HydraNetSim : A Parallel Discrete Event Simulator

Abstract: Discrete event simulation is the most suitable type of simulation for analyzing a complex system where changes happen at discrete time instants. Discrete event simulation is a major experimental methodology in several scientific and engineering domains. Unfortunately, a conventional discrete event simulator cannot meet with increasing demands of computational or the structural complexities of modern systems such as peer-to-peer (P2P) systems; therefore parallel discrete event simulation has been a focus of researchers for several decades. Unfortunately, no simulator is regarded as a standard which can satisfy the demands of all kinds of applications. Thus while given a simulator yields good performance for a specific kind of applications, it may failed to be efficient for other kinds of applications. Furthermore, although technological advancements have been made in the multi-core computing hardware, none of the mainstream P2P discrete event simulators is designed to support parallel simulation that exploits multi-core architectures. The proposed HydraNetSim parallel discrete event simulator (PDES) is a step toward addressing these issues. Developing a simulator which can support very large numbers of nodes to realize a massive P2P system, and can also execute in parallel is a non-trivial task. The literature review in this thesis gives a broad overview of prevailing approaches to dealing with the tricky problems of simulating a massive, large, and rapidly changing system, and provides a foundation for adopting a suitable architecture for developing a PDES. HydraNetSim is a discrete event simulator which allows parallel simulation and exploits the capabilities of parallelization of modern computing hardware. It is based on a novel master/slave paradigm. It divides the simulation model into a number of specific slaves (a cluster of processes) considering the number of cores provided by the underlying computing hardware. Each slave can be assigned to a specific CPU on a different core. Synchronization of the slaves is achieved by proposing a variant of the classic Null-Message Algorithm (NMA) with a focus on keeping the synchronization overhead as low as possible. Furthermore, HydraNetSim provides log information for debugging purposes and introduces a new mechanism of gathering and writing simulation results to a database. The experimental results show that the sequential counterpart of HydraNetSim (SDES) takes 41.6% more time than HydraNetSim-2Slave and 23.6% than HydraNetSim-3Slave. HydraNetSim-2Slave is 1.42 times faster, consumes 1.18 times more memory, and supports 2.02 times more nodes than a sequential discrete event simulator (SDES). Whereas, HydraNetSim-3Slave executes 1.24 times faster, consumes 2.08 times more memory, and supports 3.04 times more nodes than SDES. The scaling factor of HydraNetSim is ⌈(β-1)'102.04%⌉ of the maximum numbered of nodes supported by SDES; where β is the number of slaves.