A Lock Contention Classifier Based on Java Lock Contention Anti-Patterns

Locks are essential in multi-threaded applications as they provide a solution to synchronization of shared resources. However, improper management of locks and threads can lead to contention and surface as run-time performance degradation in the application. Nowadays, performance engineers use legacy tools and their experience to determine causes of lock contention but it takes significant expertise to use these tools. In this paper, a data clustering approach is presented to help identify lock contention faults. The classifier is trained leveraging run-time performance data acquired from a catalog of lock contention Java anti-patterns and code smells. The K-means unsupervised classifier algorithm was used to create the classification model and the results show that lock contentions can be classified into three clusters that can be identified into those caused by a) threads spending too much time inside the critical section, b) threads blocked because of high frequency access requests, and c) threads with a low contention. This classifier is intended to be used to help tailor recommendations to the developer based on the lock contention anti-patterns and type of lock contention.