Showing papers on "Network traffic simulation published in 2010"

ElasticTree: saving energy in data center networks

Abstract: Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network's ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree, a network-wide power1 manager, which dynamically adjusts the set of active network elements -- links and switches--to satisfy changing data center traffic loads.We first compare multiple strategies for finding minimum-power network subsets across a range of traffic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production e-commerce website. Our results demonstrate that for data center workloads, ElasticTree can save up to 50% of network energy, while maintaining the ability to handle traffic surges. Our fast heuristic for computing network subsets enables ElasticTree to scale to data centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill.

Modeling and Tools for Network Simulation

Abstract: A crucial step during the design and engineering of communication systems is the estimation of their performance and behavior; especially for mathematically complex or highly dynamic systems network simulation is particularly useful. This book focuses on tools, modeling principles and state-of-the art models for discrete-event based network simulations, the standard method applied today in academia and industry for performance evaluation of new network designs and architectures. The focus of the tools part is on two distinct simulations engines: OmNet++ and ns-3, while it also deals with issues like parallelization, software integration and hardware simulations. The parts dealing with modeling and models for network simulations are split into a wireless section and a section dealing with higher layers. The wireless section covers all essential modeling principles for dealing with physical layer, link layer and wireless channel behavior. In addition, detailed models for prominent wireless systems like IEEE 802.11 and IEEE 802.16 are presented. In the part on higher layers, classical modeling approaches for the network layer, the transport layer and the application layer are presented in addition to modeling approaches for peer-to-peer networks and topologies of networks. The modeling parts are accompanied with catalogues of model implementations for a large set of different simulation engines. The book is aimed at master students and PhD students of computer science and electrical engineering as well as at researchers and practitioners from academia and industry that are dealing with network simulation at any layer of the protocol stack.

Fundamentals of traffic simulation

Abstract: Models, Traffic Models, Simulation, and Traffic Simulation.- Microscopic Traffic Flow Simulator VISSIM.- Traffic Simulation with AVENUE.- Traffic Simulation with Paramics.- Traffic Simulation with Aimsun.- Traffic Simulation with MITSIMLab.- Traffic Simulation with SUMO - Simulation of Urban Mobility.- Traffic Simulation with DRACULA.- Traffic Simulation with Dynameq.- Traffic Simulation with DynaMIT.- Traffic Simulation with METANET.

GreenTE: Power-aware traffic engineering

Abstract: Current network infrastructures exhibit poor power efficiency, running network devices at full capacity all the time regardless of the traffic demand and distribution over the network. Most research on router power management are at component level or link level, treating routers as isolated devices. A complementary approach is to facilitate power management at network level by routing traffic through different paths to adjust the workload on individual routers or links. Given the high path redundancy and low link utilization in today's large networks, this approach can potentially allow more network devices or components to go into power saving mode. This paper proposes an intra-domain traffic engineering mechanism, GreenTE, which maximizes the number of links that can be put into sleep under given performance constraints such as link utilization and packet delay. Using network topologies and traffic data from several wide-area networks, our evaluation shows that GreenTE can reduce line-cards' power consumption by 27% to 42% under constraints that the maximum link utilization is below 50% and the network diameter remains the same as in shortest path routing.

An SVM-based machine learning method for accurate internet traffic classification

Abstract: Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven to be ineffective in terms of dynamic port allocation and packet encapsulation. The signature matching methods, on the other hand, require a known signature set and processing of packet payload, can only handle the signatures of a limited number of IP packets in real-time. A machine learning method based on SVM (supporting vector machine) is proposed in this paper for accurate Internet traffic classification. The method classifies the Internet traffic into broad application categories according to the network flow parameters obtained from the packet headers. An optimized feature set is obtained via multiple classifier selection methods. Experimental results using traffic from campus backbone show that an accuracy of 99.42% is achieved with the regular biased training and testing samples. An accuracy of 97.17% is achieved when un-biased training and testing samples are used with the same feature set. Furthermore, as all the feature parameters are computable from the packet headers, the proposed method is also applicable to encrypted network traffic.

