Autonomic Partition-Aware Malleable Microscopic Traffic Simulation

In this work, we present Autonomic CityMoS, a malleable parallel, and distributed traffic simulator engine that can automatically adapt the number of computing nodes in response to dynamic computational demands. We combine a snapshot system that enables data distribution, two predictive cost models that estimate the system speedup based on key metrics, including partitioning characteristics, and a policy that leverages these models to maintain a steady simulation pace. Autonomic CityMoS is able to effectively keep the simulation pace under varying traffic pattern flows, all without prior knowledge of traffic conditions. Although adaptation times are significant, we still observe an improvement in resource utilization compared with a run with static allocation of compute resources. The work presented in this paper should serve as an example of malleable simulation execution, where the objective is not to maximize performance but rather ensure a sustainable execution of large distributed simulation targeting to optimize the trade-off between target speed-up and overall compute resource utilization. Target applications include, but are not limited to, very large visual and interactive simulations.