English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Collecting flow records is a common practice of network operators andresearchers for monitoring, diagnosing and understanding a network. Traditionaltools like NetFlow face great challenges when both the speed and the complexityof the network traffic increase. To keep pace up, we propose HashFlow, a toolfor more efficient and accurate collection and analysis of flow records. Thecentral idea of HashFlow is to maintain accurate records for elephant flows,but summarized records for mice flows, by applying a novel collision resolutionand record promotion strategy to hash tables. The performance bound can beanalyzed with a probabilistic model, and with this strategy, HashFlow achievesa better utilization of space, and also more accurate flow records, withoutbringing extra complexity. We have implemented HashFlow, as well as severallatest flow measurement algorithms such as FlowRadar, HashPipe andElasticSketch, in a P4 software switch. Then we use traces from differentoperational networks to evaluate them. In these experiments, for various typesof traffic analysis applications, HashFlow consistently demonstrates a clearlybetter performance against its state-of-the-art competitors. For example, usinga small memory of 1 MB, HashFlow can accurately record around 55K flows, whichis often 12.5% higher than the others. For estimating the sizes of 50K flows,HashFlow achieves a relative error of around 11.6%, while the estimation errorof the best competitor is 42.9% higher. It detects 96.1% of the heavy hittersout of 250K flows with a size estimation error of 5.6%, which is 11.3% and73.7% better than the best competitor respectively. At last, we show thesemerits of HashFlow come with almost no degradation of throughput.

HashFlow For Better Flow Record Collection