Design and analysis of parallel Monte Carlo algorithms
SIAM Journal on Scientific and Statistical Computing - Papers from the Second Conference on Parallel Processing for Scientific Computin
Discrete event simulations and parallel processing: statistical properties
SIAM Journal on Scientific and Statistical Computing
Parallel independent replicated simulation on a network of workstations
PADS '94 Proceedings of the eighth workshop on Parallel and distributed simulation
Proceedings of the 29th conference on Winter simulation
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
A recursive random search algorithm for large-scale network parameter configuration
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Grid Computing: Making the Global Infrastructure a Reality
Grid Computing: Making the Global Infrastructure a Reality
Grid-enabling FIRST: Speeding Up Simulation Applications Using WinGrid
DS-RT '06 Proceedings of the 10th IEEE international symposium on Distributed Simulation and Real-Time Applications
Large-scale network parameter configuration using an on-line simulation framework
IEEE/ACM Transactions on Networking (TON)
Distributed computing and modeling & simulation: speeding up simulations and creating large models
Proceedings of the Winter Simulation Conference
SakerGrid: simulation experimentation using grid enabled simulation software
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Panel on grand challenges for modeling and simulation
Proceedings of the Winter Simulation Conference
SESSL: A domain-specific language for simulation experiments
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We describe a middleware framework conceived to enhance the effectiveness and efficiency of existing simulation applications by providing three capabilities: (1) access to grid-based and cloud-based execution, (2) access to advanced Design of Experiments (DOE) methodologies such as simulation-based optimization, and (3) access to robust data processing and visualization. The framework has been applied to a variety of simulations in both commercial and open source programming languages employing both discrete and continuous modeling formalisms. A key design objective is to minimize the workload necessary to adapt a simulation for use with the framework. User experience to date reveals that the learning curve for the framework is reasonable, but further automation of key tasks would enhance the framework's utility.