122,476 research outputs found
ANALYSIS OF FUTURE EVENT SET ALGORITHMS FOR DISCRETE EVENT SIMULATION
This work reports on new analytical and empirical results on the performance of algorithms for handling the
future event set in discrete event simulation.
These
results provide a clear insight to the factors affecting algorithm performance; evaluate the "hold" model, often used to study future event set algorithms; and determine the best algorithm(s) to use
AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS
Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems
MoMo: a group mobility model for future generation mobile wireless networks
Existing group mobility models were not designed to meet the requirements for
accurate simulation of current and future short distance wireless networks
scenarios, that need, in particular, accurate, up-to-date informa- tion on the
position of each node in the network, combined with a simple and flexible
approach to group mobility modeling. A new model for group mobility in wireless
networks, named MoMo, is proposed in this paper, based on the combination of a
memory-based individual mobility model with a flexible group behavior model.
MoMo is capable of accurately describing all mobility scenarios, from
individual mobility, in which nodes move inde- pendently one from the other, to
tight group mobility, where mobility patterns of different nodes are strictly
correlated. A new set of intrinsic properties for a mobility model is proposed
and adopted in the analysis and comparison of MoMo with existing models. Next,
MoMo is compared with existing group mobility models in a typical 5G network
scenario, in which a set of mobile nodes cooperate in the realization of a
distributed MIMO link. Results show that MoMo leads to accurate, robust and
flexible modeling of mobility of groups of nodes in discrete event simulators,
making it suitable for the performance evaluation of networking protocols and
resource allocation algorithms in the wide range of network scenarios expected
to characterize 5G networks.Comment: 25 pages, 17 figure
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Optimizing simulation on shared-memory platforms: The smart cities case
Modern advancements in computing architectures have been accompanied by new emergent paradigms to run Parallel Discrete Event Simulation models efficiently. Indeed, many new paradigms to effectively use the available underlying hardware have been proposed in the literature. Among these, the Share-Everything paradigm tackles massively-parallel shared-memory machines, in order to support speculative simulation by taking into account the limits and benefits related to this family of architectures. Previous results have shown how this paradigm outperforms traditional speculative strategies (such as data-separated Time Warp systems) whenever the granularity of executed events is small. In this paper, we show performance implications of this simulation-engine organization when the simulation models have a variable granularity. To this end, we have selected a traffic model, tailored for smart cities-oriented simulation. Our assessment illustrates the effects of the various tuning parameters related to the approach, opening to a higher understanding of this innovative paradigm
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