Showing papers by "Richard M. Fujimoto published in 2016"

Research Challenges in Parallel and Distributed Simulation

Abstract: The parallel and distributed simulation field has evolved and grown from its origins in the 1970s and 1980s and remains an active field of research to this day. A brief overview of research in the field is presented. Future research topics are explored including areas such as problem-driven simulation of large-scale systems and complex networks, exploitation of graphical processing unit hardware and cloud computing environments, predictive online simulation for system management and optimization, power and energy consumption in mobile platforms and data centers, and composition of heterogeneous simulations.

Data-Driven Vehicle Trajectory Prediction

Abstract: Vehicle trajectory or route prediction is useful in online, data-driven transportation simulation to predict future traffic patterns and congestion, among other uses. The various approaches to route prediction have varying degrees of data required to predict future vehicle trajectories. Three approaches to vehicle trajectory prediction, along with extensions, are examined to assess their accuracy on an urban road network. These include an approach based on the intuition that drivers attempt to reduce their travel time, an approach based on neural networks, and an approach based on Markov models. The T-Drive trajectory data set consisting of GPS trajectories of over ten thousand taxicabs and including 15 million data points in Beijing, China is used for this evaluation. These comparisons illustrate that using trajectory data from other vehicles can substantially improve the accuracy of forward trajectory prediction in the T-Drive data set. These results highlight the benefit of exploiting dynamic data to improve the accuracy of transportation simulation predictions.

Dynamic data driven application systems for smart cities and urban infrastructures

Abstract: The smart cities vision relies on the use of information and communication technologies to efficiently manage and maximize the utility of urban infrastructures and municipal services in order to improve the quality of life of its inhabitants. Many aspects of smart cities are dynamic data driven application systems (DDDAS) where data from sensors monitoring the system are used to drive computations that in turn can dynamically adapt and improve the monitoring process as the city evolves. Several leading DDDAS researchers offer their views concerning the DDDAS paradigm applied to realizing smart cities and outline research challenges that lie ahead.

Profiling Energy Consumption in Distributed Simulations

Abstract: An energy profile indicates the amount of energy consumed by different parts of a parallel or distributed simulation program. Creating energy profiles is not straightforward because high precision, low overhead energy measurement mechanisms may not be available, and it is not straightforward to determine the amount of energy consumed by different hardware components such as the CPU, memory system, or communication circuits that are operating concurrently throughout the execution of the distributed simulation. Techniques to create energy profiles of distributed simulation programs are described. A model is proposed that differentiates the energy consumed by the distributed simulation engine versus simulation application code, and energy consumed for computation versus that required for communication. A methodology and techniques are described to create energy profiles for these aspects of the distributed simulation. A study is described to illustrate this methodology to profile a distributed simulation synchronized by the Chandy/Misra/Bryant synchronization algorithm for a queuing network simulation. Empirical data are presented to validate the energy profile that is obtained.

Energy consumption of data driven traffic simulations

Abstract: Dynamic Data-Driven Application Systems (DDDAS) implemented on mobile devices must conserve energy to maximize battery life. For example, applications for online traffic prediction require use of real-time data streams that drive distributed simulations. These systems involve embedding computations in mobile computing platforms that establish the state of the system being monitored and collectively predict future system states. Understanding where energy consumption takes place in such systems is vital to optimize its use. Results of an empirical investigation are described that measure energy consumption of aspects such as data streaming, data aggregation, and traffic simulation computations using different modeling approaches to assess their contribution to overall energy consumption.

Real-time data-driven traffic simulation for performance measure estimation

Abstract: Congestion is a major issue in transportation sector. As professionals in the transportation field are increasingly exploring new solutions to alleviate traffic congestion, interest in the use of on-line simulation as a tool for estimating metrics of the traffic network for use in real-time operations has grown. The goal of the on-line simulation is to provide traffic information to facilitate more informed travel decisions and enable improved active traffic management. Performance estimation of arterials is a particularly challenging problem because it includes complexities not present in highways. The results of a sequence of experiments are presented to evaluate the effectiveness of a dynamic, data-driven, simulation-based system for estimating arterial performance measures in real-time. The envisioned system is comprised of a microscopic traffic simulation model driven by point sensor data. The conceptual framework of the system is presented, highlighting its key components. Four iterative applications of the framework are then presented, including a proof of concept experiment, two field tests and, a pseudo-field test involving origin-destination pairs from the Federal Highway Administration (FHWA) next generation simulation dataset. The results of the four applications demonstrate the feasibility of employing point sensor data to drive a microscopic traffic simulation and estimate arterial performance measures in real-time.

An approach to integrate inter-dependent simulations using HLA with applications to sustainable urban development

Abstract: Challenges such as understanding sustainable urban development require modeling interdependencies and interactions among systems. The High Level Architecture (HLA) provides an approach to studying these aspects by integrating separately developed simulations in a distributed computing environment. These applications require coupling interdependent simulations and sequencing their execution to ensure certain data dependence requirements are met. An approach to specifying the proper sequence of execution of interdependent simulations using SysML sequence diagrams is proposed. A means to implement these specifications by automatically generating code using HLA's time management services is described. This approach is demonstrated through the creation of a federated simulation to model interactions among land use, transportation, and transit in the San Diego area by integrating widely used simulators such as UrbanSim and MATSim.

Link Partitioning in Parallel Simulation of Scale-Free Networks

Abstract: It has been observed that many networks arising in practice have skewed node degree distributions. Scale-free networks are one well-known class of such networks. Achieving efficient parallel simulation of scale-free networks is challenging because large-degree nodes can create bottlenecks that limit performance. To help address this problem we describe an approach called link partitioning where each network link is mapped to a logical process (LP) in contrast to the conventional approach of mapping each network node to an LP. Link partitioning is discussed in the context of packet-level simulations of telecommunication networks. The parallelism of link partitioning relative to node partitioning is examined in terms of an idealized execution using the well-known YAWNS synchronization algorithm. Further, a critical path analysis suggests that there is much more parallelism available in these simulations than can be exploited using the YAWNS algorithm.