Showing papers on "Network traffic simulation published in 2008"

An overview of the OMNeT++ simulation environment

Abstract: The OMNeT++ discrete event simulation environment has been publicly available since 1997. It has been created with the simulation of communication networks, multiprocessors and other distributed systems in mind as application area, but instead of building a specialized simulator, OMNeT++ was designed to be as general as possible. Since then, the idea has proven to work, and OMNeT++ has been used in numerous domains from queuing network simulations to wireless and ad-hoc network simulations, from business process simulation to peer-to-peer network, optical switch and storage area network simulations. This paper presents an overview of the OMNeT++ framework, recent challenges brought about by the growing amount and complexity of third party simulation models, and the solutions we introduce in the next major revision of the simulation framework.

TraCI: an interface for coupling road traffic and network simulators

Abstract: Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to asses the usability of vehicular applications. In this article, we present Traffic Control Interface (TraCI) a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns.In contrast to the existing approaches, i.e., generating mobility traces that are fed to a network simulator as static input files, the online coupling allows the adaptation of drivers' behavior during simulation runtime. This technique is not limited to a special traffic simulator or to a special network simulator. We introduce a general framework for controlling the mobility which is adaptable towards other research areas.We describe the basic concept, design decisions and the message format of this open-source architecture. Additionally, we provide implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.

A comparative analysis of web and peer-to-peer traffic

Abstract: Peer-to-Peer (P2P) applications continue to grow in popularity, and have reportedly overtaken Web applications as the single largest contributor to Internet traffic. Using traces collected from a large edge network, we conduct an extensive analysis of P2P traffic, compare P2P traffic with Web traffic, and discuss the implications of increased P2P traffic. In addition to studying the aggregate P2P traffic, we also analyze and compare the two main constituents of P2P traffic in our data, namely BitTorrent and Gnutella. The results presented in the paper may be used for generating synthetic workloads, gaining insights into the functioning of P2P applications, and developing network management strategies. For example, our results suggest that new models are necessary for Internet traffic. As a first step, we present flow-level distributional models for Web and P2P traffic that may be used in network simulation and emulation experiments.

Urban traffic congestion propagation and bottleneck identification

Abstract: Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model (CTM). The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity (AJV) of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow (attributed to different OD pairs) component, and network traffic demand.

Classification of Network Traffic via Packet-Level Hidden Markov Models

Abstract: Traffic classification and identification is a fertile research area. Beyond Quality of Service, service differentiation, and billing, one of the most important applications of traffic classification is in the field of network security. This paper proposes a packet-level traffic classification approach based on Hidden Markov Model (HMM). Classification is performed by using real network traffic and estimating - in a combined fashion - Packet Size (PS) and Inter Packet Time (IPT) characteristics, thus remaining applicable to encrypted traffic too. The effectiveness of the proposed approach is evaluated by considering several traffic typologies: we applied our model to real traffic traces of Age of Mythology and Counter Strike (two Multi Player Network Games), HTTP, SMTP, Edonkey, PPlive (a peer-to-peer IPTV application), and MSN Messenger. An analytical basis and the mathematical details regarding the model are given. Results show how the proposed approach is able to classify network traffic by using packet-level statistical properties and therefore it is a good candidate as a component for a multi-classification framework.

Near real-time traffic routing

Abstract: A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

On the need for bidirectional coupling of road traffic microsimulation and network simulation

Abstract: Simulation of network protocol behavior in Vehicular Ad Hoc Network (VANET) scenarios is strongly demanded for evaluating the applicability of developed network protocols. In this work, we discuss the need for bidirectional coupling of network simulation and road traffic microsimulation for evaluating such protocols. The implemented mobility model, which defines all movement of nodes, influences the outcome of simulations to a great deal. Therefore, the use of a representative mobility model is essential for producing meaningful results. Based on these observations, we developed the hybrid simulation framework Veins (Vehicles in Network Simulation), composed of the network simulator OMNeT++ and the road traffic simulator SUMO. Based on a proof-of-concept study, we demonstrate the advantages and the need for bidirectionally coupled simulation.

