Cost-effective and accurate flow statistics collection in OpenFlow-based SDN

Abstract

Network resource scheduling and optimization require the acquisition of status information as a basis. High-cost solutions lead to more resource consumption but only bring negligible benefits. To address this challenge, this paper proposes a novel statistics collection method adapted to OpenFlow-based SDN, which can reduce the measurement cost while ensuring the statistical accuracy. First, based on the complex network theory, we propose multi-path weighted closeness centrality (MWCC) to perform importance ranking on network switching nodes, which helps us select top-k key nodes for statistical collection to reduce the overhead. Second, we propose an adaptive flow rule timeout mechanism AFRT. AFRT continuously optimizes the rule timeout values based on statistical results, further balancing flow table overhead and statistical accuracy. A series of simulation results on real network topologies verify the superiority of the proposed method in terms of communication cost, statistical accuracy, and time consumption, compared with the existing representative methods.