AGFT: Adaptive entries aggregation scheme to prevent overflow in multiple flow table environment

Abstract

AbstractThe revolutionary architecture termed Software‐Defined Network provides flexible network management by detaching the control logic from the underlying data plane. The flow table resides in Ternary Content Addressable Memory imparts rules for the incoming flows with a limitation of high cost, limited storage, and consumes high power. In data center networks, when the traffic rate is high the overflow occurs due to storage limitation with high packet drop, frequent rule miss, and severe controller overhead. To overcome these challenges and to provide Quality of Service to the current network design sketch‐based entry reduction scheme is proposed. It incorporates three concurrent modules integrated to function sequentially where (1) Periodical analysis in Multiple Flow Tables is performed to ensure the availability of redundant entries using the robust data mining algorithm called Term Frequency. (2) Recurrent entries are further classified and clustered using the Boyer–Moore pattern matching algorithm to facilitate the forthcoming aggregation process. (3) A compact flow table is achieved with a customized multibit trie using Huffman coding compression technique. The experimental outcomes prove that this work prevents overflow by 99.98% with 98.99% enhanced flow table space and provides a significant reduction of controller overhead than the existing schemes.