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Large-scale OpenFlow flow table hierarchical storage architecture and accelerated searching method

A technology for accelerated search and hierarchical storage, which is applied in digital transmission systems, electrical components, transmission systems, etc., and can solve the problems of not being able to make full use of the advantages of TCAM search and support wildcard flow table scenarios, etc.

Inactive Publication Date: 2018-07-27
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it only optimizes the precise flow table storage lookup, and cannot solve the flow table scenario that supports wildcards. Moreover, it uses TCAM as the second and third level flow table lookup structure, which cannot make full use of the search advantages of TCAM, and the lookup rate is still low. can be further improved

Method used

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  • Large-scale OpenFlow flow table hierarchical storage architecture and accelerated searching method
  • Large-scale OpenFlow flow table hierarchical storage architecture and accelerated searching method
  • Large-scale OpenFlow flow table hierarchical storage architecture and accelerated searching method

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Experimental program
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Embodiment 1

[0110] like image 3 As shown, this embodiment provides a large-scale OpenFlow flow table hierarchical storage architecture, the hierarchical storage architecture is a hierarchical accelerated flow table architecture (HAFT), and the hierarchical accelerated flow table architecture includes three layers: active flow table layer, Silent flow surface layer and flow table storage layer;

[0111] The active flow surface layer uses TCAM as a storage medium for storing the flow identification field of the active flow entry, and the flow identification field is mainly matching domain information;

[0112] The silent flow surface layer uses SRAM as a storage medium for storing the flow identification field and the corresponding entry index pointer of the silent flow entry; since the SRAM does not support wildcard search, the silent flow entry stored in it needs to be processed according to the mask Classified storage; BF filter is also stored in SRAM, which can reduce the search overh...

Embodiment 2

[0116] like Figure 4-7 As shown, this embodiment provides an accelerated search method for the large-scale OpenFlow flow table hierarchical storage architecture described in Embodiment 1, and the method specifically includes the following steps:

[0117] S1, establish the layered accelerated flow table framework (HAFT) as described in embodiment 1 and store the flow table,

[0118] The storage structure of the flow table is a prerequisite for the storage of the flow table. Only when the storage structure of the flow table is determined can the flow table lookup process be determined.

[0119] S2. When the OpenFlow switch receives a certain network data packet p, it first analyzes the header field, that is, parses and extracts important fields of its header, and then calculates its flow identifier fid, and then uses the flow identifier fid to find the TCAM flow table;

[0120] If the search is successful, it will index to the corresponding entry content in the DRAM flow table...

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Abstract

The invention provides a large-scale OpenFlow flow table hierarchical storage architecture and an accelerated searching method. The hierarchical storage architecture is a HAFT (Hierarchical Accelerated Flow Tables) architecture and comprises a TCAM (Ternary Content Addressable Memory) active flow table layer, a SRAM (Static Random Access Memory) silent flow table layer and a DRAM (Dynamic Random Access Memory) flow table storage layer. The invention further discloses the accelerated searching method based on the above-mentioned hierarchical storage architecture. According to the HAFT architecture disclosed by the invention, firstly, a TCAM and SRAM combined flow table hierarchical storage system is established according to the characteristic of batch arrival of data packets in a network, so that rapid flow table searching of most of data packets is implemented; then on the basis of unevenness of correspondence from network data packet flow to flow table items and correspondence from the flow table items to masks, a mask sequence is adaptively regulated by applying an MA1 heuristic method so as to improve a mask successful detection speed of the subsequent data packets; and then thecharacteristic of frequent failure in detection of the data packets on the masks is utilized, and by the means of a BF (Bloom Filter), a mask failure detection result is predicted, so that the corresponding flow table searching and traversing process can be avoided, and searching efficiency is improved.

Description

technical field [0001] The invention relates to a flow table storage architecture and a search method, in particular to a large-scale OpenFlow flow table hierarchical storage architecture and an accelerated search method based on the hierarchical storage architecture. Background technique [0002] As a new type of network architecture, Software Defined Network (Software Defined Network, hereinafter referred to as "SDN") improves network flexibility by decoupling data and control planes. The rules are stored in the form of flow tables by the SDN switch of the data plane, so as to guide the processing of network packets. [0003] The OpenFlow protocol is one of the most widely used southbound interface protocols at present. It flattens the network protocol stack, uses the important fields of the network protocol headers of each layer as the matching field of the flow entry, and supports wildcards to realize the combination of arbitrary fields, thus Realize fine-grained and fl...

Claims

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Application Information

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IPC IPC(8): H04L12/741H04L12/745H04L12/935H04L45/74H04L45/748H04L49/111
CPCH04L45/54H04L45/74H04L45/748H04L49/3009H04L45/74591
Inventor 熊兵邬仁庚赵锦元胡念胡龙平
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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