Efficient identification of uncongested links for topological downscaling of Internet-like networks
نویسندگان
چکیده
In [33, 34] two methods have been presented to scale down the topology of the Internet, while preserving important performance metrics. In partcular, based on the observation that only the congested links along the path of each flow introduce sizable queueing delays and dependencies among flows two methods have been proposed that can infer the performance of the larger Internet by creating and observing a suitably scaled-down replica, consisting of the congested links only. It has been demonstrated that these techniques can be used in practice to greatly simplify and expedite performance prediction. While a main requirement for topology downscaling is that uncongested links are known in advance, the question of whether one can identify them, in an efficient and scalable way, has not been addressed yet. However, this is quite important, as it is directly related to the practicability of topological downscaling. In this paper we provide simple rules that can be used to identify uncongested links. In particular, we first identify scenarios under which one can easily deduce whether a link is uncongested by inspecting the network topology. Then, we identify scenarios in which this is not possible and show how one can efficiently use known results, based on the large deviations theory, to approximate the queue length distribution. While our main motivation in this paper is to complement the work on topology downscaling, our approach is quite general and can be used beyond this context, e.g. for traffic engineering and capacity planning.
منابع مشابه
Performance Preserving Downscaling Laws in Computer
xiii Chapter 1: Introduction 1 1.1 The growth of the Internet . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Predicting the performance of the Internet: existing techniques and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Downscaling the Internet: a new direction for performance prediction . 5 1.4 The evolution of wireless networks . . . . . . ....
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