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基于流类型的SDN数据平面故障恢复算法 被引量:6

Failure recovery algorithm based on flow type in SDN data plane
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摘要 软件定义网络(software defined networking,SDN)的网络拓扑中,链路故障恢复目标是保证故障恢复时延在可容忍范围内、减少数据包丢失和节约交换机存储资源。现有研究方法对链路故障恢复考虑了恢复时延、数据包丢失率、网络吞吐量等因素,没有考虑数据流对网络带宽的要求及运营商/用户的一些特殊限制。为了解决以上问题,同时满足故障恢复时延要求和运营商/用户定制化需求,提出了基于流类型的SDN数据平面故障恢复算法(failure recovery algorithm based on flow type in SDN data plane,FR-FT)。该方法根据服务质量要求将数据流分为3类,将运营商/客户的定制化需求绑定到不同数据流上,根据对应约束条件对不同类型数据流制定不同故障恢复策略。仿真结果表明,该方法可以减少交换机流表项消耗、故障恢复时延、数据包丢失率。 Software defined networking( SDN) enables the underlying infrastructure to be abstracted from the network services and controlled by one or more controllers. The existing failure recovery approaches target at ensuring that the fault recovery delay is within the tolerable range,reducing packet loss and saving the switch storage resources. Existing research methods take some factors into account,such as recovery delay,packet loss rate,network throughput,and so on,but without considering the bandwidth requirements of the data flow and some special restrictions on the operator/user. In order to solve the above problems and meet the requirements of fault recovery delay and operator/user customization,this paper proposes failure recovery algorithm based on flow type in SDN data plane( FR-FT). Firstly,the method divides the data into three categories,binds the operator/user customization requirements to the data flow,and develops different fault recovery strategies for different types of data flow based on constraints. The simulation results show that this method can reduce consumption of flow table items,the fault recovery delay and packet loss rate.
出处 《重庆邮电大学学报(自然科学版)》 CSCD 北大核心 2018年第1期134-140,共7页 Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金 国家自然科学基金(61701058 61501075 61402065) 重庆市基础与前沿研究计划项目(cstc2016jcyjA0560)~~
关键词 软件定义网络 故障恢复 数据平面 数据流 software defined networking fault recovery data plane data flow
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  • 1McKeown N. Software-defined networking [ J ]. Network, 2009,11(2) :1-2.
  • 2McKeown N, Anderson T, Balakrishnan H, et al. Open- Flow: Enabling innovation in campus networks [ J ]. ACM SIGCOMM Computer Communication Review, 2008, 38 (2) :69-74.
  • 3A1-Fares M, Loukissas A, Vahdat A. A scalable, commodi- ty data center network architecture [ J ] ACM SIGCOMM Computer Communication Review, 2008,38(4) :63-74.
  • 4Benson T, Anand A, Akella A, et al. Understanding data center traffic characteristics[ J]. ACM SIGCOMM Comput- er Communication Review, 2010,40( 1 ) :92-99.
  • 5Alizadeh M, Greenberg A, Maltz D, et al. Data center TCP (DCTCP) [ J]. ACM SIGCOMM Computer Communi- cation Review 2010,40(4) :63-74.
  • 6M-Fares M, Radhakrishnan S, Raghavan B, et al. Hed- era: Dynamic flow scheduling for data center networks [ C]//Proceedings of the 7th USENIX Symposium on Net- worked Systems Design and Implementation. 2010: 281- 296.
  • 7Hopps C E. Analysis of an equal-cost multi-path algorithm [ J ]. Journal of Allergy & Clinical Immunology, 2002,109 (1) :$265.
  • 8Tootoonchian A, Ghobadi M, Ganjali Y. OpenTM: Traffic matrix estimator for OpenFlow networks [ J ]. Lecture Notes in Computer Science, 2010,6032:201-210.
  • 9Suh J, Kwon T T, Dixon C, et al. OpenSample : A low-la-tency, sampling-based measurement platform for commodi- ty SDN[ C]//2014 IEEE 34th International Conference on Distributed Computing Systems (ICDCS). 2014:228-237.
  • 10Trestian R, Muntean G M, Katrinis K. MiceTrap: Scalable traffic engineering of datacenter mice flows using OpenFlow [ C ]// IEEE International Symposium on Integrated Net- work Management. 2013:904-907.

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