An Adaptive VM Provisioning Method for Large-Scale Agent-Based Traffic Simulations on the Cloud

Using the Cloud for large-scale distributed simulations, such as agent-based traffic simulations, sounds like a good idea, as it is possible to provision and release easily processing nodes (e.g., Virtual machines) in the Cloud. However, the question is complex as it involves users' objectives, such as, time to process the simulation and cost of the simulation, and because the workload evolves in distributed simulations, in each node and the whole system, and this impact the resource provisioning plans. This paper proposes two main contributions: (i) a method for efficient utilization of computational resources for distributed agent-based simulations, providing a mechanism that adapts the resource provisioning to users' objectives and workload evolution, and (ii) a staged asynchronous migration technique to limit the migration overhead when the number of workers change. Our preliminary experimental results on a 24 hour scenario of traffic in the city of Tokyo show that our system outperforms a static provisioning by 12% in average and 23% during periods when workload changes a lot.