Conservative distributed discrete-event simulation on the Amazon EC2 cloud: An evaluation of time synchronization protocol performance and cost efficiency

Distributed execution of simulation models comes into play when memory limitations of a single computational resource prohibit their execution. In addition, the potential for parallel execution of a model on a distributed platform through the integration of multiple computational cores, can potentially reduce the execution time of a simulation. However, such gains can be voided by the overhead that time synchronization protocols for parallel and distributed simulation induce. This overhead is determined by the protocol used, the characteristics of the simulation model, as well as the architectural and performance characteristics of the hardware platform used. Recently, Infrastructure-as-a-Service offerings in the cloud computing domain have introduced flexibility in acquiring access to virtualized hardware platforms on a pay-as-you-go basis. At present, it is however unclear to what extent these offerings are suited for the distributed execution of discrete-event simulations, and how the characteristics of different resource types impact the performance of distributed simulation under different time synchronization protocols. Likewise, it is unclear which type of resources are most cost-efficient for this type of workload. To our knowledge, this paper is the first to investigate these aspects through an assessment of the performance and cost efficiency of different conservative time synchronization protocols on a range of cloud resource types that are currently available on Amazon EC2. Our analysis shows that performance levels comparable to those realized on commodity hardware based-clusters are attainable, and that the relative performance of different synchronization protocols is retained on high-end IaaS resources. In terms of cost-efficiency, we find that IaaS products tailored to traditional cluster workloads do not necessarily constitute the optimal choice, and we assess the impact of different packing configurations for logical processes in this regard.