A Generic Technique for Sketches to Adapt to Different Counting Ranges

摘要

Sketch is a compact data structure for network measurements. To achieve fast speed, it needs to be held in the on-chip memory (SRAM), which is very small. To enable the sketch fit into the on-chip memory, the product of counter size and number of counters must be below a certain limit. If we use small counters, e.g., 8 bits, some counters will overflow. If we use large counters, e.g., 16 bits per counter, the total number of counters will be small, each counter will be shared by more flows, leading to poor accuracy. To address this issue, we propose a generic technique: self-adaptive counters (SA Counter). When the value of the counter is small, it works as a normal counter. When the value of the counter is large, we increment it using a predefined probability, so as to represent a large value. Moreover, in SA Counter, the probability decreases when the value increases. This technique can significantly improve the accuracy of sketches. To verify the effectiveness of SA Counter, we apply SA Counter to three typical sketches, and conduct extensive experiments on one real dataset and one synthetic dataset. Experimental results show that, compared with the state-of-the-art, sketches using SA Counter improve the accuracy by up to 13.6 times.