Active traffic management on road networks: a macroscopic approach

Abstract: Active traffic management (ATM) is the ability to dynamically manage recurrent and non-recurrent congestion based on prevailing traffic conditions in order to maximize the effectiveness and efficiency of road networks. It is a continuous process of (i) obtaining and analysing traffic measurement data, (ii) operations planning, i.e. simulating various scenarios and control strategies, (iii) implementing the most promising control strategies in the field, and (iv) maintaining a real-time decision support system that filters current traffic measurements to predict the traffic state in the near future, and to suggest the best available control strategy for the predicted situation. ATM relies on a fast and trusted traffic simulator for the rapid quantitative assessment of a large number of control strategies for the road network under various scenarios, in a matter of minutes. The open-source macrosimulation tool Aurora ROAD NETWORK MODELER is a good candidate for this purpose. The paper describes the underlying dynamical traffic model and what it takes to prepare the model for simulation; covers the traffic performance measures and evaluation of scenarios as part of operations planning; introduces the framework within which the control strategies are modelled and evaluated; and presents the algorithm for real-time traffic state estimation and short-term prediction.

A Novel Fuzzy Model and Control of Single Intersection at Urban Traffic Network

Abstract: This paper presents a novel fuzzy model and a fuzzy logic controller for an isolated signalized intersection. The controller controls the traffic light timings and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Usually fuzzy traffic controllers are optimized to maximize traffic flows/minimize traffic waiting time under typical traffic conditions. Consequentially, these are not the optimal traffic controllers under exceptional traffic cases such as roadblocks and road accidents. We apply state-space equations to formulate the average waiting time vehicles in traffic network at fixed time control. also, We propose a novel fuzzy model and new fuzzy traffic controller that can optimally control traffic flows under both normal and exceptional traffic conditions. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional fuzzy traffic controllers under normal and abnormal traffic conditions.

Continuum Traffic Simulation

Abstract: We present a novel method for the synthesis and animation of realistic traffic flows on large-scale road networks. Our technique is based on a continuum model of traffic flow we extend to correctly handle lane changes and merges, as well as traffic behaviors due to changes in speed limit. We demonstrate how our method can be applied to the animation of many vehicles in a large-scale traffic network at interactive rates and show that our method can simulate believable traffic flows on publicly-available, real-world road data. We furthermore demonstrate the scalability of this technique on many-core systems.

Models, Traffic Models, Simulation, and Traffic Simulation

Abstract: This introductory chapter to a book on traffic simulation fundamentals is aimed at setting up a comprehensive framework for simulation as a well-established and grounded OR technique and its specificities when applied to traffic systems; the main approaches to traffic simulation and the principles of traffic simulation model building; the fundamentals of traffic flow theory and its application to traffic simulation from macroscopic, mesoscopic, or microscopic approaches. The chapter also provides a basic overview on the principles of dynamic traffic assignment and its application to traffic simulation and the calibration and validation of traffic simulation models, two key topics to establish the validity and credibility for traffic simulation models being used in the decision-making processes.

Traffic classification using probabilistic neural networks

Abstract: Traffic classification, a branch of passive network measurement, becomes more and more important for network management. As traditional traffic classification techniques like port-based and payload-based techniques become ineffective for complicated internet applications which use dynamic port number and encryption techniques to avoid detection, machine learning based techniques gained more and more attentions in the past few years. But there are few studies that focus on applying neural computation techniques for traffic classification. In this paper, we use a distributed host based traffic collection platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then probabilistic neural network was used to traffic classification. Web and P2P traffics were studied since they are the most predominant internet traffic types. experimental results show that probabilistic neural network is an effective machine learning technique for traffic identification

The General Link Transmission Model for Dynamic Network Loading and a Comparison with the DUE Algorithm

Abstract: This chapter will present the General Link Transmission Model (GLTM) that is the extension of the link transmission model (LTM) to any concave fundamental diagram and node topology and compare it with the dynamic user equilibrium (DUE) algorithm. This assignment method has been employed successfully (1) as a simulation engine in the solution of a signal synchronization problem based on a genetic algorithm, which by its nature requires many fast runs of the black box, and (2) to extend in space and time the traffic measured by probe vehicles in a travel time estimation problem, which requires short-term predictions on large congested networks. The model proved to be flexible, reliable, and easy to calibrate, as well as efficient in the use of memory and CPU. It is recommended when route choice is not crucial or elastic, otherwise DUE is preferable since it is specifically designed for the equilibrium problem. However, in the later case it is also very useful to run the GLTM at the end of DUE based on the equilibrium splitting rates, so a s to achieve a fine-grained solution of the resulting continuous dynamic network loading (CDNL)

