Management of Flow Table of SDN for Proactive Eviction Using Fuzzy Logic

With the development of Software Defined Network (SDN), numerous researches have been conducted for improving the performance of SDN. In SDN flow table is used in OpenFlow switch for the routing of the packets. Due to the space limitation of flow table and switch capacity, various issues need to be resolved for effectively dealing with a large number of flows. The existing schemes typically employ a reactive approach such that evicted entries are decided only when timeout or table miss occurs. In this paper a novel proactive eviction scheme is proposed which employs hidden Markov model (HMM) to predict the probability of table miss of the entries. If the probability exceeds the preset threshold, fuzzy logic is used to select the entries for eviction considering match priority, idle time, and the number of unmatched flows via a new notion called eviction index. The proposed scheme is for efficient flow entry eviction before table miss actually occurs, which eventually increases the speed of flow management in SDN switch. Computer simulation reveals that the proposed scheme increases the match probability and prediction accuracy, and reduces the number of misses at least 10% compared to three existing entry eviction schemes.

[1]  Martín Casado,et al.  NOX: towards an operating system for networks , 2008, CCRV.

[2]  H. Jonathan Chao,et al.  Dynamic flow scheduling for Power-efficient Data Center Networks , 2016, 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS).

[3]  Yonggang Wen,et al.  “ A Survey of Software Defined Networking , 2020 .

[4]  Sheng Wang,et al.  TimeoutX: An Adaptive Flow Table Management Method in Software Defined Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[5]  H. Jonathan Chao,et al.  Balancing flow table occupancy and link utilization in software-defined networks , 2018, Future Gener. Comput. Syst..

[6]  Seung-Ik Lee,et al.  Flow table management scheme applying an LRU caching algorithm , 2014, 2014 International Conference on Information and Communication Technology Convergence (ICTC).

[7]  Azeem Iqbal,et al.  A stochastic model for transit latency in OpenFlow SDNs , 2017, Comput. Networks.

[8]  H. Jonathan Chao,et al.  JumpFlow: Reducing flow table usage in software-defined networks , 2015, Comput. Networks.

[9]  Subhasis Banerjee,et al.  FlowMaster: Early Eviction of Dead Flow on SDN Switches , 2014, ICDCN.

[10]  S. Iniyan,et al.  Applications of fuzzy logic in renewable energy systems – A review , 2015 .

[11]  Benxiong Huang,et al.  Bandwidth-aware energy efficient flow scheduling with SDN in data center networks , 2017, Future Gener. Comput. Syst..

[12]  Hyunseung Choo,et al.  Intelligent eviction strategy for efficient flow table management in OpenFlow Switches , 2016, 2016 IEEE NetSoft Conference and Workshops (NetSoft).

[13]  Ian F. Akyildiz,et al.  A roadmap for traffic engineering in SDN-OpenFlow networks , 2014, Comput. Networks.

[14]  Vijay Mann,et al.  Effective switch memory management in OpenFlow networks , 2014, DEBS '14.

[15]  Oscar Castillo,et al.  A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation , 2014, Expert Syst. Appl..

[16]  Namgi Kim,et al.  An efficient flow table replacement algorithm for SDNs with heterogeneous switches , 2015, 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE).

[17]  Sujata Banerjee,et al.  DevoFlow: scaling flow management for high-performance networks , 2011, SIGCOMM.

[18]  Hee Yong Youn,et al.  Proactive eviction of flow entry for SDN based on hidden Markov model , 2020, Frontiers of Computer Science.

[19]  Kouji Hirata,et al.  Routing method with flow entry aggregation for software-defined networking , 2017, 2017 International Conference on Information Networking (ICOIN).

[20]  Prasant Mohapatra,et al.  Simultaneously Reducing Latency and Power Consumption in OpenFlow Switches , 2014, IEEE/ACM Transactions on Networking.

[21]  Pang-Wei Tsai,et al.  A Flow-based Method to Measure Traffic Statistics in Software Defined Network , 2014 .

[22]  H. Jonathan Chao,et al.  STAR: Preventing flow-table overflow in software-defined networks , 2017, Comput. Networks.

[23]  Arjan Durresi,et al.  Quality of Service (QoS) in Software Defined Networking (SDN): A survey , 2017, J. Netw. Comput. Appl..

[24]  Thierry Turletti,et al.  A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks , 2014, IEEE Communications Surveys & Tutorials.

[25]  Jesús Ariel Carrasco-Ochoa,et al.  Assessment and prediction of air quality using fuzzy logic and autoregressive models , 2012 .

[26]  Sheng Wang,et al.  AHTM: Achieving efficient flow table utilization in Software Defined Networks , 2014, 2014 IEEE Global Communications Conference.

[27]  Sungyong Park,et al.  A Dynamic Timeout Control Algorithm in Software Defined Networks , 2014 .

[28]  Byrav Ramamurthy,et al.  Network Innovation using OpenFlow: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[29]  Nick Feamster,et al.  Improving network management with software defined networking , 2013, IEEE Commun. Mag..

[30]  Jia Wang,et al.  Scalable flow-based networking with DIFANE , 2010, SIGCOMM '10.

[31]  Biao Han,et al.  Efficient mismatched packet buffer management with packet order-preserving for OpenFlow networks , 2016, Comput. Networks.

[32]  Svein J. Knapskog,et al.  DIPS: A Framework for Distributed Intrusion Prediction and Prevention Using Hidden Markov Models and Online Fuzzy Risk Assessment , 2007, Third International Symposium on Information Assurance and Security.

[33]  Paothai Vonglao,et al.  Application of fuzzy logic to improve the Likert scale to measure latent variables , 2017 .