SQUID: Faster Analytics via Sampled Quantiles Data-structure
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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 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 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.
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