Large-scale network parameter configuration using an on-line simulation framework

Abstract: As the Internet infrastructure grows to support a variety of services, its legacy protocols are being overloaded with new functions such as traffic engineering. Today, operators engineer such capabilities through clever, but manual parameter tuning. In this paper, we propose a back-end support tool for large-scale parameter configuration that is based on efficient parameter state space search techniques and on-line simulation. The framework is useful when the network protocol performance is sensitive to its parameter settings, and its performance can be reasonably modeled in simulation. In particular, our system imports the network topology, relevant protocol models and latest monitored traffic patterns into a simulation that runs on-line in a network operations center (NOC). Each simulation evaluates the network performance for a particular setting of protocol parameters. We propose an efficient large-dimensional parameter state space search technique called ldquorecursive random search (RRS).rdquo Each sample point chosen by RRS results in a single simulation. An important feature of this framework is its flexibility: it allows arbitrary choices in terms of the simulation engines used (e.g., ns-2, SSFnet), network protocols to be simulated (e.g., OSPF, BGP), and in the specification of the optimization objectives. We demonstrate the flexibility and relevance of this framework in three scenarios: joint tuning of the RED buffer management parameters at multiple bottlenecks, traffic engineering using OSPF link weight tuning, and outbound load-balancing of traffic at peering/transit points using BGP LOCAL_PREF parameter.

VANET Simulation Environment with Feedback Loop and its Application to Traffic Light Assistance

Abstract: Traffic applications, in which vehicles are equipped with a radio interface and communicate directly with each other and the road traffic infrastructure are a promising field for ad-hoc network technology. Vehicular applications reach from entertainment to traffic information systems, including safety aspects where warning messages can inform drivers about dangerous situations in advance. As performance tests of the real system are very expensive and not comprehensive, today's evaluations are based on analysis and simulation via traffic simulators. In order to investigate the impact of traffic information systems there are two options: First, traffic simulators can be extended by application code and a simplified model for wireless communication. Second, existing network simulators can be coupled with existing traffic simulators. We favor the coupling of existing and well known simulators as we believe that the wireless communication characteristics influence the data transfer significantly and an oversimplified transmission model can lead to flawed results. In this paper we describe the feedback loop between traffic and network simulators named traffic control interface (TraCI) and outline its versatility. We explain its use to determine possible energy consumption reduction when traffic lights send their phase schedules to vehicles.

Spatiotemporal traffic-flow dependency and short-term traffic forecasting

Abstract: Short-term traffic forecasting is playing an increasing role in modern transport management. Although many short-term traffic forecasting methods have been explored, the spatiotemporal dependency of traffic flow, an important characteristic of traffic dynamics that can benefit the forecasting of traffic changes, is often neglected in short-term traffic forecasting. This paper first investigates the spatiotemporal dependency of traffic flow using cross-correlation analysis and then discusses its implications in terms of traffic forecastability and real-time data effectiveness. This can help us to understand traffic flow, and hence improve the performance of forecasting models.

Model predictive control in urban traffic network management

Abstract: The paper investigates a model predictive control (MPC) strategy specialized in urban traffic management in order to relieve traffic congestion, reduce travel time and improve homogenous traffic flow. Over the theory the realization of the control method is also presented. To validate the effectiveness of the controller a busy traffic network was chosen for test field. The MPC strategy was implemented into the test network control system (hardware in loop simulation). The applied environment is a microscopic traffic simulator with mathematical software and proper computational applications for the evaluation. The simulation results prove the effectiveness of the designed MPC based traffic control strategy. The system is able to improve the network efficiency and reduce travel time, creating optimal flow in the network subjected to control input constraints.

Adaptively applying network acceleration services with an intermediate network device

Abstract: In general, techniques are described for adaptively applying network acceleration services within an intermediate network device. In particular, the intermediate network device comprises a classifier module that receives network traffic and a servicing engine. The servicing engine applies the network acceleration services to the network traffic in accordance with a service configuration and monitors the application of network acceleration services to determine whether the application of each of the network acceleration services improves the efficiency with which the network traffic is serviced. The servicing engine then dynamically adapts the service configuration to alter the application of the network acceleration services based on the determination. Thereafter, the servicing engine applies the network acceleration services to the network traffic in accordance with the dynamically adapted service configuration to more efficiently service the network traffic.

