Network Traffic Estimation Research Articles

Design and analysis of dynamic erbium-doped fiber amplifier gain-clamping systems with feedback control

We present an accurate and quantitative design scheme for erbium-doped fiber amplifier (EDFA) gain control systems. The proposed design scheme is demonstrated through the design of an eight-channel EDFA gain-clamping system with an all-optical self-tuning feedback loop. The effect of the time delay in the feedback loop and the selection of feedback wavelength are theoretically analyzed. Compared with existing electrical-optical EDFA gain schemes, the proposed design scheme is free from the feedback loop response speed limitation and does not need any predetermined information of the network traffic variation or operation point estimation. It can efficiently enhance the static and dynamic performance of the whole system. Furthermore, since the design can efficiently suppress the relaxation oscillation transient, it provides a cost-effective solution for the gain-clamping problem of a long EDFA chain by combining it with a fast-link control method.

Number and Location of Sensors for Real-Time Network Traffic Estimation and Prediction: Sensitivity Analysis

Installing and maintaining sensors in a transportation network can be expensive. The motivation for this research is finding the best way to deploy finite resources and generate a network detection system in a manner that produces minimal estimation errors. The analysis uses a simulation-based real-time network traffic estimation and prediction system based on dynamic traffic assignment (DTA) methodology to analyze different levels of detection and different sensor locations in a portion of the Chesapeake Highway Advisories Routing Traffic (CHART) network (between Washington, D.C., and Baltimore, Maryland). This study provides a conceptual framework of the sensor location problem and a theoretical description of the objectives associated with the sensor location problem. A sensitivity analysis of the estimation and prediction quality with the DYNASMART-X real-time DTA system in relation to sensor number and location is conducted in the Maryland CHART network. The analysis considers both randomly generated location scenarios and scenarios based on engineering judgment. The analysis reveals the importance of providing detection in specific locations of the network and the dependence of the value of additional detection on the specific location selected.

Number and Location of Sensors for Real-Time Network Traffic Estimation and Prediction

Installing and maintaining sensors in a transportation network can be expensive. The motivation for this research is finding the best way to deploy finite resources and generate a network detection system in a manner that produces minimal estimation errors. The analysis uses a simulation-based real-time network traffic estimation and prediction system based on dynamic traffic assignment (DTA) methodology to analyze different levels of detection and different sensor locations in a portion of the Chesapeake Highway Advisories Routing Traffic (CHART) network (between Washington, D.C., and Baltimore, Maryland). This study provides a conceptual framework of the sensor location problem and a theoretical description of the objectives associated with the sensor location problem. A sensitivity analysis of the estimation and prediction quality with the DYNASMART-X real-time DTA system in relation to sensor number and location is conducted in the Maryland CHART network. The analysis considers both randomly generated location scenarios and scenarios based on engineering judgment. The analysis reveals the importance of providing detection in specific locations of the network and the dependence of the value of additional detection on the specific location selected.

The impact of BGP dynamics on intra-domain traffic

Recent work in network traffic matrix estimation has focused on generating router-to-router or PoP-to-PoP (Point-of-Presence) traffic matrices within an ISP backbone from network link load data. However, these estimation techniques have not considered the impact of inter-domain routing changes in BGP (Border Gateway Protocol). BGP routing changes have the potential to introduce significant errors in estimated traffic matrices by causing traffic shifts between egress routers or PoPs within a single backbone network. We present a methodology to correlate BGP routing table changes with packet traces in order to analyze how BGP dynamics affect traffic fan-out within a large "tier-1" network. Despite an average of 133 BGP routing updates per minute, we find that BGP routing changes do not cause more than 0.03% of ingress traffic to shift between egress PoPs. This limited impact is mostly due to the relative stability of network prefixes that receive the majority of traffic -- 0.05% of BGP routing table changes affect intra-domain routes for prefixes that carry 80% of the traffic. Thus our work validates an important assumption underlying existing techniques for traffic matrix estimation in large IP networks.

Conditional expectations in network traffic estimation

We describe a fast Monte Carlo method for computing conditional expectations that arise in a fixed point equation in a problem of network traffic estimation.