Dynamic Network Simulation Research Articles

Large-scale dynamic transportation network simulation: A space-time-event parallel computing approach

This paper describes a computationally efficient parallel-computing framework for mesoscopic transportation simulation on large-scale networks. By introducing an overall data structure for mesoscopic dynamic transportation simulation, we discuss a set of implementation issues for enabling flexible parallel computing on a multi-core shared memory architecture. First, we embed an event-based simulation logic to implement a simplified kinematic wave model and reduce simulation overhead. Second, we present a space-time-event computing framework to decompose simulation steps to reduce communication overhead in parallel execution and an OpenMP-based space-time-processor implementation method that is used to automate task partition tasks. According to the spatial and temporal attributes, various types of simulation events are mapped to independent logical processes that can concurrently execute their procedures while maintaining good load balance. We propose a synchronous space-parallel simulation strategy to dynamically assign the logical processes to different threads. The proposed method is then applied to simulate large-scale, real-world networks to examine the computational efficiency under different numbers of CPU threads. Numerical experiments demonstrate that the implemented parallel computing algorithm can significantly improve the computational efficiency and it can reach up to a speedup of 10 on a workstation with 32 computing threads.

Partov: a network simulation and emulation tool

Network protocol design and evaluation requires either full implementation of the considered protocol and evaluation in a real network, or a simulation based on a model. There is also a middle approach in which both simulation and emulation are used to evaluate a protocol. In this article the Partov engine, which provides both simulation and emulation capabilities simultaneously, is presented. Partov benefits from a layered and platform-independent architecture. As a pure simulator, it provides an extensible plugin-based platform that can be configured to perform both real-time and non-real-time discrete-event simulations. It also acts as an emulator, making interaction with real networks possible in real time. Additionally, a declarative XML-based language is used, acting as a glue between simulation and emulation modules and plugins. It supports dynamic network modelling and simulation based on continuous time Markov chains. Partov is compared with other well-known tools such as NS-3 and real processes such as Hping3. It is shown that Partov requires less overhead and is much more scalable than NS-3.

Equation-free analysis of a dynamically evolving multigraph

In order to illustrate the adaptation of traditional continuum numerical techniques to the study of complex network systems, we use the equation-free framework to analyze a dynamically evolving multigraph. This approach is based on coupling short intervals of direct dynamic network simulation with appropriately-defined lifting and restriction operators, mapping the detailed network description to suitable macroscopic (coarse-grained) variables and back. This enables the acceleration of direct simulations through Coarse Projective Integration (CPI), as well as the identification of coarse stationary states via a Newton-GMRES method. We also demonstrate the use of data-mining, both linear (principal component analysis, PCA) and nonlinear (diffusion maps, DMAPS) to determine good macroscopic variables (observables) through which one can coarse-grain the model. These results suggest methods for decreasing simulation times of dynamic real-world systems such as epidemiological network models. Additionally, the data-mining techniques could be applied to a diverse class of problems to search for a succint, low-dimensional description of the system in a small number of variables.

Inter-domain optical path provisioning with blocking avoidance by intra-domain immediate reroute-based semi-proactive hierarchical PCE

Optical fiber networks have become the foundations of communication system to provide enormous transmission capacity with less cost. Connection blocking is an inherent attribute and influences optical networks' performance, especially in multi-domain network scenarios. We analyze the reason and effect of blockings including routing blockings and signaling blockings. We regard the cause of signaling blockings as the information asynchronization in resource reservation process. We define the concept of Information Asynchronization Period to describe the effect of signaling blockings. To reduce signaling blockings in end-to-end optical path provisioning for multi-domain scenarios, we propose a novel network routing and control scheme, named Intra-Domain Immediate Reroute based Semi-Proactive Hierarchical Path Computation Element (IDIRSP H-PCE). The proposed routing and control scheme mainly consists of two parts, Semi-Proactive routing and Intra-Domain Immediate Reroute mechanism. Dynamic network simulations verify our proposed scheme. We compare the network performance with Reactive Backward Recursive PCE-based Computation (BRPC) based PCE, Reactive H-PCE and Proactive H-PCE. Simulation results indicate that IDIRSP H-PCE can provide connection with a very low blocking probability in light load case, which is close to Proactive H-PCE, and obviously better than BRPC based Reactive PCE and Reactive H-PCE. For heavy load case, IDIRSP H-PCE has a remarkably lower blocking probability than other three methods. Moreover, we test our proposed routing and control scheme when facing link faults. Simulation results indicate that IDIRSP H-PCE can greatly improve the traffic access rate and optimize the network performance.

