Research Article
Algorithm Based on LL_CBF for Large Flows Identification
@INPROCEEDINGS{10.1007/978-3-030-77428-8_12, author={Lei Bai and Jianshe Zhou and Yaning Zhang}, title={Algorithm Based on LL_CBF for Large Flows Identification}, proceedings={Tools for Design, Implementation and Verification of Emerging Information Technologies. 15th EAI International Conference, TridentCom 2020, Virtual Event, November 13, 2020, Proceedings}, proceedings_a={TRIDENTCOM}, year={2021}, month={5}, keywords={Traffic measurement Large flow Least recent used Least elimination strategy}, doi={10.1007/978-3-030-77428-8_12} }- Lei Bai
Jianshe Zhou
Yaning Zhang
Year: 2021
Algorithm Based on LL_CBF for Large Flows Identification
TRIDENTCOM
Springer
DOI: 10.1007/978-3-030-77428-8_12
Abstract
In order to manage large-scale network, it is very important to measure and monitor the network traffic accurately. Identifying large flows timely and accurately provide data support for network management and network security, which has important meaning. Aiming at the deficiency of high false negative rate by using traditional algorithm to detect large flows, a novel scheme called LLCBF is presented, which uses the policies of “separation of large flow filtering and large flow identification” to improve the accuracy of traffic measurement. The algorithm is improved from four aspects: large flows handled firstly, using counting bloom filter to filtrate most small flows, using least recent used mechanism to filter small and medium flows and pre-protect large flows, and using least elimination strategy to identify large flows. The theoretical analysis and the simulation result indicates that compared with the standard LRU algorithm and LRUBF algorithm, our algorithm can identify the large flow in the network timely and accurately, and reduce the computing resource requirements effectively.
