An agent-based framework for performance modeling of an optimistic parallel discrete event simulator

Purpose: The performance of an optimistic parallel discrete event simulator (PDES) in terms of the total simulation execution time of an experiment depends on a large set of variables. Many of them have a complex and generally unknown relationship with the simulation execution time. In this paper, we describe an agent-based performance model of a PDES kernel that is typically used to simulate large-sized complex networks on multiple processors or machines. The agent-based paradigm greatly simplifies the modeling of system dynamics by representing a component logical process (LP) as an autonomous agent that interacts with other LPs through event queues and also interacts with its environment which comprises the processor it resides on. Method: We model the agents representing the LPs using a “base” class of an LP agent that allows us to use a generic behavioral model of an agent that can be extended further to model more details of LP behavior. The base class focuses only on the details that most likely influence the overall simulation execution time of the experiment. Results: We apply this framework to study a local incentive based partitioning algorithm where each LP makes an informed local decision about its assignment to a processor, resulting in a system akin to a self organizing network. The agent-based model allows us to study the overall effect of the local incentive-based cost function on the simulation execution time of the experiment which we consider to be the global performance metric. Conclusion: This work demonstrates the utility of agent-based approach in modeling a PDES kernel in order to evaluate the effects of a large number of variable factors such as the LP graph properties, load balancing criteria and others on the total simulation execution time of an experiment.