Abstract: 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.

Key words: Top-k,Activity,Time series,EWMA,SRST,Sketch