approach is just to increase the memory size of the servers until the performance problem disappears. But the situation could reach a point where more physical resources are not helping. You need IBM Turbonomic to check that the problem is the bad use of the garbage collector of a JAVA application or the bad design of database indexes.