Predicting potential deadlocks in multithreaded programs

摘要

In a multithreaded program, competition of threads for shared resources raises the deadlock possibility,rnwhich narrows the system liveness. Because such errors appear in specific schedules of concurrent executionsrnof threads, runtime verification of threads behavior is a significant concern. In this study, we extendedrnour previous approach for prediction of runtime behavior of threads may lead to an impasse. Such a predictionrnis of importance because of the nondeterministic manner of competing threads. The prediction processrntries to forecast future behavior of threads based on their observed behavior. To this end, we map observedrnbehavior of threads into time-series data sets and use statistical and artificial intelligence methods for forecastingrnsubsequent members of the sets as future behavior of the threads. The deadlock prediction is carriedrnout based on probing the allocation graph obtained from actual and predicted allocation of resources tornthreads. In our approach, we use an artificial neural network (ANN) because ANNs enjoy the applicable performancernand flexibility in predicting complex behavior. Using three case studies, we contrasted results ofrnthe current and our previous approaches to demonstrate results.