September 2008A Framework for Efficient Class-based Sampling

by Mohit Saxena , Ramana Rao Kompella , Mohit Saxena , Ramana Rao Kompella

Datum	Value	Source
TITLE	September 2008A Framework for Efficient Class-based Sampling	SVM HeaderParse 0.2
AUTHOR NAME	Mohit Saxena	SVM HeaderParse 0.2
AUTHOR NAME	Ramana Rao Kompella	SVM HeaderParse 0.2
AUTHOR NAME	Mohit Saxena	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; Purdue University	SVM HeaderParse 0.2
AUTHOR ADDR	West Lafayette, IN, 47907	SVM HeaderParse 0.2
AUTHOR NAME	Ramana Rao Kompella	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; Purdue University	SVM HeaderParse 0.2
AUTHOR ADDR	West Lafayette, IN, 47907	SVM HeaderParse 0.2
ABSTRACT	With an increasing requirement for network monitoring tools to classify traffic and track security threats, newer and efficient ways are needed for collecting traffic statistics and monitoring of network flows. However, traditional solutions based on random packet sampling based treat all flows as equal and therefore, do not provide the flexibility required for these applications. For example, network operators are often interested in observing as many unique flows as possible; however, random packet sampling is inherently biased towards large flows thus making it unsuitable for such application. Operators may also be interested in increasing the fidelity of flow measurements for a certain class of flows, which cannot be achieved in such frameworks. In this paper, we propose a novel architecture called CLAMP that provides an efficient	SVM HeaderParse 0.2
CITATIONS	11 found	ParsCit 1.0