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.

Method and apparatus for managing wireless communication based on network traffic level

Abstract: The present invention provides a method for managing communications of a wireless device in a wireless communication system based on network traffic level, and a wireless device comprising a control system configured to evaluate a network traffic level. The wireless device may be configured to transmit data, such as delay-tolerant data, based upon the network traffic level, for example transmitting data only under low traffic conditions. Deferring delay-tolerant communications may result in lower peak traffic conditions in the network. Evaluating the network traffic level may comprise measuring chip energy over total energy (Ec/Io), monitoring data on shared control channels such as HS-SCCH of a HSDPA system, inferring power allocated to channels by a dynamic power allocation scheme, or other methods.

Winner-take-all discrete recurrent neural networks

Abstract: This paper proposes a discrete recurrent neural network has simple organizations and clear dynamic behaviors. The dynamic properties of the proposed winner-take-all networks are studied detail. Simulation results are given to show network performance. Since the network model is formulated as discrete time systems, it has advantages for computer simulations over digital simulations of a continuous time neural network model. Thus they can be easily implemented in digital hardware.

Multi-agent simulation using discrete event and soft-computing methodologies

Abstract: With the emerging applications of multi-agent systems, there is always a need for simulation to verify the results before actual implementation. Multi-agent simulation provides a test bed for several soft-computing algorithms like fuzzy logic, learning automata, evolutionary algorithms, etc. In this paper we discuss the fusion of these soft-computing methodologies and existing tools for discrete event simulation (DEVS) for multi-agent simulation. We propose a methodology for combining the agent-based architecture, discrete event system and soft-computing methods in the simulation of multi-agent robotics and network security system. We also define a framework called Virtual Laboratory (V-Lab/spl reg/) for multi-agent simulation using intelligent tools.

Path Optimization in Stream-Based Overlay Networks

Abstract: The emergence of sensor networks and distributed applications that generate data streams has created a need for Internet overlays designed for streaming data. Such stream-based overlay network (SBONs) consist of a set of Internet hosts that collect, process, and deliver stream-based data to multiple applications. A key challenge in the design and implementation of SBONs is efficient path optimization when mapping logical query streams to physical network hosts and paths. Suboptimal placements can induce poor utilization of network resources, leading to severe performance penalties, link saturation, and network hotspots. Our goal is to realize efficient stream placement that takes the physical topology of the Internet into account, thereby minimizing overall network utilization. In this paper, we describe a novel, network-aware path optimization algorithm for stream-based overlay networks. Our approach is based on a spring relaxation model that operates in a metric space defined by the pairwise node latency within the SBON. This relaxation placement algorithm utilizes the underlying network resources efficiently, is capable of performing inter-stream optimization, and can be implemented in a scalable and decentralized way. To evaluate its performance, we define a set of evaluation metrics for path optimization in terms of network utilization, application delay, and resource contention. Our simulation experiments with a realistic network topology show that relaxation placement approaches optimal network utilization without introducing an undue delay penalty or resource contention. Compared to an optimal placement strategy, relaxation placement causes only 15% more network traffic on average, while adding a 24% delay penalty for the application. We validate our simulation results with actual measurements involving 70 PlanetLab nodes.