NETWORK BANDWIDTH
Network Bandwidth Predictor (NBP):
A System for Online Network performance Forecasting
Alaknantha Eswaradass, Xian-He Sun, Ming Wu
Department of Computer Science
Illinois Institute of Technology
Chicago, Illinois 60616, USA
{eswaala, sun, wuming}@iit.edu
Abstract
The applicability of network-based computing depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time.
There is no satisfactory solution available for network performance predictions. In this research, we propose, design, and implement the NBP (Network Bandwidth
Predictor) for rapid network performance prediction.
NBP is a new system that employs a neural network based approach for network bandwidth forecasting.
This system is designed to integrate with most advanced technologies. It employs the NWS (Network
Weather Service) monitoring subsystem to measure the network traffic, and provides an improved, more accurate performance prediction than that of NWS, especially with applications with a network usage pattern. The NBP system has been tested on real time data collected by NWS monitoring subsystem and on trace files. Experimental results confirm that NBP has an improved prediction.
Index Terms— Performance prediction, Network bandwidth, Artificial Neural Network, Distributed computing 1. Introduction
Performance monitoring and forecasting is an active area of research. In the growing world of networking, more emphasis is being placed on speed, connectivity, and reliability. When network problems occur, they often result in catastrophic breakdowns. Due to the heterogeneity and the constantly varying nature of the network traffic, there are only a few works available to provide prediction of network performance in terms of
available bandwidth and latency in a heterogeneous environment, such as Grid computing.
Network Weather Service (NWS) [1, 2] is a wellused network performance measurement and
prediction
A System for Online Network performance Forecasting
Alaknantha Eswaradass, Xian-He Sun, Ming Wu
Department of Computer Science
Illinois Institute of Technology
Chicago, Illinois 60616, USA
{eswaala, sun, wuming}@iit.edu
Abstract
The applicability of network-based computing depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time.
There is no satisfactory solution available for network performance predictions. In this research, we propose, design, and implement the NBP (Network Bandwidth
Predictor) for rapid network performance prediction.
NBP is a new system that employs a neural network based approach for network bandwidth forecasting.
This system is designed to integrate with most advanced technologies. It employs the NWS (Network
Weather Service) monitoring subsystem to measure the network traffic, and provides an improved, more accurate performance prediction than that of NWS, especially with applications with a network usage pattern. The NBP system has been tested on real time data collected by NWS monitoring subsystem and on trace files. Experimental results confirm that NBP has an improved prediction.
Index Terms— Performance prediction, Network bandwidth, Artificial Neural Network, Distributed computing 1. Introduction
Performance monitoring and forecasting is an active area of research. In the growing world of networking, more emphasis is being placed on speed, connectivity, and reliability. When network problems occur, they often result in catastrophic breakdowns. Due to the heterogeneity and the constantly varying nature of the network traffic, there are only a few works available to provide prediction of network performance in terms of
available bandwidth and latency in a heterogeneous environment, such as Grid computing.
Network Weather Service (NWS) [1, 2] is a wellused network performance measurement and
prediction
References: Denver, Colorado, April., 2005. 1041-1055, Sep., 2002. Proc. of 2003 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2003), Nice, France, Apr.,