Entry Aggregation and Early Match Using Hidden Markov Model of Flow Table in SDN

The usage of multiple flow tables (MFT) has significantly extended the flexibility and applicability of software-defined networking (SDN). However, the size of MFT is usually limited due to the use of expensive ternary content addressable memory (TCAM). Moreover, the pipeline mechanism of MFT causes long flow processing time. In this paper a novel approach called Agg-ExTable is proposed to efficiently manage the MFT. Here the flow entries in MFT are periodically aggregated by applying pruning and the Quine–Mccluskey algorithm. Utilizing the memory space saved by the aggregation, a front-end ExTable is constructed, keeping popular flow entries for early match. Popular entries are decided by the Hidden Markov model based on the match frequency and match probability. Computer simulation reveals that the proposed scheme is able to save about 45% of space of MFT, and efficiently decrease the flow processing time compared to the existing schemes.

[1]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[2]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[3]  Shu Yang,et al.  An Efficiency Pipeline Processing Approach for OpenFlow Switch , 2016, 2016 IEEE 41st Conference on Local Computer Networks (LCN).

[4]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[5]  Nripendra N. Biswas,et al.  Minimization of Boolean Functions , 1971, IEEE Transactions on Computers.

[6]  Sakir Sezer,et al.  Sdn Security: A Survey , 2013, 2013 IEEE SDN for Future Networks and Services (SDN4FNS).

[7]  Brian Holdsworth,et al.  Digital Logic Design , 1981 .

[8]  Minlan Yu,et al.  NOSIX: a lightweight portability layer for the SDN OS , 2014, CCRV.

[9]  Kuochen Wang,et al.  In-switch dynamic flow aggregation in software defined networks , 2017, 2017 IEEE International Conference on Communications (ICC).

[10]  Eric Torng,et al.  TCAM Razor: A Systematic Approach Towards Minimizing Packet Classifiers in TCAMs , 2007, 2007 IEEE International Conference on Network Protocols.

[11]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[12]  Sami Souihi,et al.  Distributed SDN Control: Survey, Taxonomy, and Challenges , 2018, IEEE Communications Surveys & Tutorials.

[13]  Dan Li,et al.  The problems and solutions of network update in SDN: A survey , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  Zhigang Luo,et al.  A comprehensive security architecture for SDN , 2015, 2015 18th International Conference on Intelligence in Next Generation Networks.

[15]  M. Karnaugh.  The map method for synthesis of combinational logic circuits , 1953, Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics.

[16]  R. Rudell,et al.  Multiple-Valued Logic Minimization for PLA Synthesis , 1986 .

[17]  Lemin Li,et al.  Fast incremental flow table aggregation in SDN , 2014, 2014 23rd International Conference on Computer Communication and Networks (ICCCN).

[18]  Willard Van Orman Quine,et al.  The Problem of Simplifying Truth Functions , 1952 .

[19]  Nick Feamster,et al.  The road to SDN: an intellectual history of programmable networks , 2014, CCRV.

[20]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[21]  Berk Canberk,et al.  Spatio-Temporal Multi-Stage OpenFlow Switch Model for Software Defined Cellular Networks , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[22]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .

[23]  Vitoantonio Bevilacqua,et al.  Pseudo 2D Hidden Markov Models for Face Recognition Using Neural Network Coefficients , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[24]  Robert K. Brayton,et al.  Logic Minimization Algorithms for VLSI Synthesis , 1984, The Kluwer International Series in Engineering and Computer Science.

[25]  Pontus Sköldström,et al.  Scalable fault management for OpenFlow , 2012, 2012 IEEE International Conference on Communications (ICC).

[26]  Min Zhu,et al.  B4: experience with a globally-deployed software defined wan , 2013, SIGCOMM.

[27]  Tomoya Iizawa,et al.  Fast tracker performance using the new “variable resolution associative memory” for ATLAS , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[28]  Pavlin Radoslavov,et al.  ONOS: towards an open, distributed SDN OS , 2014, HotSDN.

[29]  Huan Liu.  Reducing routing table size using ternary-CAM , 2001, HOT 9 Interconnects. Symposium on High Performance Interconnects.

[30]  Bryan Ng,et al.  Global and local knowledge in SDN , 2015, 2015 International Telecommunication Networks and Applications Conference (ITNAC).

[31]  Ying Zhang,et al.  A mechanism for reducing flow tables in software defined network , 2015, 2015 IEEE International Conference on Communications (ICC).

[32]  Willard Van Orman Quine,et al.  A Way to Simplify Truth Functions , 1955 .