DAP-Sketch: An accurate and effective network measurement sketch with Deterministic Admission Policy

Abstract

Accurate measurement of network traffic is an essential part of current network management tasks. Traditional measurement methods based on counter and sketch respectively focus on the large flow detection and all flows queries, and the measurement accuracy is limited by the uncertainty in the large flow detection algorithm. Therefore, we design an asymmetric measurement architecture named DAP-Sketch. By using different data structures to measure large and small flows separately, DAP-Sketch provides the ability to query the approximate sizes of small flows while ensuring large flow detection. We propose a Deterministic Admission Policy (DAP) to dynamically distinguish the large and small flows, which effectively improves the accuracy of large flow detection and reduces the demand for storage resources. To improve the usability and universality of DAP, we put forward a d-Length DAP which applies local optimality instead of global optimality and makes our algorithm easy to implement. Furthermore, two optimization strategies with adaptive parameter adjustment are also designed in terms of the changes in memory space and traffic characteristics. Experimental results show that DAP-Sketch can outperform the best of the five typical measurement methods up to 59.3 times, decrease the requirements of network equipment resources, and achieve highly-precise and low-overhead network traffic measurement.