Efficient Differentiated Storage Architecture for Large-Scale Flow Tables in Software-Defined Wide-Area Networks

• Main Authors: Bing Xiong, Rengeng Wu, Jinyuan Zhao, Jin Wang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Software-defined WAN; openflow switches; large-scale flow tables; differentiated storage architecture; active/idle flow differentiation
• Online Access: https://ieeexplore.ieee.org/document/8843888/

As a novel network paradigm, Software Defined Networking (SDN) decouples control logic functions from data forwarding devices, and introduces a separate control plane to manipulate underlying switches via southbound interfaces like OpenFlow. This paradigm offers numerous benefits for wide area networks (WAN), like promoting application performance and reducing deployment costs, but poses serious challenges on the storage resources and lookup performance of large-scale flow tables in OpenFlow switches. This paper is thus motivated to propose an efficient differentiated storage architecture for large-scale flow tables in OpenFlow-based software-defined WAN. Firstly, we investigate into the impact of wildcards in match fields on the packet-in-batch feature within a flow based on network traffic locality. Then, packet flows are dynamically distinguished into active ones and idle ones in terms of their short-term states. Subsequently, we store the match fields of active flows and idle flows respectively in TCAM and SRAM, and the content fields of both types of flows in DRAM, to effectively relieve the insufficiency of TCAM capacity. Finally, we evaluate the performance of our proposed flow table storage architecture with real network traffic traces by experiments. The experimental results indicate that our proposed storage architecture with the active/idle flow differentiation obviously outperforms the traditional one applying the elephant/mice flow differentiation in terms of TCAM hit rates and average flow table access time.