Seismic safety of network structures and infrastructures

Abstract: Realistic assessment of network structural safety requires modelling of a reasonably large part of the network itself. Although this statement may appear too demanding, both for modelling and computing reasons, there are clear motivations and technological possibilities to do complex network analyses. In this paper, the safety analyses of three infrastructures that have been shaken by an earthquake are described, modelled and computed: the electric power, water and road systems. For each network, results extracted from real networks, some of which have been studied in the past by the authors, are presented and discussed. No inter-network analysis is carried out, although it is recognised that this would be the most complete approach. The common parts in the procedure to model and analyse each network, via Monte Carlo simulations, are detailed at the beginning of the paper, thus showing the many common points that show up in any network analysis.

Predictive Network Anomaly Detection and Visualization

Abstract: Various approaches have been developed for quantifying and displaying network traffic information for determining network status and in detecting anomalies. Although many of these methods are effective, they rely on the collection of long-term network statistics. Here, we present an approach that uses short-term observations of network features and their respective time averaged entropies. Acute changes are localized in network feature space using adaptive Wiener filtering and auto-regressive moving average modeling. The color-enhanced datagram is designed to allow a network engineer to quickly capture and visually comprehend at a glance the statistical characteristics of a network anomaly. First, average entropy for each feature is calculated for every second of observation. Then, the resultant short-term measurement is subjected to first- and second-order time averaging statistics. These measurements are the basis of a novel approach to anomaly estimation based on the well-known Fisher linear discriminant (FLD). Average port, high port, server ports, and peered ports are some of the network features used for stochastic clustering and filtering. We empirically determine that these network features obey Gaussian-like distributions. The proposed algorithm is tested on real-time network traffic data from Ohio University's main Internet connection. Experimentation has shown that the presented FLD-based scheme is accurate in identifying anomalies in network feature space, in localizing anomalies in network traffic flow, and in helping network engineers to prevent potential hazards. Furthermore, its performance is highly effective in providing a colorized visualization chart to network analysts in the presence of bursty network traffic.

Distributed traffic control system based on model predictive control

Abstract: The paper investigates a distributed control system scheme for urban road traffic management. The control algorithm is based on model predictive control (MPC) involving Jacobi iteration algorithm to solve constrained and nonlinear programming problem. The signal controllers of traffic network constitute a network of computers. They can distribute their computation realizing an efficient traffic control without any central management. However the optimal control inputs can be also calculated by a single traffic controller if the traffic network contains few intersections. The control aim is to relieve traffic congestion, reduce travel time and improve homogenous traffic flow in urban traffic area using distributed control architecture. The MPC based control strategy can be implemented in any urban transportation network but adequate measurement system and modern traffic controllers are needed. Theory, realization possibilities and simulation of the control method are also presented. The simulation results show that the system is able to ameliorate the network efficiency and reduce travel time. The distributed MPC based traffic control strategy proves the effectiveness by realizing a dependable control operation and creating optimal flow in the network subjected to control input constraints.

Traffic Network Optimum Principle - Minimum Probability of Congestion Occurrence

Abstract: We introduce an optimum principle for a vehicular traffic network with road bottlenecks. This network breakdown minimization (BM) principle states that the network optimum is reached, when link flow rates are assigned in the network in such a way that the probability for spontaneous occurrence of traffic breakdown at one of the network bottlenecks during a given observation time reaches the minimum possible value. Based on numerical simulations with a stochastic three-phase traffic flow model, we show that in comparison to the well-known Wardrop's principles the application of the BM principle permits considerably greater network inflow rates at which no traffic breakdown occurs and, therefore, free flow remains in the whole network.

Method and system for simplified network wide traffic and/or flow monitoring in a data network

Abstract: Method and system for providing dynamic network data traffic monitoring including monitoring a data network, detecting a change in the data network, initiating a span session based on the detected change in the data network, and dynamically modifying network configuration based on the detected change in the data network is disclosed.

