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FISTE: A Black Box Approach for End-to-End QoS Management

机译:FISTE:端到端QoS管理的黑匣子方法

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The goal of traffic engineering is to achieve a target Quality of Service (QoS) while maximizing network utilization. While determining the QoS for end-to-end paths in a network under self-similar traffic models is difficult, end-to-end network performance analysis is still essential in providing QoS to networks such as Virtual Private Networks (VPN) and Peer-to-Peer (P2P) networks. The Fast Importance Sampling based Traffic Engineering (FISTE) approach proposed in this article is a prediction-based approach that maps the ingress traffic levels of a network to the QoS of end-to-end path(s) in the network. Because FISTE is a hybrid of simulation analysis and closed-form analysis, it can treat a complex network as a black box. When we combined Simulated Annealing (SA) with FISTE, the resulting approach can provide a traffic engineering solution so that multiple end-to-end QoS requirements are satisfied while the network resource utilization is maximized. FISTE originated from the concept of Importance Sampling (IS), and our approach differs from the previous Importance Sampling based approaches since this is the first time that IS is applied to multi-queue systems under Fractional Gaussian Noise (FGN) input and traffic engineering.
机译:流量工程的目标是在最大化网络利用率的同时达到目标服务质量(QoS)。虽然很难确定自相似流量模型下网络中端到端路径的QoS,但是端到端网络性能分析对于为虚拟专用网(VPN)和对等网络等网络提供QoS仍然至关重要对等(P2P)网络。本文中提出的基于快速重要性采样的流量工程(FISTE)方法是一种基于预测的方法,可将网络的入口流量级别映射到网络中端到端路径的QoS。由于FISTE是模拟分析和闭式分析的混合体,因此可以将复杂的网络视为黑匣子。当我们将模拟退火(SA)与FISTE结合使用时,所产生的方法可以提供一种流量工程解决方案,从而在最大化网络资源利用率的同时满足多个端到端QoS要求。 FISTE起源于重要性采样(IS)的概念,我们的方法与以前的基于重要性采样的方法不同,因为这是分数分数高斯噪声(FGN)输入和流量工程下将IS首次应用于多队列系统。

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