Network traffic statistics implement method supporting multi-dimensional aggregation classification

Abstract

The invention discloses a network traffic statistics implement method supporting multi-dimensional aggregation classification. The network traffic statistics implement method adopts a multi-level storage manner and a data format, is flexible to use, high in expandability, and low in storage occupation space, and can meet different application requirements. An original message is stored in a database, and original traffic is stored in a binary file, so that the requirements of storage and integrity of a large amount of data are met; a traffic linked list is stored in an internal storage, so that the data accessing speed of a user is improved, and multi-granularity traffic statistical analysis is supported. As a third-order HASH aggregation traffic linked list matrix is introduced, the traffic inquire and access speed is improved, and the user is helped to find query information quickly and rapidly. The network traffic statistics implement method is a multi-dimensional traffic aggregation classification algorithm, can be used for dynamic traffic analysis according to various divided time periods of an application, and also supports business traffic aggregation analysis based on the variation of groups organized by various applications of original-target addresses, so that multi-dimensional traffic aggregation classification statistical analysis of flexible combinations of five key words is realized.

Description

technical field [0001] The invention relates to a method for realizing network flow statistics supporting multi-dimensional aggregation and classification. Background technique [0002] Network traffic statistics and analysis is the basis for mastering network behavior, and is an effective method for analyzing network conditions and mastering traffic characteristics. It can quantitatively analyze the activity rules of different network applications in the network by collecting data or message traces, which can provide Network operators provide a basis for effectively controlling and distinguishing network traffic; in addition, through statistics and analysis of the time series that constitute business traffic, the characteristics of user business behavior are obtained, and parameters that can characterize network traffic characteristics are extracted from it to realize business traffic modeling, Simulation and performance analysis and forecasting of future user business need...

AI Technical Summary

Problems solved by technology

At present, there are many one-dimensional and two-dimensional traffic aggregation classification algorithms. To meet the user's multiple traffic aggregation query needs, each possible traffic aggregation of each user must be sorted out one by one, which often leads to the cost of data sorting in the early stage. For a long time, it takes up a lot of storage space

At present, the algorithms that support high-dimensional (more than two-dimensional) aggregation and classification either require too much memory space to meet the requirements of low cost, or the speed of classification query is too low to meet the application requirements of user traffic statistics and analysis

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