Proximity Load Balancing for Distributed Cluster-based Individual-oriented Fish School Simulations

Abstract

Partitioning and load balancing are highly important issues in distributed individual-oriented simulation. Choosing how to distribute individuals on the distributed environment can be a crucial factor at the moment of executing the simulation. Partitioning an individual-oriented system should be efficient in order to reduce communication involved in interaction between individuals belong to different logical processes. Furthermore, if the individual-oriented model exhibits mobility patterns, we should be able to maintain the load balancing in order to keep the global application performance. In this work, we present a proximity load balancing strategy for a distributed cluster-based individual-oriented fish school simulator. On one hand, we implement a robust cluster-based partitioning method by means of covering radius criterion and voronoi diagrams. We use a proximity criterion to distribute individuals on the distributed architecture. On the other hand, we propose a proximity load balancing strategy in order to maintain the application performance as the simulation progresses.