Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer networks, test , run

Key Technology of Service-Perceptive Traffic Management on Internet

Author NiuXiaoNa
Tutor GuoYunFei
School PLA Information Engineering University
Course Communication and Information System
Keywords Business flow identification Traffic management Early identification of ERBDPI Encryption business flow identification Network Processor
CLC TP393.06
Type Master's thesis
Year 2009
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The rapid development of the Internet and its bearer services traditional business flow identity management technology has brought great challenges and how to build a new generation of high- trusted network efficient perception and flexible management of a large number of P2P services and encryption class business the problems to be solved . This paper summarizes the business flow Identity Management Research , combined with the National 863 Program \difficulties, focus on the early identification of the business flow and flexible network processor-based management issues , the main results are as follows: ( ? ) the business flow early recognition algorithm ERBDPI a load - oriented feature detection to solve the business flow of accurate and efficient identification problem . The business stream only the start of a connection packet in ERBDPI load feature detection , you do not have to wait for the end of the connection , you can identify the type of business . Having the advantage of a simple and efficient . . Relative to the the packet sampling the traditional fair sampling method , ERBDPI lower than obtain higher classification accuracy , suitable for high-speed network traffic flows, real - time accurate identification . ( ? ) Proposed encrypted traffic stream early identification method based on traffic statistics features , to solve the encryption business flow , accurate and efficient identification difficult . The method utilizes mutual entropy traffic statistics used to identify characteristics of effective selection and classifier construction phase and the recognition phase feature weighting , to improve the recognition accuracy . Select the first few packets in each flow statistical characteristics of early identification , to improve the recognition efficiency . The experimental results show that 5 packets before each flow 4-7 characterized in encrypted traffic flow more than 80% recognition accuracy can be obtained . ( ? ) - Aware traffic management system based on network processor design to achieve both high performance and flexibility of business . Maintenance programs and management module identification module concurrent streams distinguish between business priorities and resource allocation and flexible scheduling mechanism . The test results show that the system combines wire-speed processing capabilities and differentiated service management flexibility , suitable as a remodeling business flow identity management components integrated into the next generation of high - trusted network equipment .

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