I first came upon the term root-cause analysis (RCA) while working at a network management startup. The concept was to determine why a problem occurred so that repair could happen sooner and service restored. To do this required a discovery of the topology of a network and its devices in order to understand where a problem could occur and the relationship between the various parts. Monitoring was necessary in order to identify that a failure occurred and provide notification.

However, the challenge in doing this was that many failure events are received in seemingly random order; thus, it is very difficult to differentiate which events signified symptoms of the problem and which event represented the actual cause. To resolve this, some solutions constructed elaborate causality chains in the hope you could follow them backwards in time to the "root-cause". This is akin to following smoke and having it lead you to the fire. Well it does work, if you do it fast enough and before the whole forest is in flames.

The obvious next thing to do was apply this to applications. It certainly seemed like a good idea at the time ... but it turned out to be much harder than expected. Why harder? Applications are far more complex than networks with many more variations in behavior and relationship. So, instead monitoring systems were applied to the various silos of application architecture such as web servers, application servers, middleware, databases and others.

For many years the focus of APM was on making the application server run better. And from that perspective, it was successful. However, while the application server became more reliable and ran faster, the two key features IT Operations management desire: getting alerted to problems before the end user is affected and being pointed in the right direction have not improved much.

Part of the difficulty in this sort of multiplicity of monitoring tools world is that there so many sources of events and so many moving parts. Is the cause capacity, a stuck message, configuration issues or even worse a misunderstanding of business requirements? Perhaps, the application is running just fine with all indicators green, but the results aren't what the business expected. Or it works fine for users in one group, but not for another. These are very difficult problems to unravel.

An approach Forrester Research suggests is to bring the events from the various sources to a single pain of glass and perform a root-cause analysis. The suggestion is made to use a technology called Complex Event Processing (CEP) to search in real-time for patterns based on events from multiple sources that together describe a problem.

CEP is very good at identifying situations spanning multiple event streams, correlating the individual events together into the "big picture", the situation. Analogous to this is the concept in QA of test cases. Think of situations as the test cases that occur spontaneously in production. APM is not for the faint of heart.

CEP can tie the seemingly unrelated events together into a picture that tells a story, what happened and what triggered it. CEP, using rules is of course dependent on the quality and completeness of those rules. But, that is something that grows ever better over time. A new situation can be described and prevented from ever causing harm again. Without the relationship between the events from the various sources, that would not be possible. We would just be fixing the web server or the database or the application server. With this approach, we are fixing the problem.

CEP represents an actionable form of analytics. You can add CEP analytics to your APM including your currently deployed monitoring solutions as it is inherently a multi-source approach. Utilizing this and delivering root-cause analysis can improve your incident management process. It can help you achieve the IT Ops goals of: getting alerted to problems before the end user is affected and being pointed in the right direction.

Charley Rich is VP Product Management and Marketing at Nastel Technologies.