Towards a Framework for Adaptive Resource Provisioning in Large-Scale Distributed Agent-Based Simulation

Large scale distributed agent-based simulations run on several computing units e.g., virtual machines in the Cloud, nodes in a supercomputer. Classically, these systems try to re-load-balance the nodes as overloaded nodes slow down the process.However another challenge in large scale distributed simulations is that the overall load evolves. In this paper we leverage on commodity computing to adapt resource provisioning number of computing units to the load during the execution of the simulation. We also propose an asynchronous migration mechanism that migrate workload between computing nodes efficiently when nodes wait for synchronisation barriers to happen. We validate our implementation on a scenario simulating one day of vehicular traffic in Tokyo, running on 2 to 8 machines depending on the demand. Our evaluation shows a 26% reduction in data migration time compared to a naive migration approach between computing units.