Mining frequent patterns from network flows for monitoring network

Abstract: Because of the varying and dynamic characteristics of network traffic, such as fast transfer, huge volume, shot-lived, inestimable and infinite, it is a serious challenge for network administrators to monitor network traffic in real time and judge whether the whole network works well. Currently, most of the existing techniques in this area are based on signature training, learning or matching, which may be too complicated to satisfy timely requirements. Other statistical methods including sampling, hashing or counting are all approximate methods and compute an incomplete set of results. Since the main objective of network monitoring is to discover and understand the active events that happen frequently and may influence or even ruin the total network. So in the paper we aim to use the technique of frequent pattern mining to find out these events. We first design a sliding window model to make sure the mining result novel and integrated; then, under the consideration of the distribution and fluidity of network flows, we develop a powerful class of algorithms that contains vertical re-mining algorithm, multi-pattern re-mining algorithm, fast multi-pattern capturing algorithm and fast multi-pattern capturing supplement algorithm to deal with a series of problems when applying frequent pattern mining algorithm in network traffic analysis. Finally, we develop a monitoring system to evaluate our algorithms on real traces collected from the campus network of Peking University. The results show that some given algorithms are effective enough and our system can definitely identify a lot of potentially very valuable information in time which greatly help network administrators to understand regular applications and detect network anomalies. So the research in this paper not only provides a new application area for frequent pattern mining, but also provides a new technique for network monitoring.

Integrated simulation framework for the process planning of ships and offshore structures

Abstract: Recently, requests for accurate process planning using simulation have been increasing in many engineering fields, including the shipbuilding industry. To date, designers of shipyards have developed in-house simulation systems or used commercial systems such as the QUEST by Dassault system when requests for the simulation of process planning have occurred. However, these methods have some limitations. First, it requires a lot of time to develop a new in-house simulation system. In addition, it is hard to reuse previously developed systems when developing a new one and it is also hard for these to satisfy the various needs of shipyards effectively. To solve these limitations, an integrated simulation framework is proposed in this study. The proposed simulation framework provides an environment for developing various simulation systems for shipbuilding process planning. It consists of a simulation kernel, basic simulation component and application-specific simulation component. The simulation kernel manages both DEVS (discrete event system specification) and DTSS (discrete time system specification) to deal with various simulation requests. The basic simulation component provides commonly used simulation models and modeling strategies, which are used to make a simulation system more efficient. The application-specific simulation component implements the dynamics analysis, collision detection and realistic three-dimensional visualization. To evaluate its efficiency and applicability, the proposed simulation framework is applied to the block erection process of ships and offshore structures. The results show that the proposed simulation framework, as compared with those of existing studies and of commercial simulation systems, can provide a consistent, integrated development environment for a simulation system.

Network Traffic Analysis and SCADA Security

Abstract: The problem of monitoring and characterizing network traffic arises in the context of a variety of network management functions. For example, consider the five functions defined in the OSI Network Management Framework [20.1], i.e., configuration management, performance management, fault management, accounting management and security management. Traffic monitoring is used in configuration management for tasks such as estimating the traffic demands between different points in the network, so that network capacity can be allocated to these demands. In performance management, traffic monitoring can be used to determine whether the measured traffic levels exceed the allocated network capacity, thus causing congestion or delays. When a fault occurs in the network, traffic monitoring is used in fault management to help locate the source of the fault, based on changes in the traffic levels through the surrounding network elements. In accounting management, traffic monitoring is needed to measure the network usage by each customer, so that costs can be charged accordingly in terms of the volume and type of traffic generated. Finally, network traffic monitoring can be used in security management to identify unusual traffic flows, which may be caused by a denial-of-service attack or other forms of misuse.

Simulation control system for an integrated live and simulation environment for an aircraft

Abstract: A method and apparatus for managing a simulation. Information about the simulation is received over a wireless communications link with a computer system in an aircraft. The information is received during running of the simulation and identifies a performance of the computer system running the simulation. The running of the simulation is controlled based on the performance of the computer system.

