Application-Level Versus Network-Level Proximity
نویسندگان
چکیده
We motivate in this paper the need for application-level proximity. This proximity is a function of network characteristics that decide on the application performance. Most of existing protocols rely on the network-level proximity as for example the one based on the delay (e.g., the delay closest peer is the best peer to contact). In this paper, we study how much the two proximity definitions differ from each other. The work consists of running extensive measurements over the PlanetLab overlay network and comparing different proximity definitions. Our major observation is that the delay proximity is not always a good predictor of quality and that other network parameters are to be considered as well based on the application requirements. Particulary, the best peer to contact is not always the delay closest one. This can be explained by our other observation, that of the slight correlation of network characteristics with each other.
منابع مشابه
A combined Wavelet- Artificial Neural Network model and its application to the prediction of groundwater level fluctuations
Accurate groundwater level modeling and forecasting contribute to civil projects, land use, citys planning and water resources management. Combined Wavelet-Artificial Neural Network (WANN) model has been widely used in recent years to forecast hydrological and hydrogeological phenomena. This study investigates the sensitivity of the pre-processing to the wavelet type and decomposition level in ...
متن کاملApplication of Particle Swarm Optimization and Genetic Algorithm Techniques to Solve Bi-level Congestion Pricing Problems
The solutions used to solve bi-level congestion pricing problems are usually based on heuristic network optimization methods which may not be able to find the best solution for these type of problems. The application of meta-heuristic methods can be seen as viable alternative solutions but so far, it has not received enough attention by researchers in this field. Therefore, the objective of thi...
متن کاملLink Prediction via Ranking Metric Dual-Level Attention Network Learning
Link prediction is a challenging problem for complex network analysis, arising in many disciplines such as social networks and telecommunication networks. Currently, many existing approaches estimate the proximity of the link endpoints from the local neighborhood around them for link prediction, which suffer from the localized view of network connections. In this paper, we consider the problem ...
متن کاملAn efficient method for cloud detection based on the feature-level fusion of Landsat-8 OLI spectral bands in deep convolutional neural network
Cloud segmentation is a critical pre-processing step for any multi-spectral satellite image application. In particular, disaster-related applications e.g., flood monitoring or rapid damage mapping, which are highly time and data-critical, require methods that produce accurate cloud masks in a short time while being able to adapt to large variations in the target domain (induced by atmospheric c...
متن کاملApplication of statistical techniques and artificial neural network to estimate force from sEMG signals
This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best ...
متن کامل