Matching model of flow table for networked big data
Yiheng Su, Ting Peng, Xiaoxun Zhong, and Lianming Zhang
TL;DR
This paper introduces F-OpenFlow, a classification-based flow table matching model for SDN switches, which enhances flow table utilization and matching efficiency in networked big data environments.
Contribution
It proposes a novel classification approach for flow table matching in SDN switches, improving efficiency and utilization for big data networking.
Findings
Improved flow table utilization rate
Enhanced matching efficiency in SDN switches
Effective handling of networked big data traffic
Abstract
Networking for big data has to be intelligent because it will adjust data transmission requirements adaptively during data splitting and merging. Software-defined networking (SDN) provides a workable and practical paradigm for designing more efficient and flexible networks. Matching strategy in the flow table of SDN switches is most crucial. In this paper, we use a classification approach to analyze the structure of packets based on the tuple-space lookup mechanism, and propose a matching model of the flow table in SDN switches by classifying packets based on a set of fields, which is called an F-OpenFlow. The experiment results show that the proposed F-OpenFlow effectively improves the utilization rate and matching efficiency of the flow table in SDN switches for networked big data.
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