CAM-based Energy-Efficient OpenFlow Packet Classification in Software-Defined Networks

• Main Authors: Lin, Po-Hung, 林柏宏
• Other Authors: Gau, Rung-Hung
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/30618264136418853719

碩士 === 國立交通大學 === 電信工程研究所 === 104 === In this thesis, we proposed a SDN flow switch architecture and a packet classification method to reduce the searching power consumption and the occupation of board space. Ternary content addressable memories (TCAMs) can do parallel searching, they are faster than other memories to perform packet classification for high-speed network industry. But TCAMs have high power consumption and large board space, they also suffer from the well-known range expansion problem, if we use TCAMs to store the SDN flow tables which have more flow numbers and larger rule widths, the negative effects above become worse. To improve the shortages, we separate a single TCAM to one BCAM and one small TCAM to share the data loading, and reduce the number of entries charged per search. Besides, we found that it's un-necessary that a rule needs all fields to maintain its independent property in the table, so we put some of fields into SRAM words to reduce the loading of CAMs. When the SDN controllers get sufficient statistics, the classification method can decides rules' position of the CAMs based on the access rate of rules, these rules may get higher priority in the CAMs. Furthermore, we also show the simulation and numerical results, and compare with single TCAMs. The results show that the proposed method can efficiently improve power and space performance.