New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Characterization of network-wide anomalies in traffic flows
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Profiling internet backbone traffic: behavior models and applications
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
A Longitudinal Study of P2P Traffic Classification
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
ACM SIGCOMM Computer Communication Review
Byte me: a case for byte accuracy in traffic classification
Proceedings of the 3rd annual ACM workshop on Mining network data
A Novel P2P Traffic Identification Scheme Based on Support Vector Machine Fuzzy Network
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
Graph-based P2P traffic classification at the internet backbone
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
| Hi-index | 0.00 |
The network traffic management is a key issue as many new emerging applications are flooding the network with their packets. Several time sensitive and low bandwidth applications run along with other time insensitive and bandwidth intensive applications. As a result, most of the time, channel capacity is exhausted by the bandwidth intensive applications like P2P file sharing, Bittorent etc. Hence, there is a limited space left for critical applications. As a result, the accurate identification of network applications through proper observation of associated packets is vital to the areas of network management and surveillance. The overall implication of the accurate traffic management provides space for critical application and also it helps in better management of available network resources like channel capacity etc. In this paper an approach is propose for the network traffic classification and bandwidth consumption by each protocol. It optimizes the use of the available network capacity and day to day traffic management. The usage of the bandwidth in the network is managed by taking accurate decisions for various types of applications. Also we propose the priority based bandwidth management approach for management of bandwidth in network. The approach is also able to maintain the QoS requirement of the real time connections by marking the time insensitive but bandwidth intensive traffic. These packets may be dropped for proper traffic management.