Inadvertent leaks: exploration via agent-based dynamic network simulation

A significant volume of information leaks in organizations are inadvertent, a form of information spillage. Because the leakage of information is driven by the complex interaction of technology, social, and behavioral factors, we use a hybrid agent-based and dynamic network model, Construct, to simulate the flow of sensitive information in knowledge-driven organizations. Because interaction patterns often change when an organization is under stress, we simulate stress to the organization with effect-based (reliability and integrity) crisis scenarios. Using a virtual experiment, we vary the crisis scenarios, organization's structure, IT connections, and pressure to separate personnel based on security ratings. Our experiment suggests that the organization's structure, IT connections, separation pressure, and typical performance all influence how much an organization suffers from inadvertent leakage. In evaluating how organizations respond to crisis, organizations with stove-piped IT tend to clamp down on leakage in response to the crisis, while organizations with Mesh IT tend to have more leakage. Integrity crises tend to decrease leakage; while reliability crises tend to increase leakage in organizations, especially those with Mesh-based IT.

Construct Demo Input Deck

Abstract : This technical report describes a demo input deck for Construct, the CASOS dynamic network simulation tool. It describes an example problem - understanding the difference in information diffusion patterns when bridging agents span two otherwise separate groups - and then how such a simulation would be simulated using Construct. In so doing, it describes the way in which data is supplied to Construct. Additionally, this report also describes how to run the simulation using the command line, and ways in which the simulation could be extended to analyze other problems.

Nonzero-Sum Relationships in Mitigating Urban Carbon Emissions: A Dynamic Network Simulation.

The "stove-pipe" way of thinking has been mostly used in mitigating carbon emissions and managing socioeconomics because of its convenience of implementation. However, systems-oriented approaches become imperative in pursuit of an efficient regulation of carbon emissions from systems as complicated as urban systems. The aim of this paper is to establish a dynamic network approach that is capable of assessing the effectiveness of carbon emissions mitigation in a more holistic way. A carbon metabolic network is constructed by modeling the carbon flows between economic sectors and environment. With the network shocked by interventions to the sectoral carbon flows, indirect emissions from the city are accounted for under certain carbon mitigation strategies. The nonzero-sum relationships between sectors and environmental components are identified based on utility analysis, which synthesize the nature of direct and indirect network interactions. The results of the case study of Beijing suggest that the stove-pipe mitigation strategies targeted the economic sectors might be not as efficient as they were expected. A direct cutting in material or energy import to the sectors may result in a rebound in indirect emissions and thus fails to achieve the carbon mitigation goal of the city as a whole. A promising way of foreseeing the dynamic mechanism of emissions is to analyze the nonzero-sum relationships between important urban components. Thinking cities as systems of interactions, the network approach is potentially a strong tool for appraising and filtering mitigation strategies of carbon emissions.

Computational systems toxicology: Emergence, development and application

系统生物学的发展为传统毒理学的研究提供了新机遇, 逐步形成了系统毒理学. 计算系统毒理学旨在整合毒理组学实验数据, 构建多水平、多尺度的预测模型, 定量评估化合物的安全性. 目前已经发展了静态网络分析预测、动态网络模拟和有害结局路径等几种研究方法. 虽起步不久, 但在全面理解毒理学机制、发现新的生物标志物及化合物安全性综合评估等方面已表现出良好的应用前景. 本文主要综述了计算系统毒理学的诞生背景、数据资源、研究方法、应用领域及未来展望.

An Approximation Method for Improving Dynamic Network Model Fitting

There has been a great deal of interest recently in the modeling and simulation of dynamic networks, that is, networks that change over time. One promising model is the separable temporal exponential-family random graph model (ERGM) of Krivitsky and Handcock, which treats the formation and dissolution of ties in parallel at each time step as independent ERGMs. However, the computational cost of fitting these models can be substantial, particularly for large, sparse networks. Fitting cross-sectional models for observations of a network at a single point in time, while still a nonnegligible computational burden, is much easier. This article examines model fitting when the available data consist of independent measures of cross-sectional network structure and the duration of relationships under the assumption of stationarity. We introduce a simple approximation to the dynamic parameters for sparse networks with relationships of moderate or long duration and show that the approximation method works best in precisely those cases where parameter estimation is most likely to fail—networks with very little change at each time step. We consider a variety of cases: Bernoulli formation and dissolution of ties, independent-tie formation and Bernoulli dissolution, independent-tie formation and dissolution, and dependent-tie formation models.

Construction, visualization, and analysis of biological network models in Dynetica

Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user‐friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy‐to‐use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile “expression variable” entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time‐course simulations. Additionally, Dynetica 2.0 provides built‐in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the Systems Biology Markup Language (SBML) format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers.

