Adaptive Partitioning for Distributed Multi-Agent Simulations.

Agent-based modeling and simulation is an essential paradigm for complex, data-intensive research questions to absorb and process emergent insights from often large-scale scenarios. That demands its execution within a distributed simulation system. One critical factor of those runtime systems is distributing and partitioning involved agents. Unsuitable partitioning schemes lead to a computing load that needs to be synchronized continuously, which bears the risk of drastic performance reductions. Work from load balancing has produced a series of distribution classes that rely on geometric distribution. The partitioning decomposes the agent environment so that when the agent leaves one partition, it instantly switches to another partition. However, many simulations cannot be partitioned in this spatial way, for example, because no spatial reference exists or agents interact on different temporal-granularity levels. This paper presents a new partitioning approach for distributed agent-based simulation systems, supporting spatially and non-spatially attached simulations with freely definable distribution keys. Our approach defines groups of agents that migrate between servers at runtime following a partitioning plan. We represent the allocated resources by intercepting and aggregating them into a frequency graph. We aim to balance the load across servers and reduce the number of agent transfers by determining the frequency of interactions between agents and objects. We evaluate our approach with a real-world scenario and results show that this adaptive partitioning can increase the execution performance by a factor of 10 with up to 89% lower latencies than existing solutions.