Adaptive and Low-Cost Traffic Engineering: A Traffic Matrix Clustering Perspective

Yi Liu;Nan Geng;Mingwei Xu;Yuan Yang;Enhuan Dong;Chenyi Liu;Qiaoyin Gan;Qing Li;Jianping Wu
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Abstract

Traffic engineering (TE) has attracted extensive attention over the years. Operators expect to design a TE scheme that accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routing solutions based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer from performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing solution for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing solution online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces little overhead of routing updates.
自适应低成本交通工程:流量矩阵聚类视角
近年来,交通工程受到了广泛的关注。运营商希望设计一种能够很好地适应流量动态的TE方案,并以较小的开销实现良好的TE性能。不经意路由等方法基于较大的流量矩阵(TM)范围计算最优静态路由,这通常会导致很大的性能损失。许多方法基于从观察到的历史tm中获得的一个或几个代表性tm来计算路由解决方案。然而,对于意外的tm,它们可能会受到性能下降的影响,并且通常会导致系统操作的大量开销。本文提出了一种基于TM分类的自适应低成本TE方案。我们开发了一种新的聚类算法,将一组历史TM正确地划分为几个簇,并为每个簇计算候选路由解。训练机器学习分类器根据从一些容易测量的统计数据中提取的特征在线推断合适的候选路由解决方案。我们实现了一个ALTE的系统原型,并使用真实和合成的流量轨迹进行了广泛的模拟和实验。结果表明,ALTE在处理动态流量时达到了近乎最优的性能,并且引入了很少的路由更新开销。
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