文献类型：期刊文献

中文题名：ACCF:时间预测机制驱动的top-k流测量

英文题名：ACCF:Time Prediction Mechanism-driven Top-k Flow Measurement

作者：胡永庆[1];杨含[1];刘子源[1];秦广军[1];戴庆龙[1]

第一作者：胡永庆

机构：[1]北京联合大学智慧城市学院,北京100101

第一机构：北京联合大学智慧城市学院

年份：2025

卷号：52

期号：10

起止页码：98-105

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：;北大核心:【北大核心2023】；

基金：面向海量事件流的流式计算框架的研究(CCFIS2019-01-01);智慧交通中的光与无线融合接入网组网技术研究(KM202111417010);拓扑结构引导的深度学习优化技术研究(ZK10202403)。

语种：中文

中文关键词：top-k;活跃度;时间序列;EWMA;SRST;Sketch

外文关键词：Top-k;Activity;Time series;EWMA;SRST;Sketch

摘要：针对当前top-k流测量过滤算法依赖固定计数器阈值的问题,提出了基于活跃度预测机制的ACCF(Activity Counting Cuckoo Filter)测量结构。ACCF通过引入活跃度预测机制,利用时间序列分析和指数加权移动平均(Exponentially Weighted Moving Average,EWMA)机制,动态计算网络流的活跃度,实现对潜在的top-k流的实时识别与提前过滤。针对哈希冲突可能导致的精度损失,ACCF引入了自刷新存储表(Self-Refreshing Storage Table,SRST),用于存储踢出路径上的网络流信息。当踢出操作达到设定的MaxNumKicks值时,SRST会在局部范围内优先踢出活跃度最小的网络流项,避免重要流量信息丢失。实验结果证明,ACCF与SRST在合适的参数组合条件下,可以提前过滤65%左右的大流并减少41%左右的插入操作,并显著提升了在top-k流量测量中的精度,尤其是在与传统的Space Saving(SS),CM Sketch,LUSketch和Cuckoo Counter算法对比时,展现了明显的优势。
In addressing the problem that current top-k flow measurement filtering algorithms depend on fixed counter thresholds,a measurement structure named ACCF based on the activity prediction mechanism has been put forward.ACCF incorporates the activity prediction mechanism,utilizing time series analysis and the EWMA mechanism,to dynamically compute the activity of network flows and accomplish real-time identification and early filtering of potential top-k flows.With respect to the accuracy loss that may be induced by hash conflicts,ACCF introduces a SRST for storing the network flow information on the evicted paths.When the eviction operation reaches the predefined MaxNumKicks value,SRST will give priority to evicting the network flow item with the lowest activity within the local scope to avoid the loss of crucial traffic information.Experimental results indicate that,under suitable parameter combinations,ACCF and SRST can filter out approximately 65%of the major flows in advance and reduce insertion operations by approximately 41%,significantly improving the accuracy in top-k traffic measurement,especially when compared with traditional algorithms such as Space Saving(SS),CM Sketch,LUSketch,and Cuckoo Counter,thereby de-monstrating distinct advantages.