Minimizing Flow Statistics Collection Cost Using Wildcard-Based Requests in SDNs

• Published in: IEEE/ACM transactions on networking 2017-12, Vol.25 (6), p.3587-3601
• Main Authors: Xu, Hongli, Yu, Zhuolong, Qian, Chen, Li, Xiang-Yang, Liu, Zichun, Huang, Liusheng
• Format: Article
• Language: English
• Subjects: Algorithm design and analysis; Algorithms; Approximation; Approximation algorithms; Bandwidth; Collection; Computer simulation; Control systems; cost; Delay; Delays; Design optimization; Flow statistics collection; Mathematical analysis; Optimization; Power capacitors; Quality of service architectures; Rounding; Statistics; Switches; Traffic control; Traffic engineering; wildcard
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2017.2748588

In a software-defined network (SDN), the control plane needs to frequently collect flow statistics measured at the data plane switches for different applications, such as traffic engineering, QoS routing, and attack detection. However, existing solutions for flow statistics collection may result in large bandwidth cost in the control channel and long processing delay on switches, which significantly interfere with the basic functions, such as packet forwarding and route update. To address this challenge, we propose a cost-optimized flow statistics collection (CO-FSC) scheme and a cost-optimized partial flow statistics collection (CO-PFSC) scheme using wildcard-based requests, and prove that both the CO-FSC and CO-PFSC problems are NP-hard. For CO-FSC, we present a rounding-based algorithm with an approximation factor f, where f is the maximum number of switches visited by each flow. For CO-PFSC, we present an approximation algorithm based on randomized rounding for collecting statistics information of a part of flows in a network. Some practical issues are discussed to enhance our algorithms, for example, the applicability of our algorithms. Moreover, we extend CO-FSC to achieve the control link cost optimization FSC problem, and also design an algorithm with an approximation factor f for this problem. We implement our designed flow statistics collection algorithms on the open virtual switch-based SDN platform. The testing and extensive simulation results show that the proposed algorithms can reduce the bandwidth overhead by over 39% and switch processing delay by over 45% compared with the existing solutions.