Desynchronization of simulation and optimization algorithms in HPC Environment

Need for scalability of an algorithm is essential, when one wants to utilize HPC infrastructure in an efficient and reasonable way. In such infrastructures, synchronization affects the efficiency of the parallel algorithms. However, one can consider introducing certain means of desynchronization in order to increase scalability. Allowing for omitting or delaying certain messages, can be easily accepted in the case of metaheuristics. Furthermore, some simulations can also follow this pattern and handle bigger environments. The paper presents a short survey of desynchronization idea, pointing out already obtained results or sketching out the future work focused on scaling the parallel and distributed computing or simulation algorithms leveraging desynchronization

Deep Reinforcement Learning Approach for Lagrangian Control: Improving Freeway Bottleneck Throughput Via Variable Speed Limit

Connected vehicles (CVs) will enable new applications to improve traffic flow. The focus of this dissertation is to investigate how reinforcement learning (RL) control for the variable speed limit (VSL) through CVs can be generalized to improve traffic flow at different freeway bottlenecks. Three different bottlenecks are investigated: A sag curve, where the gradient changes from negative to positive values causes a reduction in the roadway capacity and congestion; a lane reduction, where three lanes merge to two lanes and cause congestion, and finally, an on-ramp, where increase in demand on a multilane freeway causes capacity drop. An RL algorithm is developed and implemented in a simulation environment for controlling a VSL in the upstream to manipulate the inflow of vehicles to the bottleneck on a freeway to minimize delays and increase the throughput. CVs are assumed to receive VSL messages through Infrastructure-to-Vehicle (I2V) communications technologies. Asynchronous Advantage Actor-Critic (A3C) algorithms are developed for each bottleneck to determine optimal VSL policies. Through these RL control algorithms, the speed of CVs are manipulated in the upstream of the bottleneck to avoid or minimize congestion. Various market penetration rates for CVs are considered in the simulations. It is demonstrated that the RL algorithm is able to adapt to stochastic arrivals of CVs and achieve significant improvements even at low market penetration rates of CVs, and the RL algorithm is able to find solution for all three bottlenecks. The results also show that the RL-based solutions outperform feedback-control-based solutions

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Model-based parallelization of discrete traffic simulation models

To re-establish regular operations in a tram traffic network after a large disturbance, e.g. resulting from vehicle breakdown or station closure, the viability of several rescheduling and rerouting strategies has to be evaluated prior to their implementation. Here, a multi-modal traffic simulation system can help to enhance the decision quality. Such a system obviously faces tight time constraints, so simulation data has to be acquired fast. In this paper we propose a method for the parallel execution of discrete traffic simulation models, which would accelerate data generation in comparison to a sequential model. To assess this method's dynamic behavior in real-world applications, some experiments conducted on a software system modeling schedule based tram traffic are presented. After giving an introduction to the scope and aim, we show some background on the parallelization of discrete simulation models. The main part of the paper begins with the proposal of a method to parallelize the execution of simulation models with problem specific properties. Some estimations of the method's efficiency are shared, followed by several experiments to highlight its dynamic behavior in real-world applications. The paper ends with a short summary and some thoughts on further research

Dynamical Systems on Networks: A Tutorial

We give a tutorial for the study of dynamical systems on networks. We focus especially on "simple" situations that are tractable analytically, because they can be very insightful and provide useful springboards for the study of more complicated scenarios. We briefly motivate why examining dynamical systems on networks is interesting and important, and we then give several fascinating examples and discuss some theoretical results. We also briefly discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give an outlook on the field.Comment: 39 pages, 1 figure, submitted, more examples and discussion than original version, some reorganization and also more pointers to interesting direction