Traffic Simulation with Dynameq

Abstract: Dynameq is a simulation-based dynamic traffic assignment (DTA ) model. This model employs an iterative solution method to find the user-optimal assignment of time-varying origin–destination demands to paths through a road network where the path travel times – which depend on the assigned path flows – are time-varying and determined using a detailed traffic simulation model. Increasing congestion and the use of increasingly sophisticated measures to manage it – such as adaptive traffic control, reserved, reversible and tolled lanes, and time-varying congestion pricing – have created a need for models that are more detailed and realistic than static assignment models traditionally used in transportation planning. DTA models have begun to fill that need and have been successfully applied on real-world networks of significant size. This chapter provides a description of the assignment and simulation models that comprise the software, a discussion of fundamental concepts such as user-equilibrium and stability , an introduction to calibration methodology for simulation-based DTA, and a brief description of a typical project.

A New Hybrid Network Traffic Prediction Method

Abstract: How to predict the self-similar network traffic with high burstiness is a great challenge for network management. The covariation orthogonal prediction could effectively capture the burstiness in the network traffic, and the artificial neural network prediction could adapt the network traffic change by self-learning. To improve the prediction accuracy, we propose a new hybrid network traffic prediction method based on the combination of the covariation orthogonal prediction and the artificial neural network prediction. Through empirical study, the accuracy of the new prediction method can be effectively improved seen from the mean and the prediction error.

On Global Modeling of Backbone Network Traffic

Abstract: We develop a probabilistic framework for global modeling of the traffic over a computer network. The model integrates existing single-link (-flow) traffic models with the routing over the network to capture the global traffic behavior. It arises from a limit approximation of the traffic fluctuations as the time--scale and the number of users sharing the network grow. The resulting probability model is comprised of a Gaussian and/or a stable, infinite variance components. They can be succinctly described and handled by certain 'space-time' random fields. The model is validated against real data and applied to predict traffic fluctuations over unobserved links from a limited set of observed links.

A platform for realistic online vehicular network management

Abstract: This paper introduces a platform for realistic and computationally efficient online vehicular networks simulation. It permits decentralized traffic management applications simulation as nodes mobility is modifiable at runtime thanks to the integration of two state-of-the-art network and traffic simulators. The platform embeds a tool that generates vehicular traces based on traffic counting data and ensures performance through a geographical decomposition of the network. Evidence of its performance is given on a Luxembougian traffic management scenario, using real road network and traffic data.

Real-time traffic analysis through integration of road traffic prediction and traffic microsimulation models

Abstract: A system, method and computer program product for forecasting a vehicle traffic condition in a near future The system comprises a traffic prediction tool, a turning percentage prediction module and a simulation tool The traffic prediction tool estimates a traffic speed and volume in a traffic link A traffic link refers to a portion of a traffic road where the traffic prediction tool is installed The turning percentage prediction module estimates a turning percentage in the traffic link based on the estimated traffic speed and traffic volume The simulation tool computes, based on the estimated turning percentage, the estimated traffic speed and the estimated traffic volume, an expected traffic volume in the traffic link

A network traffic prediction approach based on multifractal modeling

Abstract: This work extends the notion of the widely mentioned and used fractional Brownian traffic model in the literature. Extensive experimental investigations indicate that the proposed traffic model, named extended fractional Brownian traffic, can capture not only the self-similar properties, but also the inherent multifractal characteristics of those traffic flows found in modern communication networks. Additionally, the structure of this traffic model is taken into account in a traffic prediction algorithm that benefits from the more accurate traffic modeling. The experimental results clearly point out the advantages of using the proposed model in traffic modeling as well as in traffic prediction.

Expectations of optical network traffic gain afforded by bit rate adaptive transmission

Abstract: We propose and evaluate a metric for the value proposition of bit rate adaptive transmission in the form of the quasi-static multiplicative increase in network traffic that could be supported by an optical network without increasing the number of wavelength channels or spectral bandwidth. Under the assumption of idealized bit rate adaptive transmission, we assess the increase in network traffic throughput, or traffic gain, using the network global expectation model methodology and recent results and observations for the capacity limits of optical fiber transmission and the distance-dependence of capacity deployments. Depending upon the details of the optical transmission system technology, network topology, and traffic-influenced capacity profile, we estimate ideal potential network mean traffic gains in the range of 1.5X to 4X. e 2010 Alcatel-Lucent.