Efficient algorithms for distributed snapshots and global virtual time approximation
Journal of Parallel and Distributed Computing - Special issue on parallel and discrete event simulation
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
µsik " A Micro-Kernel for Parallel/Distributed Simulation Systems
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
The definitive guide to the xen hypervisor
The definitive guide to the xen hypervisor
Optimistic Synchronization of Parallel Simulations in Cloud Computing Environments
CLOUD '09 Proceedings of the 2009 IEEE International Conference on Cloud Computing
Master/worker parallel discrete event simulation
Master/worker parallel discrete event simulation
The impact of virtualization on network performance of amazon EC2 data center
INFOCOM'10 Proceedings of the 29th conference on Information communications
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
GVT algorithms and discrete event dynamics on 129K+ processor cores
HIPC '11 Proceedings of the 2011 18th International Conference on High Performance Computing
Conservative Distributed Discrete Event Simulation on Amazon EC2
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
| Hi-index | 0.00 |
Virtual machine (VM) technologies, especially those offered via Cloud platforms, present new dimensions with respect to performance and cost in executing parallel discrete event simulation (PDES) applications. Due to the introduction of overall cost as a metric, the choice of the highest-end computing configuration is no longer the most economical one. Moreover, runtime dynamics unique to VM platforms introduce new performance characteristics, and the variety of possible VM configurations give rise to a range of choices for hosting a PDES run. Here, an empirical study of these issues is undertaken to guide an understanding of the dynamics, trends and trade-offs in executing PDES on VM/Cloud platforms. Performance results and cost measures are obtained from actual execution of a range of scenarios in two PDES benchmark applications on the Amazon Cloud offerings and on a high-end VM host machine. The data reveals interesting insights into the new VM-PDES dynamics that come into play and also leads to counter-intuitive guidelines with respect to choosing the best and second-best configurations when overall cost of execution is considered. In particular, it is found that choosing the highest-end VM configuration guarantees neither the best runtime nor the least cost. Interestingly, choosing a (suitably scaled) low-end VM configuration provides the least overall cost without adversely affecting the total runtime.