On packet loss estimation for virtual private networks services

Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969) Pub Date : 2004-10-11 DOI:10.1109/ICCCN.2004.1401618

Dongli Zhang, D. Ionescu

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引用次数: 19

Abstract

When provisioning the quantitative QoS based VPN services over packet switched networks, parameters such as packet loss, delay and delay jitter, besides the required bandwidth, have to be guaranteed. While the bandwidth is easier to guarantee during the lifetime of a VPN service, maintaining a value of the loss parameter below a preset value presents serious difficulties. Thus one of the main issues to be solved is to estimate the packet loss parameter effectively and accurately. Linking the stochastic characteristics of the traffic process to the loss probability has been examined as a solution under various contexts. Based on the large deviation theory (LDT), two types of asymptotic loss estimators have been studied extensively: the large buffer asymptotic and the large number of source asymptotic. This paper explores the application of the above estimators to the core links which aggregates lots of VPN services. To reduce the computational complexity, aggregate traffic approximation estimator is employed instead on the latter of the study. Firstly, the two practical estimators are derived based on LDT and Gaussian traffic model. Then their performances are evaluated in the live MPLS VPN network. Conclusion and suggestion for future research are formulated at the end of this paper

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虚拟专用网业务丢包估计研究

在分组交换网络中提供基于定量QoS的VPN业务时，除了需要保证所需的带宽外，还需要保证丢包、时延、时延抖动等参数。在VPN业务的生命周期内，带宽是比较容易保证的，但是将损耗参数的值保持在一个预设值以下是非常困难的。因此，有效、准确地估计丢包参数是需要解决的主要问题之一。将交通过程的随机特征与损失概率联系起来，作为各种情况下的解决方案进行了研究。基于大偏差理论(LDT)，广泛研究了两类渐近损失估计量:大缓冲渐近量和大源渐近量。本文探讨了上述估计量在聚合了大量VPN业务的核心链路上的应用。为了降低计算复杂度，在后一种方法中采用了聚合流量近似估计器。首先，推导了基于LDT和高斯交通模型的两个实用估计量。然后在实际MPLS VPN网络中对其性能进行了评价。最后对本文的研究进行了总结和展望

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969)

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