CAJ | 학술논문

维护每个报文的流记录需要占用大量测量资源.目前已有多种抽样技术估计网络流统计信息,然而精确地估计出流数统计信息是目前的研究难点.提出了Integral和Iteration两种基于报文抽样样本估计网络流数的算法. Integral算法只需使用抽样流长为1的流数信息就可以近似推导出未抽样的流数.Iteration算法通过建立迭代函数估计未抽样流数,然后根据未抽样流数和已抽样的流数推断出原始流量的流数.采用 CERNET(China education and research network)骨干网络链路数据将这两种算法与EM(expectation maximization)算法进行对比,表明Iteration算法具有较好的精度和性能.
유호매개보문적류기록수요점용대량측량자원.목전이유다충추양기술고계망락류통계신식,연이정학지고계출류수통계신식시목전적연구난점.제출료Integral화Iteration량충기우보문추양양본고계망락류수적산법. Integral산법지수사용추양류장위1적류수신식취가이근사추도출미추양적류수.Iteration산법통과건립질대함수고계미추양류수,연후근거미추양류수화이추양적류수추단출원시류량적류수.채용 CERNET(China education and research network)골간망락련로수거장저량충산법여EM(expectation maximization)산법진행대비,표명Iteration산법구유교호적정도화성능.
Recording flow statistics for each network packet is resource-intensive. Various sampling techniques are used to estimate flow statistics. However, the estimation accuracy based on the sampling remains a significant challenge. This paper introduces both sampling techniques denoted as Integral and Iteration algorithms, which can accurately infer the number of original flows from the sampled flow records. The Integral algorithm uses only the number of sampled flows with one sampled packet to approximately d educe the number of unsampled flows. The Iteration algorithm can estimate the number of unsampled flows using an iteration method. The number of original flows can be precisely estimated according to both the number of sampled flows and unsampled flows. Both the algorithms are compared to the EM (expectation maximization) algorithm using multiple traffic traces collected from CERNET (China education and research network) backbone. The result shows that the Iteration algorithm is superior to the EM algorithm and can provide highly accurate estimation on the number of original flows.