## 6 Conclusions and Future Work  
In response to the single link failure issue in the SDN data plane, this paper proposes the FAPR scheme, which combines the proactive and reactive schemes. Firstly, the proposed scheme uses the pro-VLAN method [19] to implement automatic path switching after link failures, and calculates multiple backup paths for the same link in advance. Secondly, when link failures happen, the controller assigns different backup paths to different types of flows through the analysis of flows, as well as the pre-calculated backup paths. As a result, FAPR  
Figure 14: Memory usage using the three schemes. Figure 13: CPU usage using the three schemes. not only achieves fast failure recovery and reduces the resource occupancy of the switches, but also promises the high availability and adaptability of backup paths to decrease the rate of flow congestion and interruption after the failure recovery process. Through simulation experiment verification and theoretical analysis, FAPR shows a high performance of failure recovery when compared with the traditional proactive and reactive schemes, which are detailed in the following three points. 1. FAPR keeps a stable and fast failure recovery speed no matter how the number of flows varies, reducing over 65% compared with the reactive scheme. 2. FAPR corresponds to the minimum interruption rate, which is reduced by 20% and 50% compared with the reactive and proactive schemes, respectively. 3. FAPR only installs essential backup flow rules in switches in advance, and keeps the CPU and memory usages almost stable. Although the FAPR scheme has unique advantages, there are unavoidable defects and unsolvable problems for future research. This scheme is not effective on every network topology. The bottleneck of the FAPR scheme is related most to many factors such as controller performance, frequency of single link failures, network scale, and the number of preset backup paths. Among them, controller performance is more important. The better the performance is, the higher is the number of limit conditions that can be supported. We can use performance-matched controllers and the multi-controller architecture in a real network environment, which will bring a greater economic burden. In addition, the judgment of flow types in this paper is realized through the transport layer protocols and port numbers, which cannot classify different flows in a fine-grained way. In the future, we intend to collect big data for different flows, and use machine learning algorithms for classification. Conceptualization, H.Q. and J.C.; methodology, H.Q. ; software, H.Q. ; validation, H.Q., J.C. and X.Q. ; formal analysis, H.Q. ; investigation, X.Z. ; resources, M.C. ; data curation, X.Z. and M.C. ; writing--original draft preparation, H.Q. ; writing--review and editing, H.Q. and J.C.; visualization, H.Q. ; supervision, J.C.; project administration, H.Q. and J.C.; funding acquisition, X.Q. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China and The Research Project of Shanghai Science and Technology Commission (Grant No. 62102241, No. 23 ZR142540). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] [PERSON] SlickFlow: Resilient source routing in Data Center Networks unlocked by OpenFlow. In Proceedings of the 38 th Annual IEEE Conference on Local Computer Networks, Sydney, NSW, Australia, 21-24 October 2013; pp. 606-613. [CrossRef]
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