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.

Large-scale dynamic transportation network simulation: A space-time-event parallel computing approach

Abstract: This paper describes a computationally efficient parallel-computing framework for mesoscopic transportation simulation on large-scale networks. By introducing an overall data structure for mesoscopic dynamic transportation simulation, we discuss a set of implementation issues for enabling flexible parallel computing on a multi-core shared memory architecture. First, we embed an event-based simulation logic to implement a simplified kinematic wave model and reduce simulation overhead. Second, we present a space-time-event computing framework to decompose simulation steps to reduce communication overhead in parallel execution and an OpenMP-based space-time-processor implementation method that is used to automate task partition tasks. According to the spatial and temporal attributes, various types of simulation events are mapped to independent logical processes that can concurrently execute their procedures while maintaining good load balance. We propose a synchronous space-parallel simulation strategy to dynamically assign the logical processes to different threads. The proposed method is then applied to simulate large-scale, real-world networks to examine the computational efficiency under different numbers of CPU threads. Numerical experiments demonstrate that the implemented parallel computing algorithm can significantly improve the computational efficiency and it can reach up to a speedup of 10 on a workstation with 32 computing threads.

On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

Abstract: Modern networks-on-chip (NoC) systems are required to handle complex run-time traffic patterns and unprecedented applications. Data traffics of these applications are difficult to be fully comprehended at design-time so as to optimize the network design. However, it has been discovered that the majority data flows in a network are dominated by less than 10% of the specific pathways. In this paper, we introduce a method that is capable of identifying critical pathways in a network at run-time and, then, can dynamically reconfigure the network to optimize for the network performance subjected to the identified dominated flows. An online learning and analysis scheme is employed to quickly discover the emerged dominated traffic flows and provides a statistical traffic prediction using regression analysis. The architecture of a self-tuning network is also discussed which can be reconfigured by setting up the identified point-to-point paths for the dominance data flows in large traffic volumes. The merits of this new approach are experimentally demonstrated using comprehensive NoC simulators. Compared to the conventional network architectures over a range of realistic applications, the proposed self-tuning network approach can effectively reduce the latency and power consumption by as much as 25% and 24%, respectively. We also evaluated the configuration time and additional hardware cost. This new approach demonstrates the capability of an adaptive NoC to handle more complex and dynamic applications.

Comprehensive topology and traffic model of a nationwide telecommunication network

Abstract: As a basis for meaningful simulation and optimization efforts with regard to traffic engineering or energy consumption in telecommunication networks, suitable models are indispensable. This concerns not only realistic network topologies but also models for the geographical distribution and the temporal dynamics of traffic, as well as the assumptions on network components and technology. This paper derives such a model from the practice of a large national carrier. Applying the network and traffic model, we demonstrate its use by presenting various optimization cases related to energy-efficient telecommunication. Here, we focus on load adaptivity by employing sleep modes to the network hardware, where several constraints on the reconfigurability of the network over time are considered.

On the burstiness of the TCP congestion-control mechanism in a distributed computing system

Abstract: Several studies in network traffic characterization have concluded that network traffic is self-similar and therefore not readily amenable to statistical multiplexing in a distributed computing system. This paper examines the effects of the TCP protocol stack on network traffic via an experimental study on the different implementations of TCP. We show that even when aggregate application traffic smooths out as more applications' traffic are multiplexed, TCP introduces burstiness into the aggregate traffic load, reducing network performance when statistical multiplexing is used within the network gateways.