OpenFlow网络测量方法的自适应采样

Guang Cheng, Jun-Jae Yu
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引用次数: 7

摘要

OpenFlow以用户指定的频率为每个流和聚合指标提供统计数据收集方案。然而,流量统计的周期性轮询不能很好地平衡测量精度和有限的控制通道带宽之间的权衡。为了进一步讨论资源/精度的权衡,本文扩展了OpenFlow协议,为每个监控流条目添加采样动作,并系统地对匹配的数据包进行采样以推断流级统计信息。尽管流量采样在某种程度上可能容易出错,但它将提供流量动力学的近实时测量,并确定其准确的轮询频率,这是基于轮询的方法无法实现的。在本文中,我们提出了一种按流采样方案,指导控制器自适应调整轮询频率,然后在链路利用率监测的背景下对其进行评估。
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Adaptive Sampling for OpenFlow Network Measurement Methods
OpenFlow provides a statistics collection scheme for per-flow and aggregate metrics at a user-specified frequency. However, periodic polling for flow statistics cannot well balance the tradeoff between measurement accuracy and limited control channel bandwidth. To further discuss the resource/accuracy tradeoff, this paper extends OpenFlow protocol to add sampling action for each monitoring flow entry, and systematically sample the matching packets to infer the flow-level statistics. Although traffic sampling can somehow be error-prone, it will provide near-real-time measurements of flow dynamics and determine its accurate polling frequency, which the polling-based approach cannot achieve. In this paper, we propose a per-flow sampling solution to instruct the controller to adaptively adjust polling frequency, then evaluate it in the context of link utilization monitoring.
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