SCREAM: sketch resource allocation for software-defined measurement

M. Moshref, Minlan Yu, R. Govindan, Amin Vahdat
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引用次数: 100

Abstract

Software-defined networks can enable a variety of concurrent, dynamically instantiated, measurement tasks, that provide fine-grain visibility into network traffic. Recently, there have been many proposals for using sketches for network measurement. However, sketches in hardware switches use constrained resources such as SRAM memory, and the accuracy of measurement tasks is a function of the resources devoted to them on each switch. This paper presents SCREAM, a system for allocating resources to sketch-based measurement tasks that ensures a user-specified minimum accuracy. SCREAM estimates the instantaneous accuracy of tasks so as to dynamically adapt the allocated resources for each task. Thus, by finding the right amount of resources for each task on each switch and correctly merging sketches at the controller, SCREAM can multiplex resources among network-wide measurement tasks. Simulations with three measurement tasks (heavy hitter, hierarchical heavy hitter, and super source/destination detection) show that SCREAM can support more measurement tasks with higher accuracy than existing approaches.
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尖叫:软件定义度量的草图资源分配
软件定义的网络可以支持各种并发的、动态实例化的度量任务,这些任务提供对网络流量的细粒度可见性。近年来,人们提出了许多利用草图进行网络测量的建议。然而,硬件交换机中的草图使用受限的资源,如SRAM存储器,并且测量任务的准确性是每个交换机上用于它们的资源的函数。本文提出了尖叫,一个分配资源到基于草图的测量任务的系统,以确保用户指定的最小精度。尖叫估计任务的瞬时精度,从而动态调整分配给每个任务的资源。因此,通过在每个交换机上为每个任务找到适当数量的资源,并在控制器上正确合并草图,SCREAM可以在全网范围的测量任务之间实现资源复用。对三种测量任务(重磅、分层重磅和超级源/目标检测)的仿真表明,与现有方法相比,SCREAM可以支持更多的测量任务,并且精度更高。
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