Software Defined Network Inference with Passive/Active Evolutionary-Optimal pRobing (SNIPER)

M. Malboubi, Yanlei Gong, Xiong Wang, C. Chuah, P. Sharma
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引用次数: 8

Abstract

A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the hard constraints of network measurement resources. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications including per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring 8.8\% of per-flow path delays in Harvard network, congested paths can be detected with probability 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.
具有被动/主动进化最优探测(SNIPER)的软件定义网络推理
对网络相关特性的准确、可靠的监测是网络管理的一个关键要求。在当今的大规模网络中,由于网络测量资源的硬性约束,这是一项具有挑战性的任务。本文提出了一个新的框架,SNIPER,它利用软件定义网络(SDN)提供的灵活性来设计最佳的观察或测量矩阵,该矩阵可以使用矩阵补全(MC)技术获得最佳的可实现估计精度。为了解决设计大规模最优观测矩阵的复杂性,我们采用直接以最终估计精度为优化目标函数的进化优化算法(EOA)。我们使用来自不同网络拓扑的合成和真实网络测量痕迹来评估SNIPER的性能,并考虑两个主要应用,包括每流大小和延迟估计。我们的结果表明,SNIPER可以应用于硬资源约束下的各种网络性能测量。例如,通过测量哈佛网络中每流路径延迟的8.8%,可以以0.94的概率检测到拥塞路径。为了证明我们的框架的可行性,我们还在Mininet中实现了SNIPER的原型。
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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