In order to improve the precision of super point detection and control measurement resource consumption, this paper proposes a super point detection method based on sampling and data streaming algorithms （SDSD）, and...
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In order to improve the precision of super point detection and control measurement resource consumption, this paper proposes a super point detection method based on sampling and data streaming algorithms （SDSD）, and proves that only sources or destinations with a lot of flows can be sampled probabilistically using the SDSD algorithm. The SDSD algorithm uses both the IP table and the flow bloom filter （BF） data structures to maintain the IP and flow information. The IP table is used to judge whether an IP address has been recorded. If the IP exists, then all its subsequent flows will be recorded into the flow BF; otherwise, the IP flow is sampled. This paper also analyzes the accuracy and memory requirements of the SDSD algorithm , and tests them using the CERNET trace. The theoretical analysis and experimental tests demonstrate that the most relative errors of the super points estimated by the SDSD algorithm are less than 5%, whereas the results of other algorithms are about 10%. Because of the BF structure, the SDSD algorithm is also better than previous algorithms in terms of memory consumption.
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