Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

Traffic configuration for evaluating networks on chips

Abstract: Network-on-chip (NoC) provides a network as a global communication platform for future SoC designs. Evaluating network architectures requires both synthetic workloads and application-oriented traffic. We present our traffic configuration methods that can be used to configure uniform and locality traffic as synthetic workloads, and to configure channel-based traffic for specific applications. We also illustrate the significance of applying these methods to configure traffic for network evaluation and system simulation. These traffic configuration methods have been integrated into our Nostrum NoC simulation environment.

Improving confidence in network simulations

Abstract: Simulation has become the tool of choice for an increasing number of networking researchers. Unfortunately, standard statistical techniques often cannot be applied when Internet-like heavy-tailed workloads are used as input. We present issues involved in using heavy-tailed distributions in network simulations, including three different methods for dealing with such distributions in simulation. We also discuss the proper use of the random number generator implemented in the ns-2 simulator and the impacts of improper usage.

Single-lane traffic simulation with multi-agent system

Abstract: Agent-based traffic simulation has emerged as an efficient tool to investigate traffic phenomenon. However, the main problem is how to reproduce realistic patterns of traffic flow at both macroscopic and microscopic levels with restricted computational resources. In this paper, we present a multi-agent framework of single-lane traffic simulation. We focus on the driver-vehicle agents, which is the most important ones in the framework. The three-layer architecture of driver-vehicle agents is proposed, the decision tree of tactical longitudinal movement is put forward, and the simplified formulas of acceleration and deceleration rate are derived. With parallel update mode of agent states, a one-dimension traffic simulation model is developed. To validate it at the macroscopic level, we reproduced the realistic flow-density and speed-density relation of traffic flow with periodic boundary condition. To validate it at the microscopic level, a platoon of 10 vehicles was simulated. Given four kinds of typical speed profiles of the leader, each follower can follow its leader safely and stably. The result shows that this model can reproduce realistic macroscopic and microscopic characteristics of single-lane traffic flow.

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.

Solving P2P Traffic Identification Problems Via Optimized Support Vector Machines

Abstract: Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portions of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. Application level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and application level classification, which merely uses transport layer information. The method uses support vector machines which have been optimized for performing large learning tasks, rendering that this method become more suitable for large network traffic. The experimental results show that this method achieved high efficiency and is suitable for real-time identification. And carefully tuning the parameters could make the method achieve high accuracy.