Distributed extremum-seeking control over networks of unstable dynamic agents

In this paper, a distributed extremum seeking control technique is proposed to solve a class of real-time optimization problems over a network of dynamic agents with unknown unstable dynamics. Each dynamic agent measures a cost that is shared over a network. A dynamic average consensus approach is used to provide each agent with an estimate of the total network cost. The extremum seeking controller uses the local estimate of the total cost to adjust the value of the local decision variables. The contribution of the proposed technique is the simultaneous stabilization of the network dynamics and the distributed optimization of the total network cost. A dynamic network simulation example is presented to demonstrate the effectiveness of the technique.

Self‐organizing femtocell offloading at the flow level

SUMMARYFemtocell technology is expected to be fully self‐managed, empowered by self‐organizing network functionalities. This paper proposes a solution for self‐optimized offloading of macrocell traffic towards open/hybrid‐access femtocells. A heterogeneous network comprising macro‐ and femtocells is modeled as parallel queues. The coverage area of the femtocells is self‐optimized by dynamically adapting their pilot powers. A simple update equation for the pilot power is given and its convergence is studied using stochastic approximation techniques. The algorithm balances the load among the cells to improve network capacity. Simulation results illustrate the important performance gains brought about by the proposed scheme, using a dynamic network simulator. Copyright © 2013 John Wiley & Sons, Ltd.

Largenet2: an object-oriented programming library for simulating large adaptive networks

The largenet2 C++ library provides an infrastructure for the simulation of large dynamic and adaptive networks with discrete node and link states. The library is released as free software. It is available at http://biond.github.com/largenet2. Largenet2 is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License. gerd@biond.org

A Dynamic Pore Network Model for Oil Displacement by Wettability-Altering Surfactant Solution

A dynamic pore network model, capable of predicting the displacement of oil from a porous medium by a wettability-altering and interfacial tension reducing surfactant solution, is presented. The key ingredients of the model are (1) a dynamic network model for the displacement of oil by aqueous phase taking account of capillary and viscous effects, (2) a simulation of the transport of surfactant through the network by advection and diffusion taking account of adsorption on the solid surface, and (3) the coupling of these two by linking the contact angle and interfacial tension appearing in the dynamic network simulation to the local concentration of surfactant computed in the transport simulation. The coupling is two-way: The flow field used to advect the surfactant concentration is that associated with the displacement of oil by the injected aqueous phase, and the surfactant concentration influences the flow field through its effect on the capillarity parameters. We present results obtained using the model to validate that it reproduces the displacement patterns observed by other authors in two-dimensional networks as capillary number and mobility ratio are varied, and to illustrate the effects of surfactant on displacement patterns. A mechanism is demonstrated whereby in an initially mixed-wet medium, surfactant-induced wettability alteration can lead to stabilization of displacement fronts.

Efficient Transient Simulation of Non-linear Dynamic Networks with Discontinuous Forcing

AbstractNew technologies produce highly complex hybrid systems containing analogue components next to digital hardware and software. Simulating those mixed-signal systems exhibits discontinuities in signals due to the nature of the digital components. In this paper, an efficient and accurate method for the transient analysis of non-linear dynamic systems is presented that can handle discontinuous forcing functions which may occur as prescribed voltages or forces, for instance. It is based on a transformation of a model's equation system by substituting the time for a new independent variable. This approach facilitates suitable system-level models that altogether maintain the performance that is needed for an overall system-level simulation of complex application scenarios.

Performance of Mobile MIMO OFDM Systems With Application to UTRAN LTE Downlink

This paper analyzes the performance of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technology through the ergodic capacity. The capacity is represented based on the mean of the effective signal-to-interference-and-noise ratio (SINR) values. The asymptotic distribution of the effective SINR in the case of independent and m-dependent quality measures, i.e., post-processed SINRs, is established analytically. The asymptotic distribution is applicable for the size of post-processed SINRs of at least 1000 samples or more and m = 10. The mean value of the effective SINR based on the moment generating function of post-processed SINR is derived. Additionally, the impact of mobility on the system performance is characterized through the ergodic capacity and a system level factor which measures user mobility. The performance of MIMO OFDM system is validated by fully dynamic 3GPP Long Term Evolution network simulations in downlink under realistic mobility scenarios with low (3 km/h), medium (30 km/h) and high (120 km/h) user speeds. The simulations verify that the derived asymptotic distributions of the effective SINR for the independent and m-dependent cases are very accurate in all mobility scenarios. The simulated ergodic capacity show clear loss in the average MIMO spectral efficiency especially in high mobility scenarios.