Characterizing the traffic distribution in linear wireless sensor networks

Abstract: In a multi-hop wireless sensor network (WSN), the traffic load is not evenly distributed over the nodes. For example, the sensors which are one hop away from the sink relay the whole network traffic. This imbalanced traffic distribution can degrade the network lifetime and functionality. Here, an analysis is proposed to characterize the traffic load distribution over a randomly deployed linear WSN. The effect of the number of nodes and their distribution over the network is taken into account and the results are verified through computer simulation.

Adaptive multi-topology IGP based traffic engineering with near-optimal network performance

Abstract: In this paper we present an intelligent multi-topology IGP (MT-IGP) based intra-domain traffic engineering (TE) scheme that is able to handle unexpected traffic fluctuations with near-optimal network performance. First of all, the network is dimensioned through offline link weight optimization using Multi-Topology IGPs for achieving maximum path diversity across multiple routing topologies. Based on this optimized MT-IGP configuration, an adaptive traffic engineering algorithm performs dynamic traffic splitting adjustment for balancing the load across multiple routing topologies in reaction to the monitored traffic dynamics. Such an approach is able to efficiently minimize the occurrence of network congestion without the necessity of frequently changing IGP link weights that may cause transient forwarding loops and routing instability. Our experiments based on real network topologies and traffic matrices show that our approach has a high chance of achieving near-optimal network performance with only a small number of routing topologies.

Synchronized network emulation: matching prototypes with complex simulations

Abstract: Network emulation, in which real systems interact with a network simulation, is a common evaluation method in computer networking research. Until now, the simulation in charge of representing the network has been required to be real-time capable, as otherwise a time drift between the simulation and the real network devices may occur and corrupt the results. In this paper, we present our work on synchronized network emulation. By adding a central synchronization entity and by virtualizing real systems for means of control, we can build-up network emulations which contain both unmodified x86 systems and network simulations of any complexity.

The automatic programming of simulations

Abstract: The effective extension of simulation techniques demands the use of computers and the provision of automatic coding aids. This paper outlines a programme of work which has contributed to this extension by devising a simulation language which can be subjected to automatic coding by a Ferranti ‘Pegasus’ Computer. A master program has been written which executes the coded statements within a common structure which applies to all simulation experiments. The structure depends on representing the plant as a collection of machines in various states. A change of state of any machine is an ‘event’, and the program predicts successive events and changes the plant state accordingly. The predictions are based on a series of logical rules concerning the necessary states of machines for processes to commence, and uses sampling techniques to determine their conclusion. The simulation language is designed for describing the logical rules. Experience so far with this scheme indicates a substantial saving of time in writing and testing computer simulations, with no severe loss of running speed. It has been found flexible enough to deal with a wide range of different problems.

Optimal traffic networks topology: A complex networks perspective

Abstract: We investigate and analyse an optimal traffic network structure for resisting traffic congestion with different volumes of traffic. For this aim, we introduce a cost function and user-equilibrium assignment (UE) which ensures the flow balance on traffic systems. Our finding is that an optimal network is strongly dependent on the total system flow. And the random network is most desirable when the system flow is small. But for the larger volume of traffic, the network with power-law degree distribution is the optimal one. Further study indicates, for scale-free networks, that the degree distribution exponent has large effects on the congestion of traffic network. Therefore, the volume of traffic and characteristic of network determine the optimal network structure so as to minimize the side-effect produced by traffic congestion.

Parallel Vehicular Traffic Simulation using Reverse Computation-based Optimistic Execution

Abstract: Vehicular traffic simulations are useful in applications such as emergency management and homeland security planning tools. High speed of traffic simulations translates directly to speed of response and level of resilience in those applications. Here, a parallel traffic simulation approach is presented that is aimed at reducing the time for simulating emergency vehicular traffic scenarios. Three unique aspects of this effort are: (1) exploration of optimistic simulation applied to vehicular traffic simulation (2) addressing reverse computation challenges specific to optimistic vehicular traffic simulation (3) achieving absolute (as opposed to self- relative) speedup with a sequential speed equal to that of a fast, de facto standard sequential simulator for emergency traffic. The design and development of the parallel simulation system is presented, along with a performance study that demonstrates excellent sequential performance as well as parallel performance.

