Addressing False Causality while Detecting Predicates in Distributed Programs

in Distributed Programs Ashis Tarafdar Vijay K. Garg Dept. of Computer Sciences Dept. of Electrical and Computer Engg. University of Texas at Austin University of Texas at Austin Austin, TX 78712 Austin, TX 78712 ([email protected]) ([email protected]) Abstract The partial-order model of distributed computations based on the happened before relation has been criticized for allowing false causality between events. Our strong causality model addresses this problem by allowing multiple local threads of control. This paper addresses the predicate detection problem for the class of weak conjunctive predicates in the strong causality model. We show that, in general, the problem is NP-complete. However, an e cient solution is demonstrated for a useful sub-case. Further, this solution can be used to achieve an exponential reduction in time for solving the general problem. Our predicate detection algorithms can be applied to distributed debugging when processes have independent events, as in multi-threaded processes.