Self‐optimization of random access channel in 3rd Generation Partnership Project Long Term Evolution

ABSTRACTThe long term evolution (LTE) is being designed to enhance third generation radio access technologies, and it represents a major step towards high‐performance mobile data networks that are fully optimized for packet‐switched connections. In an initial access, the LTE user equipment executes the random access (RA) procedure while searching for serving base station and initiating services. For a good cell coverage to be obtained, feasible uplink data rate and low delays in, for example, call setup, the optimal random access channel (RACH) performance is essential. Although manual tuning of procedures can be very costly, it is important to design self‐organizing network (SON) algorithms that optimize procedures such as RA. In this paper, a mathematical framework for the RACH modeling in LTE technology is presented on the basis of probability theory and statistics. A self‐optimization algorithm for RACH preamble sequence allocation and preamble split is then examined using the deduced mathematical framework and system‐level simulations. Performance analysis is semi‐analytical: First, RACH performance results are produced using analytical model, and second, outputs are fed into a dynamic LTE network simulator. Results show that the proposed optimization approach clearly improves uplink performance and ensures a certain success rate at the first RA attempt so that shorter handover durations and call setup delays can be achieved. Copyright © 2011 John Wiley & Sons, Ltd.

Dynamic Simulations of Pathways Downstream of ERBB-Family, Including Mutations and Treatments: Concordance with Experimental Results

The pathways downstream of ErbB-family proteins are very important in BC, especially when considering treatment with onco-protein inhibitors. We studied and implemented dynamic simulations of four downstream pathways and described the fragment of the signaling network we evaluated as a Molecular Interaction Map. Our simulations, enacted using Ordinary Differential Equations, involved 242 modified species and complexes, 279 reversible reactions and 111 catalytic reactions. Mutations within a single pathway tended to be mutually exclusive; only inhibitors acting at, or downstream (not upstream), of a given mutation were active. A double alteration along two distinct pathways required the inhibition of both pathways. We started an analysis of sensitivity/robustness of our network, and we systematically introduced several individual fluctuations of total concentrations of independent molecular species. Only very few cases showed significant sensitivity. We transduced the ErbB2 over-expressing BC line, BT474, with the HRAS (V12) mutant, then treated it with ErbB-family and phosphorylated MEK (MEKPP) inhibitors, Lapatinib and U0126, respectively. Experimental and simulation results were highly concordant, showing statistical significance for both pathways and for two respective endpoints, i.e. phosphorylated active forms of ERK and Akt, p one tailed = .0072 and = .0022, respectively. Working with a complex 39 basic species signaling network region, this technology facilitates both comprehension and effective, efficient and accurate modeling and data interpretation. Dynamic network simulations we performed proved to be both practical and valuable for a posteriori comprehension of biological networks and signaling, thereby greatly facilitating handling, and thus complete exploitation, of biological data.

DNP을 이용한 플랜트의 강인 안정화 기법

본 논문에서는 외란이나 시스템의 파라미터 변동 및 불확실성 등이 존재하는 자동화 설비시스템을 강인하고 정밀하게 제어할 수 있도록 하기 위해 동적 신경망 처리기(DNP)인 신경망 제어기를 설계하였다. 자동화 설비시스템에서 부품의 조립, 가공 등 복잡하고 정교한 임무를 수행시키기 위해서는 end-effector의 이동경로 궤적에 대한 추적제어 뿐만 아니라 목표물에 대하여 접촉하는 힘의 궤적에 대한 추적 제어가 필수적이다. 또한 자동화 설비시스템에서 플랜트의 역기구학적인 좌표변환을 계산하기 위한 학습구조를 개발하였으며, DNP가 이용될 수 있는 예를 설명하였다. 제안된 동적 신경망인 DNP의 구조와 학습 알고리즘을 제시하고 컴퓨터 모의실험을 통해 학습 성능을 증명하였다. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the Plants of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

Description and simulation of dynamic mobility networks

During the last decade, the study of large scale complex networks has attracted a substantial amount of attention and works from several domains: sociology, biology, computer science, epidemiology. Most of such complex networks are inherently dynamic, with new vertices and links appearing while some old ones disappear. Until recently, the dynamics of these networks was less studied and there is a strong need for dynamic network models in order to sustain protocol performance evaluations and fundamental analyzes in all the research domains listed above. We propose in this paper a novel framework for the study of dynamic mobility networks. We address the characterization of dynamics by proposing an in-depth description and analysis of two real-world data sets. We show in particular that links creation and deletion processes are independent of other graph properties and that such networks exhibit a large number of possible configurations, from sparse to dense. From those observations, we propose simple yet very accurate models that allow generate random mobility graphs with similar temporal behavior as the one observed in experimental data.