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.

Load shedding in network monitoring applications

Abstract: Monitoring and mining real-time network data streams is crucial for managing and operating data networks. The information that network operators desire to extract from the network traffic is of different size, granularity and accuracy depending on the measurement task (e.g., relevant data for capacity planning and intrusion detection are very different). To satisfy these different demands, a new class of monitoring systems is emerging to handle multiple arbitrary and continuous traffic queries. Such systems must cope with the effects of overload situations due to the large volumes, high data rates and bursty nature of the network traffic. In this paper, we present the design and evaluation of a system that can shed excess load in the presence of extreme traffic conditions, while maintaining the accuracy of the traffic queries within acceptable levels. The main novelty of our approach is that it is able to operate without explicit knowledge of the traffic queries. Instead, it extracts a set of features from the traffic streams to build an on-line predictionmodel of the query resource requirements. This way the monitoring system preserves a high degree of flexibility, increasing the range of applications and network scenarios where it can be used. We implemented our scheme in an existing network monitoring system and deployed it in a research ISP network. Our results show that the system predicts the resources required to run each traffic query with errors below 5%, and that it can efficiently handle extreme load situations, preventing uncontrolled packet losses, with minimum impact on the accuracy of the queries' results.

A novel algorithm for discrete-event simulation: asynchronous distributed discrete-event simulation algorithm for cyclic circuits using a dataflow network

Abstract: Distributed simulation of circuits in which the process interactions form a cyclic graph is addressed. The method described uses a dataflow network synthesized on the basis of the connectivity of the circuit components. The algorithm, called Yaddes (which stands for yet another asynchronous distributed discrete-event simulation algorithm), computes for each component a quantity time of next event, which permits the corresponding model to execute asynchronously as far ahead in simulation time as possible. The network ensures that a simulation process executing in a distributed processing environment will not deadlock. The algorithm, which also offers acceptable performance and provable correctness, is compared with the two other principal algorithms proposed to avoid deadlocks: the deadlock recovery algorithm and the exception-mode algorithm. Performance results for Yaddes are presented. >

Server-based network performance metrics generation system and method

Abstract: A method and system of generating performance metrics for network traffic being transferred in and out of an intranet. Network traffic is non-intrusively measured from a server to client perspective within periodic measurement time intervals. The network traffic is analyzed based on each connection made from a server to a client and the measurement time interval. Using the network traffic analyzed, performance metrics are generated. Accumulated performance metrics are also generated when a connection extends beyond a measurement time interval.

A novel hybrid prediction algorithm to network traffic

Abstract: Network traffic describes the characteristics and users’ behaviors of communication networks. It is a crucial input parameter of network management and network traffic engineering. This paper proposes a new prediction algorithm to network traffic in the large-scale communication network. First, we use signal analysis theory to transform network traffic from time domain to time-frequency domain. In the time-frequency domain, the network traffic signal is decomposed into the low-frequency and high-frequency components. Second, the gray model is exploited to model the low-frequency component of network traffic. The white Gaussian noise model is utilized to describe its high-frequency component. This is reasonable because the low-frequency and high-frequency components, respectively, represent the trend and fluctuation properties of network traffic, while the gray model and white Gaussian noise model can well capture the characteristics. Third, the prediction models of low-frequency and high-frequency components are built. The hybrid prediction algorithm is proposed to overcome the problem of network traffic prediction in the communication network. Finally, network traffic data from the real network is used to validate our approach. Simulation results indicate that our algorithm holds much lower prediction error than previous methods.

Stochastic Geometry and Telecommunications Networks

Abstract: Just as queueing theory revolutionized the study of circuit switched telephony in the twentieth century, stochastic geometry is gradually becoming a necessary theoretical tool for modelling and analysis of modern telecommunications systems, in which spatial arrangement is typically a crucial consideration in their performance evaluation, optimization or future development. In this survey we aim to summarize the main stochastic geometry models and tools currently used in studying modern telecommunications. We outline specifics of wired, wireless fixed and ad hoc systems and show how stochastic geometry modelling helps in their analysis and optimization. Point and line processes, Palm theory, shot-noise processes, random tessellations, Boolean models, percolation, random graphs and networks, spatial statistics and optimization: this is a far from exhaustive list of techniques used in studying contemporary telecommunications systems and which we shall briefly discuss.