Resettable Encoded Vector Clock for Causality Analysis with an Application to Dynamic Race Detection

Causality tracking among events is a fundamental challenge in distributed environments. Most previous work on this subject has been focused on designing an efficient and scalable protocol to represent logical time. Several implementations of logical clocks have been proposed, most recently the Encoded Vector Clock (EVC), a protocol to encode Vector Clocks in scalar numbers through the use of prime numbers, to improve performance and scalability. In this thesis work we propose and formalize the concept of Resettable Encoded Vector Clock (REVC), a new logical clock implementation, which builds on the EVC to tackle its very high growth rate issue. We show how our REVC can be applied in both shared memory systems and message passing systems to achieve a consistent logical clock. We show, through practical examples, a comparison between REVC's and EVC's growth rates, to demonstrate our clock's advantages. Finally, we show a practical application of the REVC to the Dynamic Race Detection problem in multi-threaded environments. We compare our tool to the currently existing VC-based tool DJIT+ to show how the REVC can help in achieving higher performance with respect to the Vector Clock.