Cost‐effective and accurate flow statistics collection in OpenFlow‐based SDN. Issue 4 (28th February 2022)

• Record Type: Journal Article
• Authors: Yan, Binghao; Liu, Qinrang; Shen, Jianliang; Liang, Dong; Liu, Xingyu
• Is Part Of: International journal of network management. Volume 32:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-28
• Subjects: complex network -- software‐defined networks -- statistics collection -- timeout algorithm; Computer networks -- Management -- Periodicals; 004.6
• DOI: 10.1002/nem.2197
• Languages: English
• ISSNs: 1055-7148

Abstract

Summary: 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. Abstract : This paper proposes a cost‐effective and accurate statistics collection method in SDN. First, we propose multi‐path weighted closeness centrality to perform importance ranking on switching nodes, which helps us select top‐k key nodes for statistical collection. Second, we propose an adaptive flow rule timeout mechanism for accurate.