SOSP '87 Proceedings of the eleventh ACM Symposium on Operating systems principles
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
Parallel Vehicular Traffic Simulation using Reverse Computation-based Optimistic Execution
Proceedings of the 22nd 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
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
Efficiently Scheduling Multi-Core Guest Virtual Machines on Multi-Core Hosts in Network Simulation
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
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With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES) over multiple virtual machines, in contrast to executing in native mode directly over hardware as is traditionally done over the past decades. While mature VM-based parallel systems now offer new, compelling benefits such as serviceability, dynamic reconfigurability and overall cost effectiveness, the runtime performance of parallel applications can be significantly affected. In particular, most VM-based platforms are optimized for general workloads, but PDES execution exhibits unique dynamics significantly different from other workloads. Here we first present results from experiments that highlight the gross deterioration of the runtime performance of VM-based PDES simulations when executed using traditional VM schedulers, quantitatively showing the bad scaling properties of the scheduler as the number of VMs is increased. The mismatch is fundamental in nature in the sense that any fairness-based VM scheduler implementation would exhibit this mismatch with PDES runs. We also present a new scheduler optimized specifically for PDES applications, and describe its design and implementation. Experimental results obtained from running PDES benchmarks (PHOLD and vehicular traffic simulations) over VMs show over an order of magnitude improvement in the run time of the PDES-optimized scheduler relative to the regular VM scheduler, with over 20× reduction in run time of simulations using up to 64 VMs. The observations and results are timely in the context of emerging systems such as cloud platforms and VM-based high performance computing installations, highlighting to the community the need for PDES-specific support, and the feasibility of significantly reducing the runtime overhead for scalable PDES on VM platforms.