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CEOF: Enhanced Clustering-based Entries Optimization scheme to prevent Flow table overflow

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Abstract

Software-Defined Networking is an advanced networking architecture that decouples the control and data plane for efficient and flexible network administration. The packets are forwarded based on the rules existing in the flow table that resides in the Ternary Content Addressable Memory (TCAM) and plays a key role in packet forwarding. TCAM is prominent for wire-speed processing with certain limitations such as high power consumption, expensive, and limited storage. It creates a serious challenge in terms of scalability where the limited sized flow tables are over-utilized and are easily overflowed during a high traffic rate. The flow table overflow creates blocking of new incoming flows or eviction of existing entries that are accessed by active flows. To overcome these challenges and to provide Quality of Service to the current network design, an entry reduction scheme is proposed using machine learning algorithms. It consists of two phases (1) Detection of overflow by estimating the cardinality of entries in each snapshot of the flow table which is carried out using HyperLogLog. (2) When overflow is detected, immediately the mitigation is carried out by evicting the extravagant entries using Hierarchical Agglomerative Clustering followed by entries optimization of each cluster using Pareto Optimizer. The simulation results proved that the proposed work reduces 99.99% of redundant entries and, 99.98% of increased network throughput with reduced controller overhead.

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Authors and Affiliations

  1. Ramanujan Computing Centre, College of Engineering, Anna University, Chennai, Tamilnadu, 600 025, India

    N. Priyanka & K. Murugan

  2. Society for Electronic transactions & Security (SETS), Taramani, Chennai, India

    T. R. Reshmi

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  1. N. Priyanka
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  3. K. Murugan
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Priyanka, N., Reshmi, T.R. & Murugan, K. CEOF: Enhanced Clustering-based Entries Optimization scheme to prevent Flow table overflow. Wireless Netw 28, 69–83 (2022). https://doi.org/10.1007/s11276-021-02823-8

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