Research of Traffic Flow Forecasting Based on Neural Network

Abstract: Intelligent transportation system (ITS) is an effective measure to solve the problem of traffic jam. Accurate real-time predication of traffic flow is the key technology of ITS. In this paper a dynamic traffic flow forecasting model based on neural network is proposed. BP and RBF neural network are used to build the forecasting models. The data pre-handle method and the judgment criterion of the forecasting model are given. Simulation shows the traffic flow forecasting method is effective, and the RBF can be more fast and effective in forecasting the traffic flow by simulation analysis.

System and method for network device configuration

Abstract: Methods and systems for identifying configuration parameters for a network device are provided. The method includes generating a stream of traffic data, where the traffic data has a known characteristic. Then, applying the stream of traffic data to the network device, where the network device has a specific type, and the network device generates an output based on the traffic data. The method then includes monitoring performance of the network device while the traffic data is processed by the network device, and the monitoring is configured to generate monitoring data for the traffic data applied to the network device having the specific type. Also, the method includes analyzing the output from the network device, where the analyzing is performed to identify how the traffic data was handled by the network device, and the analyzing is configured to generate performance metrics. The method further includes saving the monitoring data and the performance metrics to a knowledge database. The knowledge database is capable of being accessed to enable configuration of other network devices based in part on the monitoring data and performance metrics.

Fuzzy-logic-based traffic incident detection algorithm for freeway

Abstract: Automatic incident detection is an important portion of advanced traffic management systems. Based on real-time analysis of traffic parameters, automatic traffic incident detection transfers information to traffic managers, aimed at reducing traffic jam and lessening traffic accident. Based on fuzzy logic, a new traffic incident detection algorithm for freeway was provided. With traffic simulation, the parameters of the algorithm were calibrated. Finally, the algorithm was validated by the data from traffic simulation. Results showed that performance of the algorithm was encouraging.

Traffic analysis at short time-scales: an empirical case study from a 3G cellular network

Abstract: The availability of synchronized packet-level traces captured at different links allows the extraction of one-way delays for the network section in between. Delay statistics can be used as quality indicators to validate the health of the network and to detect global performance drifts and/or localized problems. Since packet delays depend not only on the network status but also on the arriving traffic rate, the delay analysis must be coupled with the analysis of the traffic patterns at short time scales. In this work we report on the traffic and delay patterns observed at short timescales in a 3G cellular mobile network. We show that the aggregate traffic rate exhibits large impulses and investigate on their causes. Specifically, we find that high- rate sequential scanners represent a common source of traffic impulses, and identify the potential consequences of such traffic onto the underlying network. This case-study demonstrates that the microscopic analysis of delay and traffic patterns at short time-scales can contribute effectively to the task of troubleshooting IP networks. This is particularly important in the context of 3G cellular networks given their complexity and relatively recent deployment.

Wireless Sensor Network-based system for measuring and monitoring road traffic

Abstract: Nowadays, the measure and classification of vehicles in road traffic is accomplished by inductive loops placed under the pavement. These inductive loops allow monitoring vehicle passing by means of different configurations which provide us a number of data in order to control several parameters of the traffic (vehicle speed, traffic congestion and traffic accidents, between others). The major objective of this paper is to analysis an alternative to magnetic loops: the Wireless Sensor Networks. Firstly, a state of the art about road traffic control is described. Secondly, an alternative system based on Wireless Sensor Networks is analyzed. Network architecture for this WSN will be specified. It is not a trivial task because of the hard constraints of the small devices which compose the WSNs. In previous papers [1], we have proposed a methodology that facilitates the WSNs design for supporting real time applications such as traffic control applications. This design methodology has been used in order to obtain a WSN that reaches the real time requirements of a monitoring traffic application for intelligent roads. In the short term, the aim is to define a simulation model based on the designed WSN. To conclude, we have introduced a section about possible future directions in the smart roads field.

