Experience Report: Log-Based Behavioral Differencing
Maayan Goldstein, Danny Raz, Itai Segall · 2017
Monitoring systems and ensuring the required service level is an important operation task. However, doing this based on external visible data, such as systems logs, is very difficult since it is very hard to extract from the logged data the exact state and the root cause to the actions taken by the system. Yet, identifying behavioral changes of complex systems can be used for early identification of problems and allow proactive correction measurements. Since it is practically impossible to perform this task manually, there is a critical need for a methodology that can analyze logs, automatically create a behavioral model, and compare the behavior to the expected behavior.In this paper we propose a novel approach for comparison between serviceexecutions as exhibited in their log files. The behavior is captured by FiniteState Automaton models (FSAs), enhanced with performance related data, bothmined from the logs. Our tool then computes the difference between the current model and behavioral models created when the service was known to operate well. A visual framework that graphically presents and emphasizes the changes in the behavior is then used to trace their root cause. We evaluate our approach over real telecommunication logs.