Accurate active large flow detection architecture and method based on small flow filtering

Abstract

The invention discloses a small flow filtering-based active large flow accurate detection architecture and method, and the architecture comprises a small flow filter which is used for filtering most small flows in a network, reducing the resource overhead, and reducing the Hash conflict rate; and the active large flow detector is used for extracting the active large flow in the network and improving the identification precision of the active large flow. According to the method, the small flow filter is constructed by using the thought of cycle updating, so that the resource overhead is reduced, and meanwhile, the filter can continuously and effectively filter the small flow. Meanwhile, an active large flow detector is constructed based on a Hopscotch Hashing algorithm, and inactive flows are screened out through an LRU strategy while Hash conflicts are reduced, so that Top-k flows in the network are accurately identified.

Description

technical field [0001] The invention relates to the field of network measurement, in particular to a structure and method for accurately detecting active large flows based on small flow filtering. Background technique [0002] With the improvement of the degree of interconnection in the world, network data traffic has shown explosive exponential growth, which greatly increases the difficulty of network management. Thus, network measurements are essential in modern network management of clouds and data centers. Administrators infer critical behavior by determining various traffic statistics, such as active traffic, and use measured traffic statistics to form the basis for management operations such as traffic engineering, performance diagnostics, and intrusion prevention. According to the technical requirements of active large flow extraction, the current large flow extraction technology is mainly the data flow method. At present, the data flow method can guarantee good mea...

Images