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Host cardinality is one of the important attributes in the field of networkresearch. The cardinality estimation under sliding time window has become aresearch hotspot in recent years because of its high accuracy and small delay.This kind of algorithms preserve the time information of sliding time window byintroducing more powerful counters. The more counters used in these algorithms,the higher the estimation accuracy of these algorithms. However, the availablenumber of sliding counters is limited due to their large memory footprint orlong state-maintenance time. To solve this problem, a new sliding counter,asynchronous timestamp (AT), is designed in this paper which has the advantagesof less memory consumption and low state-maintenance time. AT can replacecounters in existing algorithms. On the same device, more AT can be used toachieve higher accuracy. Based on AT, this paper designs a new multi-hostscardinalities estimation algorithm VATE. VATE is also a parallel algorithm thatcan be deployed on GPU. With the parallel processing capability of GPU, VATEcan estimate cardinalities of hosts in a 40 Gb/s high-speed network in realtime at the time granularity of 1 second.

VATE: a trade-off between memory and preserving time for high accuracy cardinalities estimation under sliding time window