A Hierarchical Cluster Algorithm for Dynamic, Centralized Timestamps

Partial-order data structures used in distributed-system observation tools typically use vector timestamps to efficiently determine event precedence. Unfortunately, all current dynamic vector-timestamp algorithms either require a vector of size equal to the number of processes in the computation or require a graph search operation to determine event precedence. This fundamentally limits the scalability of such observation systems. In this paper we present an algorithm for hierarchical, clustered vector timestamps. We present results for a variety of computation environments that demonstrate such timestamps can reduce space consumption by more than an order-of-magnitude over Fidge/Mattern timestamps while still providing acceptable time bounds for computing timestamps and determining event precedence.