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.

Analysis and experimentation of an open distributed platform for synthetic traffic generation

Abstract: This work presents an open distributed platform for traffic generation that we called distributed Internet traffic generator (D-ITG), capable of producing traffic (network transport and application layer) and of accurately replicating appropriate stochastic processes for both IDT (inter departure time) and PS (packet size) random variables. We implemented two different versions of our distributed generator. In the first one, a log server is in charge of recording the information transmitted by senders and receivers and these communications are based either on TCP or UDP. In the other one, senders and receivers make use of the MPI library. A complete performance analysis among centralized version and the two versions of D-ITG is presented. To our knowledge, no similar works are available.

Traffic prioritization techniques for wireless networks

Abstract: Various embodiments are disclosed relating to traffic prioritization techniques for wireless networks. Different priorities may be applied to uplink traffic and downlink traffic at one or more nodes or mesh points (MP1, MP2, MP3) in a wireless network, for at least some traffic. In another example embodiment, a first set of QoS parameters may be used for uplink traffic while a second set of QoS parameters may be used for downlink traffic for one or more nodes within a wireless network, for at least some traffic. According to another example embodiment, local or intra-cell traffic may be prioritized differently than inter-cell traffic for a mesh point within a wireless meshed network, for at least some traffic. For example, local or intra-cell traffic may be prioritized over inter-cell traffic for a mesh point within a wireless meshed network (100).

A distributed approach to queueing network simulation

Abstract: Discrete simulation is a widely used technique for system performance evaluation. The conventional approach to discrete simulation (e.g., GPSS, Simscript) does not attempt to exploit the parallelism typically available in queueing network models. In this paper, a distributed approach to discrete simulation is presented. It involves the decomposition of a simulation into components and the synchronization of these components by message passing. This approach can result in the speedup of the total time to complete a given simulation if a network of processors is available. The architecture of a microcomputer network suitable for distributed simulation is described and some results concerning the distributed approach are presented.

Packet-level integration of fluid TCP models in real-time network simulation

Abstract: We present a hybrid network traffic model that combines time-stepping fluid model with discrete-event packet-oriented simulation. We propose an integration scheme allowing packet flows to interact with fluid flows within each network queue. Different from previous schemes that require physical division of the virtual network between the fluid model and packet-oriented simulation, our hybrid model allows full integration of the two paradigms making it possible to dynamically change the composition of traffic flows to allow the simulation to keep up with real time. Experiments show that our model provides a good prediction of the network behavior. More important, as we increase the proportion of packet flows, the simulation is capable of capturing more detail of the network traffic behavior at the expense of more computing time. Hence the tradeoff.

Radio Frequency Traffic Classification Over WLAN

Abstract: Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types.