Neural Network Based Traffic Prediction for Wireless Data Networks

Abstract: In a wireless network environment accurate and timely estimation or prediction of network traffic has gained much importance in the recent past The network applications use traffic prediction results to maintain its performance by adopting its behaviors Network Service provider will use the prediction values in ensuring the better Quality of Service(QoS) to the network users by admission control and load balancing by inter or intra network handovers This paper presents modeling and prediction of wireless network traffic Here traffic is modeled as nonlinear and non-stationary time series The nonlinear and non-stationary time series traffic is predicted using neural network and statistical methods The results of both the methods are compared on different time scales or time granularity The Neural Network (NN) architectures used in this study are Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN)The statistical model used here in this work is Fractional Auto Regressive Integ

Compiling network traffic into rules using soft computing methods for the detection of flooding attacks

Abstract: The ability to dynamically collect and analyze network traffic and to accurately report the current network status is critical in the face of large-scale intrusions, and enables networks to continually function despite of traffic fluctuations. The paper presents a network traffic model that represents a specific network pattern and a methodology that compiles the network traffic into a set of rules using soft computing methods. This methodology based upon the network traffic model can be used to detect large-scale flooding attacks, for example, a distributed denial-of-service (DDoS) attack. We report experimental results that demonstrate the distinctive and predictive patterns of flooding attacks in simulated network settings, and show the potential of soft computing methods for the successful detection of large-scale flooding attacks.

Optimal resource allocation for efficient transport on complex networks

Abstract: The problem of efficient transport on a complex network is studied in this paper. We find that there exists an optimal way to allocate resources for information processing on each node to achieve the best transport capacity of the network, or the largest input information rate which does not cause jamming in network traffic, provided that the network structure and routing strategy are given. More interestingly, this achievable network capacity limit is closely related to the topological structure of the network, and is actually inversely proportional to the average distance of the network, measured according to the same routing rule.

What Do Different Traffic Flow Models Mean for System-Optimal Dynamic Traffic Assignment in a Many-to-One Network?

Abstract: A variety of macroscopic traffic flow models have been applied to formulate the link-based system optimal dynamic traffic assignment (SO-DTA) problem in a many-to-one network. It is expected that SO-DTA models based on various traffic flow models usually result in different optimal link traffic evolution patterns, but whether and how traffic flow models affect the minimal system cost and the corresponding optimal arrival pattern at the destination is unclear. Three traffic flow models—point-queue, spatial-queue, and cell-transmission—are examined, and their resulted minimal system cost in many-to-one networks is compared. It is proved that for three simple networks—a linear network with two sequential bottlenecks, a two-in-one-out merge network, and a one-in-two-out diverge network—the minimal system costs for the SO-DTA models based on these three traffic flow models are identical. Numerical experiments show that this property appears to be held for general many-to-one networks. The reason may be that al...

Agents for Traffic Simulation

Abstract: Vehicular traffic is a classical example of a multi-agent system in which autonomous drivers operate in a shared environment. The article provides an overview of the state-of-the-art in microscopic traffic modeling and the implications for simulation techniques. We focus on the short-time dynamics of car-following models which describe continuous feedback control tasks (acceleration and braking) and models for discrete-choice tasks as a response to the surrounding traffic. The driving style of an agent is characterized by model parameters such as reaction time, desired speed, desired time gap, anticipation etc. In addition, internal state variables corresponding to the agent's "mind" are used to incorporate the driving experiences. We introduce a time-dependency of some parameters to describe the frustration of drivers being in a traffic jam for a while. Furthermore, the driver's behavior is externally influenced by the neighboring vehicles and also by environmental input such as limited motorization and braking power, visibility conditions and road traffic regulations. A general approach for dealing with discrete decision problems in the context of vehicular traffic is introduced and applied to mandatory and discretionary lane changes. Furthermore, we consider the decision process whether to brake or not when approaching a traffic light turning from green to amber. Another aspect of vehicular traffic is related to the heterogeneity of drivers. We discuss a hybrid system of coupled vehicle and information flow which can be used for developing and testing applications of upcoming inter-vehicle communication techniques.