Abstract: In software-defined networks(SDN),statistical flow cardinality information plays a crucial role in various applications such as traffic engineering,traffic rerouting,and attack detection.Existing research primarily falls into two categories:measurement structures deployed on a single switch and collaborative measurements across multiple switches.However,neither of the two approaches achieves full network flow coverage,and collaborative measurements often employ independent measurements from each switch followed by merging,which can lead to duplicate counting.To address these issues,a flow cardinality estimation method based on distributed sketches is proposed.This method leverages the advantages of centralized control in the SDN control plane and collaboratively utilizes switches along the longest continuous common sub-path of each flow to construct a logical layer counting structure for that flow.Additionally,a mapping from the logical layer counting structure to the physical switch space for each flow is established,allowing participating switches to dynamically adjust the mapping intervals based on their own states and actual load conditions,thus achieving load balancing among all switches in the network.Taking the vHLL algorithm as an example,we implement a prototype of the distributed flow cardinality estimation method and conduct experimental evaluations using a real network traffic dataset on a four-layer Fat-Tree network topology.The experimental results demonstrate that the proposed method effectively achieves flow cardinality estimation across the entire network.In terms of accuracy,its average relative error(ARE) and average absolute error(AAE) values outperform the comparison experiments by up to 94.7% and 93.8%,respectively.Regarding load balancing,the method fully utilizes all switches in the network,achieving a normalized average packet load of 0.394,which is lower than that of the comparison methods,indicating its good performance.

Key words: Software-defined network, Flow cardinality measurement, Distributed network, Sketch, Load balancing