Robust and Reactive Traffic Engineering for Dynamic Traffic Demands

P. Casas, L. Fillatre, Sandrine Vaton
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引用次数: 13

Abstract

Traffic engineering (TE) has become a challenging mechanism for network management and resources optimization due to uncertain and difficult to predict traffic patterns. Recent works have proposed robust optimization techniques to cope with uncertain traffic, computing a stable routing configuration that is immune to demand variations within certain uncertainty set. However, using a single routing configuration for longtime periods can be highly inefficient. Even more, the presence of abnormal and malicious traffic has magnified the network operation problem, claiming for solutions which not only deal with traffic uncertainty but also allow to detect and identify faulty traffic to take the appropriate countermeasures. In this paper, we introduce the Reactive Robust Routing (RRR) for TE, an approach that combines both proactive and reactive techniques to tackle the problem. Based on expected traffic patterns, we adapt the uncertainty set and build a multi-hour yet robust routing scheme that outperforms the stable robust approach. For the case of anomalous and unexpected traffic, we propose a fast anomaly detection/isolation algorithm to detect and localize abrupt changes in traffic flows and decide routing changes. This algorithm is optimal in the sense that it minimizes the decision delay for a given mean false alarm rate and false isolation probability. We validate these proposals using real data from two different backbone networks and we show how the RRR can handle uncertain and highly dynamic traffic in an automatic fashion, simplifying network operation.
动态交通需求的鲁棒和响应交通工程
由于流量模式的不确定性和难以预测,流量工程已成为网络管理和资源优化的挑战机制。最近的研究提出了鲁棒优化技术来处理不确定的交通,计算一个稳定的路由配置,在一定的不确定性集合内不受需求变化的影响。但是,长时间使用单一路由配置可能非常低效。更重要的是,异常和恶意流量的存在放大了网络运行问题,要求解决方案不仅要处理流量的不确定性,还要能够检测和识别故障流量,以便采取适当的对策。在本文中,我们介绍了用于TE的响应鲁棒路由(RRR),这是一种结合了主动和响应技术来解决问题的方法。基于期望的交通模式,我们调整了不确定性集并构建了一个多小时且鲁棒的路由方案,该方案优于稳定鲁棒方法。对于异常和意外流量,我们提出了一种快速的异常检测/隔离算法来检测和定位交通流的突变,并决定路由的变化。在给定的平均虚警率和假隔离概率下,该算法的决策延迟最小,是最优的。我们使用来自两个不同骨干网的真实数据验证了这些建议,并展示了RRR如何以自动方式处理不确定和高度动态的流量,从而简化了网络操作。
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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