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 LL_CBF 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 LRU_BF algorithm, our algorithm can identify the large flow in the network timely and accurately, and reduce the computing resource requirements effectively.
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Acknowledgment
We would like to express our appreciation for the assistance with data collection we acquire from CAIDA. This research was financially supported by “Research on key technologies and model verification of prose genre oriented text understanding (ZDI135-101)”, “Research and Application of Key Technologies of Intelligent Auxiliary Reading System (ZDI135-79)”, Capacity Building for Sci-Tech Innovation-Fundamental Scientific Research Foundation (20530290082).
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Bai, L., Zhou, J., Zhang, Y. (2021). Algorithm Based on LL_CBF for Large Flows Identification. In: Weng, Y., Yin, Y., Kuang, L., Zhang, Z. (eds) Tools for Design, Implementation and Verification of Emerging Information Technologies. TridentCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 380. Springer, Cham. https://doi.org/10.1007/978-3-030-77428-8_12
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DOI: https://doi.org/10.1007/978-3-030-77428-8_12
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