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首页> 外文期刊>International Journal of Network Management >Decision Tree-Based Entries Reduction scheme using multi-match attributes to prevent flow table overflow in SDN environment
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Decision Tree-Based Entries Reduction scheme using multi-match attributes to prevent flow table overflow in SDN environment

机译：使用多匹配属性的基于决策树的条目减少方案来防止SDN环境中的流表溢出

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摘要

The software-defined networking is used extensively in data centers that provide centralized control for the widely deployed networking resources. The traffic is shaped by rules created by the controller dynamically without modifying the individual switch. The key component that stores rules which are used to process the flows is the flow table which resides in the ternary content addressable memory. The current commercial OpenFlow appliances accommodate limited entries up to 8000 due to its high cost and high power consumption. There are two issues to be considered, where (1) flow table's inability to provide rules during flow table overflow leads to dropping of incoming packets and (2) the significant amount of rule replacement occurs when the traffic in data centers increases which creates massive route requests to controller creating overhead. The proposed scheme prevents flow table overflow using the robust machine learning algorithm called decision tree (Iterative Dichotomiser 3) that allows the flow table to learn its high prioritized fine-grained entries by means of multiple matching attributes. The entries are classified, and the usual eviction process is replaced by pushing the low important entries into counting bloom filter which acts as a cache to prevent flow entry miss. The simulations were carried out using real-time network traffic datasets, and the comparisons with the various existing schemes prove that the proposed approach reduces 99.99% of the controller's overhead and the entries are minimized to 99% providing extra space for new flows.
机译：软件定义的网络广泛用于数据中心，为广泛部署的网络资源提供集中控制。流量由控制器创建的规则动态地在不修改各个交换机的情况下。存储用于处理流量的规则的关键组件是驻留在三元内容可寻址存储器中的流表。由于其高成本和高功耗，目前的商业开放器设备可容纳8000的有限条目。有两个问题需要考虑，其中（1）流表无法在流表溢出期间提供规则，导致进入数据包的丢弃，并且（2）当数据中心的流量增加时，发生大量规则替换，这增加了巨大的路线请求控制器创建开销。所提出的方案使用称为决策树（迭代二分形式器3）的强大机器学习算法来防止流表溢出，其允许流表通过多个匹配属性来学习其高优先级的细粒度条目。条目分类，并且通过将低重要条目推入计数盛开过滤器来替换通常的驱逐过程，以防止流动条目未命中。使用实时网络流量数据集进行模拟，以及与各种现有方案的比较证明了所提出的方法减少了控制器的开销的99.99％，并且该条目最小化为99％为新流程提供额外空间。

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  • 来源
    《International Journal of Network Management》 |2021年第4期|e2141.1-e2141.20|共20页
  • 作者

    Nallusamy Priyanka; Saravanen Sapna; Krishnan Murugan;

  • 作者单位

    Anna Univ Coll Engn Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India;

    Anna Univ Coll Engn Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India;

    Anna Univ Coll Engn Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India;

  • 收录信息 美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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