Measurement is a fundamental enabler of network applications such as load balancing, attack detection and mitigation, and traffic engineering. A key building block in many critical measurement tasks is \emph{q-MAX}, where we wish to find the largest $q$ values in a number stream. A standard approach of maintaining a heap of the largest $q$ values ordered results in logarithmic runtime, which is too slow for large measurements. Modern approaches attain a constant runtime by removing small items in bulk and retaining the largest $q$ items at all times. Yet, these approaches are bottlenecked by an expensive quantile calculation method. We propose SQUID, a method that redesigns q-MAX to allow the use of \emph{approximate quantiles}, which we can compute efficiently, thereby accelerating the solution and, subsequently, many measurement tasks. We demonstrate the benefit of our approach by designing a novel weighted heavy hitters data structure that is faster and more accurate than the existing alternatives. Here, we combine our previous techniques with a lazy deletion of small entries, which expiates the maintenance process and increases the accuracy. We also demonstrate the applicability of our algorithmic approach in a general algorithmic scope by implementing the LRFU cache policy with a constant update time. Furthermore, we also show the practicality of SQUID for improving real-world networked systems, by implementing a P4 prototype of SQUID for in-network caching and demonstrating how SQUID enables a wide spectrum of score-based caching policies directly on a P4 switch.
翻译:网络应用,如负载平衡、攻击探测和减缓以及交通工程等,是一个基本的促进器。许多关键测量任务的关键构件是 emph{q-MAX} 。 许多关键测量任务的关键构件是 \ emph{qq-MAX}, 我们希望在数量流中找到最大的 $q 值。 一种标准的方法是保持最大 $ 定值的堆积在对数运行时的结果, 这对于大型测量来说太慢。 现代方法通过清除小批量小项目和在任何时候保留最大 $q 的物品而达到一个稳定的运行时间。 然而, 这些方法受到昂贵的宽度计算方法的瓶颈。 我们提议SQUID, 这是一种重新设计 q-MAX 的方法, 以便使用 最大 $q 的值值值值值值值值值值值值值。 我们通过设计新的重重数据系统, 快速和准确性数据结构, 来显示我们的方法的好处。 我们把我们以前的技术与小条目的删除方法结合起来, 来显示实时显示实时维护Q 和直接更新 SLR 方法的应用范围。