A master/worker paradigm for executing large-scale parallel discrete event simulation programs over networkenabled computational resources is proposed and evaluated. In contrast to conventional approaches to parallel simulation, a client/server architecture is proposed where clients (workers) repeatedly download state vectors of logical processes and associated message data from a server (master), perform simulation computations locally at the client, and then return the results back to the server. This process offers several potential advantages over conventional parallel discrete event simulation systems, including support for execution over heterogeneous distributed computing platforms, load balancing, efficient execution on shared platforms, easy addition or removal of client machines during program execution, simpler fault tolerance, and improved portability. A prototype implementation called the Aurora Parallel and Distributed Simulation System (Aurora) is described. The structure and interaction of the Aurora components is described. Results of an experimental performance evaluation are presented detailing primitive timings and application performance on both dedicated and shared computing platforms.